Google search in exchange rate models: Hype or hope?

Herzog, Bodo; dos Santos, Lana Article Google search in exchange rate models: Hype or hope? Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Herzog, Bodo; dos Santos, Lana (2021) : Google search in exchange rate models: Hype or hope?, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 14, Iss. 11, pp. 1-40, https://doi.org/10.3390/jrfm14110512 This Version is available at: https://hdl.handle.net/10419/258615 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Risk and Financial Management Article Google Search in Exchange Rate Models: Hype or Hope? Bodo Herzog 1,2,3,* and Lana dos Santos 1   Citation: Herzog, Bodo, and Lana dos Santos. 2021. Google Search in Exchange Rate Models: Hype or Hope? Journal of Risk and Financial Management 14: 512. https:// doi.org/10.3390/jrfm14110512 Academic Editor: Stefan Bojnec Received: 12 September 2021 Accepted: 19 October 2021 Published: 25 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Economics Department, ESB Business School, Reutlingen University, 72762 Reutlingen, Germany; [email protected] 2Reutlingen Research Institute (RRI), Reutlingen University, 72762 Reutlingen, Germany 3Institute of Finance and Economics (IFE), Reutlingen University, 72762 Reutlingen, Germany *Correspondence: [email protected] Abstract: This paper studies the power of online search intensity metrics, measured by Google, for examining and forecasting exchange rates. We use panel data consisting of quarterly time series from 2004 to 2018 and ten international countries with the highest currency trading volume. Newly, we include various Google search intensity metrics to our panel data. We find that online search improves the overall econometric models and fits. First, four out of ten search variables are robustly significant at one percent and enhance the macroeconomic exchange rate models. Second, country regressions corroborate the panel results, yet the predictive power of search intensity with regard to exchange rates vary by country. Third, we find higher prediction performance for our exchange rate models with search intensity, particularly in regard to the direction of the exchange rate. Overall, our approach reveals a value-added of search intensity in exchange rate models. Keywords: exchange rate; Google search; big data; AI; information inattention JEL Classification: D82; D87 1. Introduction Google processes over three billion queries per day and retains over sixty percent share in the web search market (Desjardins 2018). In 2004, the company launched Google Trends, a free internet facility where the search volume of keywords can be accessed by the public (Helft 2009). This new big data analytics tool is free, user-friendly, and offers data in nearly real time. As official records are published belatedly, online search data appears to be an attractive and timely source of information and opens the gate to new opportunities in empirical research. The high and growing number of online users indicates that search intensity does reflect individual intentions and expectations (Ginsberg et al. 2009). Recent research papers prove the application of search data to be incrementally broad. For instance, Askitas and Zimmermann (2009) use Google data to forecast unemployment rates. Choi and Varian (2012) use web data to improve the forecast of automobile sales and Vaughan and Chen (2015) use it to identify business cycle turning points. Different in scope and methodology but related to our research is the work by Bulut (2017) and Chai et al. (2018). In this research, we generalize the exchange rate model by Dornbusch (1976b) with online search intensity metrics and study the predictive power of online data and exchange rates. We utilize search intensity in order to exhibit the expectation channel of exchange rate movements. Our theoretical approach is based on the well-known (augmented-)overshooting model in the exchange rate literature, which relies on different macroeconomic fundamentals among the most relevant interest rates and inflation (Dornbusch 1976b) . Subsequently, we extend the macroeconomic benchmark model by new online-search intensity metrics and examine the role of online data on exchange rate dynamics. Search intensity variables J. Risk Financial Manag. 2021,14, 512. https://doi.org/10.3390/jrfm14110512 https://www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2021,14, 512 2 of 40 are generated by specific keywords which bear a certain relation to the macroeconomic variables and exchange rates. Thereafter, we evaluate the performance of the different models in regard to the prediction and forecasting performance. The panel regression consists of ten countries and quarterly data from 2004 to 2018. The main research question is whether search intensity improves the models’ performance. The results reveal that online search intensity variables increase the model’s significance by lowering their standard errors. A single online search variable does add little, except for the following keywords: (a) Inflation, (b) GDP, (c) CPI, (d) job openings, and (e) (currency) exchange rate. Those search intensity variables are significant at 0.1 percent. Noteworthy, the keyword ’interest rate’ is highly significant when estimating the models with raw data only. The country specific regressions reveal that merely the keyword ‘currency+exchange rate’ denotes robustness across all countries. Looking to the macroeconomic variables, we find strong significance for inflation, interest rate, money supply, relative price of non-tradable goods, debt-to-GDP and terms of trade. This is similar in the country specific regression exercise. The value-added of including online-search keywords is revealed by the outperforming nature of the search intensity-models in general. Nonetheless, conducting research with search data is bound by the quality of the keywords (Lazer et al. 2014). For instance, there could be more searches for the name ‘Apple’ because the word in themselves stands for the fruit and the name of the company. Overall, there are three major contributions of this research work. First, we demonstrate that search intensity enhances exchange rate models in general, including the prediction and forecasting performance (Tables 6 and 7). Second, broadly defined search indices do not provide a value-added because keyword aggregation eliminates the singularity of the information and narrows the variance (Tables 6 and 7). Third, integrating search intensity in macroeconomic models positively affects the direction of in-sample forecasting (Table 8). On a side note, we corroborate empirical findings in the literature when focusing on the macroeconomic variables alone. This paper unveils novel insights into the challenging realm of exchange rate economics. Understanding exchange rates is a critical issue in understanding the interplay of the financial and real economy. Hence, this subject is of paramount relevance not only for central banks and governments, but also for businesses and financial investors. This paper is structured as follows. In Section 2, we review the literature on exchange rates as well as on online data. The data and methodology are prescribed in Section 3. In a series of subsections, we explain the design of different search indices, the different regression models as well as the findings of pre-testing in regard to our panel data. Section 4 contains the main findings. We discuss the results and analyse the policy implications, including limitations. Finally, Section 5concludes the paper. 2. Literature Review The dynamic movement of a currency is a spinal question in international economics. In the past decades, economists have examined exchange rate behavior from partly orthogonal angles utilizing either a theoretical or empirical approach. 2.1. Exchange Rate Literature Theoretical exchange rate models date back to the classical and keynesian schools of thought (Cassel 1918;Dornbusch 1976a;Dornbusch and Krugman 1976;Mundell 1968). Due to fixed exchange rates during the Gold Standard and the Bretton Woods System, most empirical research started after the 1970s (Clark and MacDonald 1998). For the first time, our empirical study aims to address the complexity of the stochastic nature of exchange rate dynamics by introducing a new determining behavioral variable: Online search intensity. The following literature review highlights work closely related to ours. Note, there are already seminal papers and good reviews in that field (Berkowitz and Giorgianni 2001;Clarida and Gali 1995;Mark 1995;Rogoff 1995;Rossi 2013). J. Risk Financial Manag. 2021,14, 512 3 of 40 A seminal work on exchange rates is the approach by Mundell and Fleming devised around the 1960s. The Mundell-Fleming model describes the relationship between a country’s exchange rate, its output, and interest rate in an open economy. Mundell (1963) states that the behavior of the exchange rate depends crucially on the economy and vice versa. Intuitively, when money supply increases or equivalently interest rates decline, we expect a special transition process. On the one hand, a monetary expansion increases the output. Ceteris paribus, high GDP growth renders a country ´ s currency more attractive and leads to an appreciation. On the other hand, a monetary expansion causes capital outflows due to lower interest rates, as investments abroad become relatively more appealing. With a lower supply of foreign exchange, the home currency depreciates. This depreciation makes exports relatively cheap, which increases net exports, and, in turn, increases the total domestic output. This represents the impact of monetary policy under floating exchange rates while assuming perfect capital mobility. The Mundell-Fleming approach, however, fails to explain the performance of major currencies. The model assumes the purchasing power parity (PPP), which proves to be wrong under certain circumstances, such as in the short term. The PPP assumption implies constant exchange rates when, to all intents and purposes, market participants experience rather high volatilities (Brissimis et al. 2005;Liang 1998;Rogoff 1995). The inconclusiveness led to the development of other models, such as the exchange rate overshooting model by Dornbusch (1976b). This model is based on a slow adjustment of prices and consistent expectations. In the end, the model captures the phenomena of short term overshooting of exchange rates above their long-run equilibrium. Thus, the exchange rate volatility is partly attributed to market inefficiencies. Dornbusch (1976b) maintains that volatility is intrinsic to the market as the exchange rate responds to changes in monetary policy disproportionally to compensate for slow-adjusting prices. Hence, an expansionary monetary policy leads to lower interest rates and an exchange rate movement. In the short term, we expect an depreciation of the exchange rate. However, in the long-run, we expect a appreciation due to further stimulus or, in other words, the first-order effect overshoots only in the short-run. In Rogoff (2002) words, the “initial excess depreciation leaves room for the ensuing appreciation needed to simultaneously clear the bond and money markets”. Our intention when utilizing online search intensity, is to attain a better understanding of the role of expectations on the exchange rate dynamics as well as the interplay to both the goods and money market. As with every theoretical approach, the overshooting model is not a complete picture of reality. Hooper and Morton (1982), Driskill (1981) and Buiter and Miller (1981), for instance, extended the model to overcome some of its restrictions, such as allowing for changes in the long-run real exchange rate, imperfect substitutability between domestic and foreign assets, and non-zero inflation. Moreover, contemporary theoretical research by Gray (1976) and Fischer (1977) study the idea of sticky-price open economy models in order to explain the exchange rate dynamics. Frankel (1979) developed a similar model and, as a consequence, the overshooting model is sometimes described as the Dornbusch-Frankel model. A difference between the two is that Frankel (1979) argues that exchange rates are driven by their real interest rate differentials and not their nominal. In our paper, we estimate an overshooting model and an augmented-overshooting model including search intensity. The theoretical exchange rate models’ often display weak or inconclusive empirical performance. For instance, Meese and Rogoff (1983) compare in a seminal article the outof-sample forecasting accuracy of various structural models. They find that all, including the overshooting model, perform rather poorly in comparison to the simple random walk model—termed naive forecast. Their results constitute what is known as the “disconnecting puzzle” in international finance. Indeed, their modeling exercises fail to establish a significant link between real exchange rates and economic fundamentals. A later study finds similar results when analyzing modern models under certain horizons for certain criteria (Cheung et al. 2005). The results from Meese and Rogoff (1983) demonstrate that J. Risk Financial Manag. 2021,14, 512 4 of 40 market expectations of exchange rates are relevant, however, expectations are difficult to measure. The timely availability of search intensity, however, gives the measurement of expectations in this literature a new lease of life. Molodtsova and Papell (2009) show that exchange rate models can beat a random walk in an out-of-sample forecasting exercise. Sarno and Schmeling (2014) document that fundamental exchange rate models have predictive power, but their performance relies heavily on the currency and forecast horizon. Kouwenberg et al. (2017) develop a model of selected fundamentals regardless it only works for 5 out of 10 currencies, implying that it does not beat a random walk. An approved notion is that fundamentals matter in the long-run but not in the short-run (Mark 1995;Mark and Sul 2001). Engel and West (2005) state that the expectations about future macroeconomic fundamentals drive exchange rates much more than lagged fundamentals do. Their findings relate to research of Andersen et al. (2003) that find strong evidence of exchange rates reacting to news. The results support the fact that exchange rates are conditioned by both macroeconomic fundamentals on the one hand and market expectations on the other. The disparity in ideas surrounding empirical exchange rate research can be traced back to the limitation of the studies. Indeed, the context of foreign exchange markets is challenging, especially given their high interconnectedness in globalized and interconnected markets as well as the multidimensionality and stochastic nature of exchange rates. Understanding or predicting the behavior of exchange rates still represents a tall order up until today. The literature suggest that there are other—partly hidden— forces at work. Those forces are either not adequately considered or measured within the existing empirical models. On that extent, this paper aims to contribute to the literature by including a factor which thus far has been turned a blind eye to: The people ´ s attention measured through online-search intensity. We reveal that macroeconomic fundamentals and search intensity are significant drivers alike. 2.2. Literature on Search Data There is strong evidence on the usefulness of online search data. Shim et al. (2001) find that consumer’s online search patterns predict their posterior purchases. Thus, information seeking behavior measures expectations and the potential demand of customers. Google data, as the world’s leading search engine, particularly provide search intensity data via the tool of Google Trends (Choi and Varian 2012). Since 2004, Google publishes search data on every keyword instantaneously. Several papers have demonstrated the usefulness of search data successfully. Firstly, there is a seminal study that accounts for the power of search data by predicting flu dynamics (Ginsberg et al. 2009). Since then, the number of publications using search data is flourishing (Jun et al. 2017). Online search data has been proven to be useful in a whole raft of different fields. For instance Vaughan and Romero-Frias (2013) find a significant correlation between search volume of a university’s name and its academic reputation. Althouse et al. (2011) show that search queries predict dengue incidences, such as other influenza-like diseases (Ginsberg et al. 2009). In economics, Ettredge et al. (2005) examine U.S. unemployment trends and establish a significant correlation between job search data and unemployment. Da et al. (2011) propose a new method of capturing investor interest using search frequency, and Jun et al. (2017) demonstrate how Google Trends helps companies discover the perceptions consumers have about brands. Yet, there is only one paper related to our work on the entwinement of search intensity and exchange rates. Bulut (2017) finds that Google Trends surpass structural models in predicting the direction of exchange rates. He concludes that the out-of-sample forecast of Google models offers better predictions for five currency pairs when compared to structural models. However, when compared to the random walk, neither Google models nor structural models perform to a similar extent. Our research differs from Bulut’s in several aspects. First, our data has more currencies and a longer time period. Hence, our panel is significantly larger and thus permits to draw a more complete picture. Second, J. Risk Financial Manag. 2021,14, 512 5 of 40 we investigate the impact of a large sample of different search keywords and even integrate newly computed aggregate search intensity indices. Third, we utilize two well-established exchange rate models, such as the overshooting model and the augmented-overshooting model with and without search intensity. Fourth, our paper estimates the models and studies their prediction as well as the forecasting performances. Furthermore, and in contrast to Bulut (2017), our work does not rely on the PPP assumption and includes more elaborated search metrics. Last but not least, we study the exchange rate determination first and not merely the forecasting power. Yet, research with search intensity is not free from limitations. Goel et al. (2010) claim, for instance, that there is little gain in prediction when the tool is used in forecasting. Vaughan and Chen (2015) are comparing Google Trends with the Baidu Index and find that Google data verges on futility if the number of people is relatively reduced in a certain territory. Lazer et al. (2014) raise questions about the nature of predictions and argue that web search is not reliable to replace traditional methods. While not all studies have significant results, Jun et al. (2017) stress that online search has advantages in terms of immediacy and objectivity. All in all, much of the quality of search data depends on the research question and methodology. 3. Data and Methodology We utilize a strongly balanced panel to estimate different models over a quarterly time series which begins in 2004, the earliest year Google Trends data is available, and extends until 2018. The panel consists of the following ten countries: Australia, Canada, China, Germany, Japan, Mexico, Sweden, Switzerland, United Kingdom (UK) and United States (US). The sample represents ten of the twenty most traded currencies in the world (BIS 2016) . Germany represents the major economy for the Euro currency. Integrating all Eurozone countries would be more appropriate to better capture the Euro exchange, yet, we leave this task partially to future research. For each country, we analyze the following macroeconomic variables: Real exchange rate (RRT); money supply (MM); gross domestic product (GDP); interest rate (IR); consumer price index (CPI); relative price of non-tradable goods (PNT); government debt-to-GDP (DBT); terms of trade (TOT) and net foreign assets (NFA). The debt-to-GDP and money supply data is from the OECD database (OECD 2018). All other variables are gathered from the International Monetary Fund financial statistics (IMF 2018). Because debt-to-GDP is only available annually, we conduct a cubic spline interpolation in order to disaggregate the observations into a higher frequency. As data availability differs across countries and time, some missing data points are present.1 3.1. Measuring Search Intensity This paper includes online search intensity (SI), streamed from Google Trends. Search data is a proxy for measuring attention on exchange rates or related variables respectively. Google provides information on the search volume for any specific search term. The data represent the ratio of online searches made for a specific keyword in a given geographical region within a specific period to the total number of online searches made under the same specifications. The resulting time series of search intensity is scaled in the range of 0 to 100. Each search index number represents the relative popularity of a keyword (Google 2018). One of the difficulties of working with search data is selecting the appropriate set of keywords. Naccarato et al. (2018) state that a selection based on an objective and adequate method delivers useful results. Our keyword selection criteria are: (i) Keywords have to represent the major macroeconomic fundamentals of exchange rates; (ii) keywords coincide with search terms suggested by Google’s algorithm; and (iii) keywords are representative to similar studies in this literature. Table 1contains the list with all search keywords utilised in our study. These terms were selected using the aforementioned parameters and directly or indirectly affect the macroeconomic fundamentals and hence the exchange rate. J. Risk Financial Manag. 2021,14, 512 6 of 40 Table 1. Google Trends Keyword Selection. English Germany Japanese French Chinese Swedish Spanish inflation Inflation インフレーショ ンinflation 通貨膨脹inflation inflación CPI VPI 消費者物価指数IPC 消費者物價指數KPI IPC GDP BIP Version September 12, 2021 submitted to Journal Not Specified 6 of 45 Table 1: Google Trends Keyword Selection English Germany Japanese French inflation Inflation インフレション inflation CPI VPI 消者物指数IPC GDP BIP 国内生产总值PIB interest rate Zinssatz 利率taux d’intérêt loan Kredit クレジット crédit ATM Geldautomat 金自支Distribute-ur de billets job opening Stellenangebot 求人Offre d’emploi vacation Urlaub 休暇vacance shopping einkaufen ショッピング shopping exchange rate Wechselkurs 替相taux de change appreciation Aufwertung 再réévaluation Chinese Swedish Spanish 通膨inflation inflación 消者物指KPI IPC 生值BNP PIB 利率räntesats tasa de interés 借款kredit crédito 自提款bankomat cajero 缺lediga jobb oferta de trabajo 假期ferie vacacionas 物shopping compras 汇率växelkurs tipo de cambio 重估omvärdering revaluación After gathering the search intensity variables, we obtain for each keyword, i , a 245 time-series SIi,t over time t . One way to incorporate the search intensity to the model 246 is by adding each single search term. This is labelled the sum of search intensity (SIΣ t) . 247 A second way is to include aggregate search indices, for instance the mean of certain 248 search terms (SICAT t) . Among others, we include the mean of all inflation query data 249 (IN) , the average of interest rate queries (IR) , and the mean of consumption queries 250 (CON). Hence, we first obtain two trivial search intensity variables:251 SIΣ t=SIINF t+SIGDP t+SICPI t+SIIR t+SILOAN t+SIATM t+SIJO t+SIVC +SISHOP t+SIExR t (1) SICAT t=SIINF t+SIIR t+SICON t, (2) where the acronyms denote the following search keywords: search intensity of 252 inflation (SI-INF), search intensity of gross domestic product (SI-GDP), search intensity of 253 consumer price index (SI-CPI), search intensity of interest rate (SI-IR), search intensity of 254 loan (SI-LOAN), search intensity of automated teller machine (SI-ATM), search intensity 255 of job opening (SI-JO), search intensity of vacation (SI-VC), search intensity of shopping 256 (SI-SHOP), and search intensity of exchange rate (SI-ExR). Both aggregate measures are 257 based on disaggregated search queries over time.258 Nonetheless, we design four more sophisticated online search intensity indices. 259 First, we follow Chen et al. [51] and define an abnormal average change in SIi,t , computed 260 by261 ACSIi,t=SIi,t−AVSIi|t−4,t−1 SDAVSIi|t−4,t−1 , (3) PIB Version September 12, 2021 submitted to Journal Not Specified 6 of 45 Table 1: Google Trends Keyword Selection English Germany Japanese French inflation Inflation インフレション inflation CPI VPI 消者物指数IPC GDP BIP 国内生产总值PIB interest rate Zinssatz 利率taux d’intérêt loan Kredit クレジット crédit ATM Geldautomat 金自支Distribute-ur de billets job opening Stellenangebot 求人Offre d’emploi vacation Urlaub 休暇vacance shopping einkaufen ショッピング shopping exchange rate Wechselkurs 替相taux de change appreciation Aufwertung 再réévaluation Chinese Swedish Spanish 通膨inflation inflación 消者物指KPI IPC 國內生產總值BNP PIB 利率räntesats tasa de interés 借款kredit crédito 自提款bankomat cajero 缺lediga jobb oferta de trabajo 假期ferie vacacionas 物shopping compras 汇率växelkurs tipo de cambio 重估omvärdering revaluación After gathering the search intensity variables, we obtain for each keyword, i , a 245 time-series SIi,t over time t . One way to incorporate the search intensity to the model 246 is by adding each single search term. This is labelled the sum of search intensity (SIΣ t) . 247 A second way is to include aggregate search indices, for instance the mean of certain 248 search terms (SICAT t) . Among others, we include the mean of all inflation query data 249 (IN) , the average of interest rate queries (IR) , and the mean of consumption queries 250 (CON). Hence, we first obtain two trivial search intensity variables:251 SIΣ t=SIINF t+SIGDP t+SICPI t+SIIR t+SILOAN t+SIATM t+SIJO t+SIVC +SISHOP t+SIExR t (1) SICAT t=SIINF t+SIIR t+SICON t, (2) where the acronyms denote the following search keywords: search intensity of 252 inflation (SI-INF), search intensity of gross domestic product (SI-GDP), search intensity of 253 consumer price index (SI-CPI), search intensity of interest rate (SI-IR), search intensity of 254 loan (SI-LOAN), search intensity of automated teller machine (SI-ATM), search intensity 255 of job opening (SI-JO), search intensity of vacation (SI-VC), search intensity of shopping 256 (SI-SHOP), and search intensity of exchange rate (SI-ExR). Both aggregate measures are 257 based on disaggregated search queries over time.258 Nonetheless, we design four more sophisticated online search intensity indices. 259 First, we follow Chen et al. [51] and define an abnormal average change in SIi,t , computed 260 by261 ACSIi,t=SIi,t−AVSIi|t−4,t−1 SDAVSIi|t−4,t−1 , (3) BNP PIB interest rate Zinssatz 利率taux d’intérêt 利率räntesats tasa de interés loan Kredit クレジットcrédit 借款kredit crédito ATM Geldautomat 現金自動支払機 Distribute-ur de billets 自動提款機 bankomat cajero job opening Stellenangebot 求人Offre d’emploi 職缺lediga jobb oferta de trabajo vacation Urlaub 休暇vacance 假期ferie vacacionas shopping einkaufen ショッピング shopping 購物shopping compras exchange rate Wechselkurs 為替相場taux de change Version September 12, 2021 submitted to Journal Not Specified 6 of 45 Table 1: Google Trends Keyword Selection English Germany Japanese French inflation Inflation インフレション inflation CPI VPI 消者物指数IPC GDP BIP 国内生产总值PIB interest rate Zinssatz 利率taux d’intérêt loan Kredit クレジット crédit ATM Geldautomat 金自支Distribute-ur de billets job opening Stellenangebot 求人Offre d’emploi vacation Urlaub 休暇vacance shopping einkaufen ショッピング shopping exchange rate Wechselkurs 替相taux de change appreciation Aufwertung 再réévaluation Chinese Swedish Spanish 通膨inflation inflación 消者物指KPI IPC 國內生產總值BNP PIB 利率räntesats tasa de interés 借款kredit crédito 自提款bankomat cajero 缺lediga jobb oferta de trabajo 假期ferie vacacionas 物shopping compras 汇率växelkurs tipo de cambio 重估omvärdering revaluación After gathering the search intensity variables, we obtain for each keyword, i , a 245 time-series SIi,t over time t . One way to incorporate the search intensity to the model 246 is by adding each single search term. This is labelled the sum of search intensity (SIΣ t) . 247 A second way is to include aggregate search indices, for instance the mean of certain 248 search terms (SICAT t) . Among others, we include the mean of all inflation query data 249 (IN) , the average of interest rate queries (IR) , and the mean of consumption queries 250 (CON). Hence, we first obtain two trivial search intensity variables:251 SIΣ t=SIINF t+SIGDP t+SICPI t+SIIR t+SILOAN t+SIATM t+SIJO t+SIVC +SISHOP t+SIExR t (1) SICAT t=SIINF t+SIIR t+SICON t, (2) where the acronyms denote the following search keywords: search intensity of 252 inflation (SI-INF), search intensity of gross domestic product (SI-GDP), search intensity of 253 consumer price index (SI-CPI), search intensity of interest rate (SI-IR), search intensity of 254 loan (SI-LOAN), search intensity of automated teller machine (SI-ATM), search intensity 255 of job opening (SI-JO), search intensity of vacation (SI-VC), search intensity of shopping 256 (SI-SHOP), and search intensity of exchange rate (SI-ExR). Both aggregate measures are 257 based on disaggregated search queries over time.258 Nonetheless, we design four more sophisticated online search intensity indices. 259 First, we follow Chen et al. [51] and define an abnormal average change in SIi,t , computed 260 by261 ACSIi,t=SIi,t−AVSIi|t−4,t−1 SDAVSIi|t−4,t−1 , (3) 率växelkurs tipo de cambio appreciation Aufwertung 再評価réévaluation 重估omvärdering revaluación After gathering the search intensity variables, we obtain for each keyword, i , a timeseries SIi,t over time t . One way to incorporate the search intensity to the model is by adding each single search term. This is labelled the sum of search intensity (SIΣ t) . A second way is to include aggregate search indices, for instance the mean of certain search terms (SICAT t) . Among others, we include the mean of all inflation query data (IN) , the average of interest rate queries (IR) , and the mean of consumption queries (CON) . Hence, we first obtain two trivial search intensity variables: SIΣ t=SIINF t+SIGDP t+SICPI t+SIIR t+SILOAN t+SIATM t+SIJO t+SIVC +SISHOP t+SIExR t(1) SICAT t=SIINF t+SIIR t+SICON t, (2) where the acronyms denote the following search keywords: Search intensity of inflation (SI-INF), search intensity of gross domestic product (SI-GDP), search intensity of consumer price index (SI-CPI), search intensity of interest rate (SI-IR), search intensity of loan (SILOAN), search intensity of automated teller machine (SI-ATM), search intensity of job opening (SI-JO), search intensity of vacation (SI-VC), search intensity of shopping (SISHOP), and search intensity of exchange rate (SI-ExR). Both aggregate measures are based on disaggregated search queries over time. Nonetheless, we design four more sophisticated online search intensity indices. First, we follow Chen et al. (2001) and define an abnormal average change in SIi,t , computed by ACSIi,t=SIi,t−AVSIi|t−4,t−1 SDAVSIi|t−4,t−1 , (3) where AVSIi|t−4,t−1 and SDAVSIi|t−4,t−1 are the mean and standard deviation of SI for series i over the past 4 quarters, respectively. An ACSI search index, that measures the relative search to the average of the past 4-quarters, signifies an abnormally high (low) attention on the respective search expression. Second, we follow the seminal paper by Da et al. (2011) and define: LASIi,t=log(SIi,t)−log(Median(SIi|t−4,t)). (4) In short, LASIi,t measures the logarithmic value of search intensity (SI) of a search expression i minus the logarithmic value of the median of search intensity during the previous quarter. Third, we follow a related idea in the seminal work by Baker et al. (2016). We compute an aggregate search intensity index (SII) that is standardized and normalized in a range of 0 to 100. In the first step, we divide the SIi,t by the time-series standard deviation σi for all J. Risk Financial Manag. 2021,14, 512 7 of 40 search terms for all t . This creates a new time-series labeled SSi,t . Secondly, we compute for each search term and time the normalized time-series according to NSSi,t=(SSi,t−min SSi,t)×100 (max SSi,t−min SSi,t). (5) Next, we compute the mean over all NSSi,t in each quarter in order to obtain the aggregate search intensity index SIIt. Fourth, we compute our own aggregate search index labelled average standardized search intensity (ASSIi,t) . In a first step, we compute the quarterly means, defined as SSi,t , of the standardized search (SSi,t) . Second, we compute the difference of the search volume variables divided by the standard deviation—with the formula: ASSIi,t= (SIi,t−SSi,t)/σi . We utilize the following six search indices SIndexM1−M6 i,t in our econometric models: (M1) SIΣ t , (M2) SICAT t , (M3) ACSIi,t , (M4) LASIi,t , (M5) SIIt , and (M6) ASSIi,t . All search indices are based on search data and represent an attention measure for the macroeconomic drivers of the exchange rate. We include these indices as control variables in our two macroeconomic exchange rate models. Thus, we regress the exchange rate in a panel set, consisting of countries i over time t in respect to two different models ( Modeltype i,t ) and the set of six search indices (SIndexM1−M6 i,t). The model is specified as follows ExchangRatei,t=αi,t+β1Modeltype i,t+β2SIndexM1−M6 i,t+ei,t, (6) where we assume i.i.d. for the error term ei,t . All abbreviations and variables names are listed at the end of the paper. 3.2. Econometric Methodology Next, we describe the two types of exchange rate models in more detail. The macroeconomic fundamentals of the overshooting model are the price level, output, money supply, and interest rate (Dornbusch 1976b). Our regression of the overshooting model is similar to Cheung et al. (2005), where the variables are as previously described. The term et is the standard error and is normally distributed N(0, σ2 e). The ’Overshooting Model’ (OM) captures the overshooting channel via IRi,t and assumes that PPP holds merely in the long run. Our econometric equation follows this idea but incorporates the exchange rate equilibrium relationship as described by Clark and MacDonald (1998) or in work by Ewards (1989): ModelOM i,t:=ηMMMi,t+ηGGDPi,t+ηIIRi,t+ηCCPIi,t, (7) The second econometric model is an augmented-overshooting model. We define the regression equation of the ‘Augmented Model’ (AM) as follows ModelAM i,t:=ModelOM i,t+βPNTi,t+γDBTi,t+ρTOTi,t+ζNFAi,t. (8) Equation (8) incorporates the Balassa-Samuelson effect via PNTt and the portfolio balance effect via DBTtand NFAt.2 Whether a country’s currency appreciates or depreciates depends ultimately on the perceived desirability of holding that currency. Therefore, one can conceive the variables in Equations (7) and (8) as the major macroeconomic exchange rate determinants. If a nation’s inflation or debt levels are considerably high, the desirability for that currency will be low, or, in other words, there is a tendency of a weak currency. The relative price of nontradable goods, the terms of trade, and net foreign assets reflect the country’s productivity, economic health, and demand for the country’s goods and services. All of this influences the country’s currency demand. Everything else constant, the better the productivity and economic situation, the higher the desirability for holding the currency. J. Risk Financial Manag. 2021,14, 512 8 of 40 As a measure of market expectations, we include search intensity as described previously. The idea that web search represents collective attention is established by recent research, among others by Ettredge et al. (2005), Ginsberg et al. (2009) and Da et al. (2011). Ultimately, we describe the fourteen variants of our econometric regression models. Firstly, we distinguish between the two benchmark models, consisting of the ‘Overshooting Model’ (OM) in Equation (7) and the ‘Augmented Model’ (AM) in Equation (8). In order to simplify the terminology, we re-label Xj i,t:=Modelj i,t , where j denotes either the OMor AM-model. Both benchmark models capture the pure macroeconomic fundamentals, as denoted in Equation (9). Secondly, we estimate an extended model and include our six search indices. Equation (10) denotes the models with search intensity metrics (M1) SIΣ t , (M2) SICAT t , (M3) ACSIi,t , (M4) LASIi,t , (M5) SIIt , and (M6) ASSIi,t . Thus, we obtain two regression equations based on either the pure macroeconomic models or the extended models by six search indices, ExRXj i,t i,t=α+β1Xj i,t+ei,t(9) ExRXj,M1−M6 i,t i,t=α+β1Xj i,t+β2SIndexM1−M6 i,t+ei,t, (10) where ExRi,t denotes the respective exchange rate, Xj i,t denotes the macroeconomic OM — or AM —model, and SIndex represents the six search indices for each country i over time t . We test the hypothesis whether search intensity enhances the models. 3.3. Prediction and Forecasting Methodology A rigorous evaluation of the prediction and forecasting performance of our model reveals a final insight about the use and quality of online search data. First, we evaluate the prediction of our two best models with search data in comparison to the economic benchmark models. We run the prediction by utilizing the estimated coefficients from above and compute the confidence intervals of 95%. In the end, we compare the real exchange rate with the performance of the model prediction: ExRXj i,t pre =b α+ c β1Xj i,t+ei,t(11) ExRXj,M1,M6 i,t pre =b α+ c β1Xj i,t+ c β2SIndexM1,M6 i,t+ei,t(12) where Xj i,tis always the AM-model because it is outperforming the narrow OM-model. Second, we compute an in-sample forecast of the exchange rate over four quarters. In the forecasting exercise, we compare the forecasting of our benchmark model and the models with search intensity as well as a specified ARIMA-Model and the naive forecast of a random walk. According to standard lag-tests, we use an ARIMA(2,0,1) model. The forecasting equations are: ExRXj i,t+1=b α+ c β1Xj i,t+ei,t, Basic forecast ExRXj,M1,M6 i,t+1=b α+ c β1Xj i,t+ c β2SIndexM1,M6 i,t+ei,t, SI forecast ExRXj i,t+1=ρExRXj t+ρ2ExRateXj t−1+ei,t+νei,t−1, ARIMA forecast ExRXj i,t+1=ExRateXj i,t, Naive forecast. In order to evaluate the forecasting performance, we compute the square errors of each forecast Πi,t= [ExRf orecast i,t−ExRi,t]2 and the sum of square errors Π∑ i=∑t[ExRf orecast i,t− ExRi,t]2 . We evaluate the forecasting performance by testing the null-hypothesis of Πi,t and Π∑ i to be zero. A rejection of the null-hypothesis in regard to a zero mean-square error identifies the models with insufficient forecasting performance. J. Risk Financial Manag. 2021,14, 512 15 of 40 Table 5. Panel Regression Table OM Model. Model OLS Model PA Model FE Model RE Model ML dmm 1.472 *** 0.565 0.465 0.631 0.537 (0.327) (0.337) (0.331) (0.326) (0.327) GDP 1.269 * 0.397 0.317 0.451 0.375 (0.574) (0.479) (0.463) (0.468) (0.461) IR 0.110 1.231 *** 1.303 *** 1.181 *** 1.252 *** (0.224) (0.250) (0.245) (0.242) (0.243) CPI −105.3 −217.2 *** −221.4 *** −214.2 *** −218.4 *** (79.00) (64.44) (62.23) (63.05) (61.96) Constant 202.6 * 315.7 *** 319.1 *** 312.6 *** 316.9 *** (79.12) (64.66) (62.41) (63.24) (62.19) sigma(u) Constant 8.027 *** (1.863) sigma(e) Constant 8.197 *** (0.260) Observations 506 506 506 506 506 Adjusted R20.054 0.045 F 8.196 9.147 Standard errors in parentheses, FE = Fixed-Effects, RE = Random-Effects, PA = Pooled, ML = Maximum-Likelihood. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. In this paper, we follow the online search literature by defining the most appropriate keywords in order to have comparable results (Bank et al. 2011;Takeda and Wakao 2013;Vlastakis and Markellos 2012). Nonetheless, a more systematic method or theory of selecting relevant keywords is an open research question. Vaughan and Chen (2015) finds that search indices have different predictive power in different countries. Therefore, we estimate country specific regressions of the overshooting model and augmented-overshooting model for each country. Results are in the Appendix Cin Table A19 , which summarize the estimation results for the benchmark model. Similarly, Tables A20 and A23 illustrate search intensity model 1 with all disaggregated search intensity variables. In Tables A21 and A24 we report search intensity model 6 with our ASSIi,t search variables. The results show that the interest rate is at least significant at 1 percent in eight of ten countries, the relative price and terms of trade in six of ten countries, the debt-to-GDP ratio in five of ten countries, and the money supply, GDP and net foreign assets in three of ten countries. Noteworthy, the direction of those significant macroeconomic fundamentals vary from currency to currency (Tables A19 and A22). The results confirm the previous findings of the macroeconomic variables. In the OM-model search intensity variables are significant particularly for the keyword ‘exchange rate’ (Table A20). In the AM-model, however, search intensity is significant only for the keyword exchange rate in three out of nine countries (Table A23). Even though the search intensity model 6 has a similar pattern, our search intensity variables have a higher significance across most countries. Indeed, the overshooting model delivers mixed results, however, the augmentedovershooting model has significant results for all countries with an R-square in the range of [ 0.53; 0.89 ] . Notably, search intensity variables are particularly significant in the US ( Tables A20 and A21 ). The results for the other countries are rather mixed, yet having Germany, Japan, Mexico and the UK with high significance. This can be explained by Vaughan and Chen (2015) because online search data is better in countries where the market share of Google’s search engine is high, particularly in Western democracies and English speaking countries. The AM-model including search intensity variables stand out in regard of the adjusted R-square in a range of [ 0.77; 0.91 ] and highly significant F-statistics ( Appendix C J. Risk Financial Manag. 2021,14, 512 16 of 40 Tables A23 and A24 ). All findings corroborate that online search intensity covers market expectations in a similar way than news does as explored by Andersen et al. (2003), however, the results vary significantly across countries. At a first stage, we conclude that search intensity improves the estimation results and the overall fit of the models. Yet, we have findings indicating that some search variables offer minor explanatory value. In our view, further research could address this issue through a more systematic method to gather the relevant keywords. 4.1. Performance of Model Prediction and Forecasting In a next step, we evaluate the prediction performance of search intensity model 1 and model 6 in comparison to the benchmark macroeconomic OMand AM-models. Juxtaposing the prediction of the exchange rates, we find evidence that the predictive power is raised significantly in models including search intensity. The exchange rate prediction for each country is almost always inside the 95% confidence intervals. On the contrary, the benchmark OMand AM-models devoid of search intensity perform significantly inferior. A detailed evaluation of the prediction error is represented in Table 6. The smaller prediction errors in models 1 and 6 with the search intensity variables confirm the usefulness of search data under a prediction exercise. Table 6. Prediction Performance Test. Basic SI Model ASSI Model Australia 15.44 * 20.21 15.22 (4.530) (11.38) (7.984) China 3.214 2.124 0.994 (1.672) (1.931) (0.453) Germany 0.618 5.058 5.296 (0.598) (0.961) (1.503) Mexico 303.9 ** 16.31 16.74 (40.64) (5.456) (5.496) Swiss 7.447 5.287 7.840 (5.900) (5.193) (6.309) UK 28.74 1.051 1.488 (12.87) (0.624) (0.808) Observations 4 4 4 Standard errors in parentheses. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Surprising, however, is the forecasting performance. Table 7and particularly Table 8 corroborate that models with search intensity outperform those lacking it. The hypothesis that the forecasting error is of zero cannot be rejected for most of the models with search intensity data. On the contrary, the forecasting error of the macroeconomic benchmark model or ARIMA model is significantly different to zero (Table 8). Nonetheless, the naive forecast is tantamount to that of our two top models 1 and 6. Not surprisingly, the confidence intervals under forecasting are rather wide in contrast to the naive forecast. However, looking to the forecasting direction, we interestingly find that our models do predict the direction better than the naive forecast (Table 8). J. Risk Financial Manag. 2021,14, 512 17 of 40 Table 7. Forecast Performance Test. Basic ARIMA Model ASSI Prediction Naive Forecast Australia 15.44 * 9.045 15.22 1.653 (4.530) (3.006) (7.984) (0.679) China 3.214 10.74 0.994 1.218 (1.672) (5.054) (0.453) (0.769) Germany 0.618 3.489 5.296 8.192 (0.598) (1.968) (1.503) (4.582) Mexico 303.9 ** 23.61 16.74 48.86 (40.64) (14.75) (5.496) (22.59) Swiss 7.447 5.973 7.840 12.81 (5.900) (3.515) (6.309) (9.440) UK 28.74 1.787 * 1.488 2.946 (12.87) (0.468) (0.808) (1.479) Observations 4 4 4 4 Standard errors in parentheses. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Table 8. Square-Mean Error of Forecasting after 1 year. Basic ARIMA Model ASSI Forecast Naive Forecast Australia 41.84 * 25.61 * 23.21 4.683 * (10.80) (5.425) (13.45) (0.939) Canada 116.8 24.33 13.74 9.497 (.) (7.666) (.) (4.573) China 8.387 * 18.34 2.446 3.448 * (2.371) (8.670) (0.851) (1.014) Germany 0.932 12.25 ** 8.893 * 12.47 (0.304) (1.638) (1.700) (7.706) Mexico 796.8 * 70.38 ** 50.82 * 103.1 (173.1) (8.030) (10.00) (45.59) Sweden 0.0808 27.62 1.841 17.59 (.) (11.92) (.) (7.193) Swiss 14.51 *** 10.18 15.30 *** 15.92 (0.387) (4.638) (0.383) (12.02) UK 62.74 * 4.574 * 4.045 ** 7.058 (13.58) (1.359) (0.413) (2.227) US 319.3 69.42 ** 0.0780 33.53 (.) (8.401) (.) (18.23) Observations 4 4 4 4 Standard errors in parentheses. Note: Independent variable is the Real Exchange Rate. In case of unreported std. errors. in the table, the regression algorithm either cannot compute or report errors well below zero. Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Finally, we apply a forecasting exercise by using a vector error correction (VEC) model (Figures 1and 2). The VEC models broadly corroborate the prediction and forecasting performance from the previous exercises. The models including search intensity are almost always significant, while the pure macroeconomic models pale into insignificance. Note, we only illustrate the forecastingand VEC-model for the US due to high Google search intensity and longer timeseries data in the US (Figures 1and 2). 12 All in all, we find first evidence that search intensity might enhances the prediction and forecasting of exchange rate models. J. Risk Financial Manag. 2021,14, 512 18 of 40 80 90 100 110 120 130 2004q1 2007q3 2011q1 2014q3 2018q1 t1 lb_prearima/up_prearima xb prediction, dyn(52) Fitted values Real_ExchRate yhat Exchange Rate vs. ASSI Model Figure 1. US—Forecasting Errors. 60 80 100 120 50 100 150 50 100 150 200 250 2011q3 2013q3 2015q3 2017q3 2011q3 2013q3 2015q3 2017q3 Forecast for Real_ExchRate Forecast for Real_ExchRate Forecast for Real_ExchRate 95% CI forecast observed Figure 2. US—VEC-Model Forecasting. 4.2. Limitations Panel data are a prefect tool for studying exchange rate dynamics across countries and time. Nevertheless, one obstacle is missing data points in large panel sets. However, we minimize this issue by using quarterly data over fourteen years and 10 countries. In this study, we have further limitations due to the availability of the search intensity data. J. Risk Financial Manag. 2021,14, 512 19 of 40 Google provides data from the year of 2004 onwards. Thus, our sample starts in 2004 and runs until 2018. Moreover, there is an unsolved limitation of possible structural breaks during the time period of 14 years and across various countries. This issue is up to future research. In the near future, as the time range of online data enlarges and as the timing of structural breaks is feasible, we plan to enhance the research by practical applications. Last but not least, when working with online search data, the choice of keywords is critical (Naccarato et al. 2018). Different search terms deliver partly different outputs. So far, there is no universal method of selecting keywords. Yet, even search data has internal flaws. Google Trends is using sampling methods, which might affect the data by a few percentage points from day to day according to Choi and Varian (2012). This creates replication problems if data is downloaded at different times. 5. Conclusions In today’s post-coronavirus economy, the challenges of the globalized supply chains and the respective exchange rate dynamics has come to the foreground. Much like the sunlight to flowers to grow, the exchange rates is key to international trade, economic and political stability in an interconnected and globalized world. Modeling exchange rates, however, is a rather tedious and sophisticated task due to complex non-linear dynamics. Most of the time, the literature argues that the naive forecast is the best prediction of exchange rates in the short-run. Yet, we show that exchange rate models augmented by online search intensity metrics enhance the estimation as well as the prediction and forecasting performance of standard macroeconomic models. In some instances, our generalized exchange rate models even beat the gold-standard of the naive forecast. Our paper reveals the following results: Some single-word online search queries appear to be of high relevance. Particularly the keywords ‘interest rate’ and ‘exchange rate’ are robust and significant across almost all models. Indeed, we find that four out of ten search variables are robustly significant at one percent and enhance the macroeconomic exchange rate models. Furthermore, one of our newly created search metrics are beneficial, particularly our ASSI -metric. Moreover, we demonstrate that the country regressions corroborate the panel results, yet the predictive power of search intensity in regard to exchange rates vary by country. Finally, we find higher prediction performance for our exchange rate models with search intensity, particularly in regard to the direction of the exchange rate forecast. Overall, our approach reveals a value-added of search intensity in exchange rate models. The practicality and benefits of online search cannot be understated. As the field develops and some of its limitations are overcome, big data analytics will enhance present exchange rate models. A more thorough keyword selection process might pave the way for a more systematic evaluation in future. In the end, we might obtain a better understanding of the complex non-linear currency dynamics in the global economy. Author Contributions: B.H. and L.d.S. equally contributed to this research article. All authors have read and agreed to the published version of the manuscript. Funding: We are grateful that the publication fee was financed by Albert-Ludwigs-Universität Freiburg, Universitätsbibliothek, Geschäftsstelle des Konsortiums Baden-Württemberg. We have not received further external funding for the research project. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data is available from authors upon request. Acknowledgments: We are very grateful to our research assistant Adrian Elsässer Briones who has been editing a preliminary paper version. Conflicts of Interest: The authors declare no conflict of interest. J. Risk Financial Manag. 2021,14, 512 20 of 40 Abbreviations Abbreviations and Variables used in this manuscript: RRT Real exchange rate NRT Nominal exchange rate MM Money Supply dmm Log-difference of MM GDP Gross Domestic Product IR Interest rate CPI Consumer Price Index PNT Relative price of non-tradable goods DBT Debt-to-GDP ratio ddbt Log-difference of DBT TOT Terms of Trade NFA Net foreign assets dnfa Log-difference of NFA SI Search Intensity SI-INF Search Intensity of Inflation SI-GDP Search Intensity of GDP SI-CPI Search Intensity of CPI SI-IR Search Intensity of Interest Rate SI-LOAN Search Intensity of Loan SI-ATM Search Intensity of ATM SI-JO Search Intensity of Job Opening SI-VC Search Intensity of Vacation SI-SHOP Search Intensity of Shopping SI-ExR Search Intensity of Exchange Rate SSi,tStandardized search defined by SIi,tdivided by σi SSi,tQuarterly means of SSi,t SIΣ tAggregate sum of all search intensities SICAT tMean of each search category ACSIi,tAbnormal average change in search intensity LASIi,tLogarithmic difference of SIi,t SIIi,tAggregate normalized mean value of search intenstiy ASSIi,tDifference of SIi,tand SSi,t J. Risk Financial Manag. 2021,14, 512 21 of 40 Appendix A Table A1. Summary statistics. Variable Mean (Std. Dev.) Min. Max. N NRT 100.195 (11.807) 66.326 130.573 560 RRT 99.743 (11.146) 69.283 132.054 560 MM 107.72 (35.901) 33.641 249.786 560 dmm 1.84 (1.53) −1.501 8.519 550 GDP 0.537 (0.845) −5.22 2.704 532 IR 1.935 (2.312) −2 10.05 543 CPI 1.005 (0.007) 0.972 1.036 560 PNT 1.012 (0.037) 0.908 1.177 560 DBT 0.852 (0.511) 0.185 2.388 460 ddbt 0.001 (0.018) −0.284 0.033 451 ToT 1.009 (0.131) 0.616 1.498 474 NFA 99.856 (8.793) 71.986 134.897 560 dnfa 0.456 (1.368) −12.237 6.618 550 SI-INF 47.754 (18.979) 6 92.333 560 SI-GDP 50.965 (14.454) 17 93.667 560 SI-CPI 45.892 (18.973) 6 96.333 560 SI-IR 48.085 (15.188) 5 89 560 SI-LOAN 48.132 (22.33) 5 97 560 SI-ATM 44.246 (24.275) 1 99.333 560 SI-JO 44.92 (20.134) 0 96.333 560 SI-SHOP 58.042 (16.645) 2 96.667 560 SI-ExR 29.958 (22.78) 0 97.333 560 Table A2. Test of Multi-collinearity (VIF Values). Variable Pooled OLS Pooled OLS Pooled OLS Pooled OLS OM Model 1 AM Model 1 OM Model 2 AM Model 2 IR 1.34 2.34 1.21 1.36 CPI 1.21 1.21 1.20 1.70 MM 1.16 0.79 Log(Money) 1.12 1.15 GDP 1.04 1.04 1.08 1.09 DBT 1.87 Log(Debt) 1.11 ToT 1.27 1.05 RNT 1.10 1.13 Mean VIF 1.18 1.44 1.15 1.29 Table A3. Breusch-Pagan (LM) test for heteroscedasticity. Variable chi2-Value p-Value Pooled OLS OM Model 1 28.13 0.00 Fixed-Effect OM Model 1 553.27 0.00 Pooled OLS OM Model 2 7.96 0.00 Pooled OLS AM Model 1 3.41 0.06 Fixed-Effect AM Model 1 219.81 0.00 Pooled OLS AM Model 2 8.03 0.00 Table A4. Modified Wald Test for group (panel) heteroscedasticity. Variable chi2-Value p-Value Fixed-Effect OM Model 1 230.58 0.00 Fixed-Effect AM Model 1 214.66 0.00 J. Risk Financial Manag. 2021,14, 512 22 of 40 Table A5. Wooldridge test for autocorrelation in panel data. Variable F-Statistic p-Value Fixed-Effect OM Model 1 239.08 0.00 Fixed-Effect AM Model 1 186.57 0.00 Table A6. Ramsey REST test for omitted variables in panel data. Variable F-Statistic p-Value Pooled OLS OM Model 1 1.43 0.23 Pooled OLS OM Model 2 5.13 0.00 Pooled OLS AM Model 1 2.38 0.07 Pooled OLS AM Model 2 3.76 0.01 Table A7. Breusch-Pagan LM test for random effects. Variable chi2-Statistic p-Value OM Model 1 1451.87 0.00 OM Model 2 1371.34 0.00 AM Model 1 1707.50 0.00 AM Model 2 0.00 1.00 Table A8. Hausman test for panel data. Variable chi2-Statistic p-Value OM Model 1 29.09 0.00 OM Model 2 289.52 0.00 AM Model 1 −596.81 1.00 AM Model 2 51.39 0.00 Appendix B. Figures about Countryand Time-Effects 60 80 100 120 140 0 2 4 6 8 10 group(Country) Real_ExchRate RER_mean2 Figure A1. Scatter Plot for Country Effects. J. Risk Financial Manag. 2021,14, 512 23 of 40 60 80 100 120 140 1960q1 1965q1 1970q1 1975q1 group(Quarter) Real_ExchRate RER_mean1 Figure A2. Scatter Plot for Time Effects. 95 100 105 110 115 120 2004q1 2007q3 2011q1 2014q3 2018q1 t1 lb_pre/up_pre Fitted values Real_ExchRate Exchange Rate vs. Basic Model 90 100 110 120 130 2004q1 2007q3 2011q1 2014q3 2018q1 t1 lb_pre2/up_pre2 Fitted values Real_ExchRate Exchange Rate vs. SI Model 90 100 110 120 130 2004q1 2007q3 2011q1 2014q3 2018q1 t1 lb_pre3/up_pre3 Fitted values Real_ExchRate Exchange Rate vs. ASSI Model Figure A3. US—Exchange Rate Prediction. J. Risk Financial Manag. 2021,14, 512 24 of 40 Appendix C Table A9. Panel Regression OM-Fixed-Effects and AM-Random-Effects Models 1. OM-Model OM-FE Model AM-Model AM-RE Model MM 0.0241 0.162 *** 0.0484 ** 0.175 *** (0.0152) (0.0233) (0.0172) (0.0280) GDP 0.545 1.161 * −0.146 0.173 (0.467) (0.522) (0.445) (0.462) IR 1.478 *** 0.320 1.907 *** 1.445 *** (0.299) (0.254) (0.280) (0.296) CPI −214.1 *** −169.0 * −51.38 −90.08 (62.70) (70.25) (63.37) (64.92) SI-INF 2.603 *** 2.618 *** (0.658) (0.659) SI-GDP 2.704 *** 1.874 ** (0.597) (0.618) SI-CPI −1.161 −0.397 (0.685) (0.618) SI-IR −2.058 *** −0.743 (0.575) (0.521) SI-LOAN −3.525 *** 0.750 (0.661) (0.716) SI-ATM −0.178 1.108 (0.538) (0.657) SI-JO −1.231 * −1.990 *** (0.554) (0.579) SI-VC −0.967 * −0.210 (0.487) (0.476) SI-SHOP 2.071 ** 0.212 (0.656) (0.701) SI-ExR −3.985 *** −6.330 *** (0.734) (0.835) PNT 99.67 *** 158.2 *** (13.14) (16.03) DBT 0.716 3.632 * (1.250) (1.513) ToT 25.72 *** 10.93 ** (3.545) (3.711) Constant 310.4 *** 256.3 *** 15.36 −5.667 (62.91) (70.32) (66.40) (67.82) Observations 515 515 447 447 Standard errors in parentheses, FE = Fixed-Effects, RE = Random-Effects. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Table A10. Panel Regression OM-Fixed-Effects and AM-Fixed-Effects Models 2. OM-Model OM-FE Model AM-Model AM-FE Model Log(MM) 0.465 0.724 * 0.722 * 0.766 ** (0.331) (0.331) (0.291) (0.296) GDP 0.317 0.0250 0.0291 −0.247 (0.463) (0.448) (0.387) (0.373) IR 1.303 *** 1.288 *** 1.573 *** 1.560 *** (0.245) (0.321) (0.214) (0.281) CPI −221.4 *** −161.5 ** −114.9 −92.27 (62.23) (59.56) (64.56) (61.05) J. Risk Financial Manag. 2021,14, 512 31 of 40 Table A15. Cont. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 ASSI-IR −0.909 *** (0.273) ASSI-LOAN −0.108 (0.395) ASSI-ATM 0.257 (0.469) ASSI-JO 0.414 (0.363) ASSI-VC 0.0527 (0.196) ASSI-SHOP 0.347 (0.285) ASSI-ExR −0.550 (0.484) Constant 120.0 *** 125.9 *** 124.0 *** 127.5 *** 128.0 *** 121.5 *** 125.6 *** (17.23) (17.79) (17.65) (17.47) (17.89) (17.31) (17.85) Observations 515 515 515 509 501 515 515 Adjusted R2 F Standard errors in parentheses. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Table A16. Panel Regression Table AM Model 2 (Robust and HAR). Model OLS Model FE Model RE Model FE-HAR Model RE-HAR Model GLS(h) Model GLS(p) GDP −0.431 0.137 −0.431 0.0273 0.0212 0.106 −0.0173 (0.558) (0.636) (0.532) (0.153) (0.153) (0.376) (0.148) IR 1.516 *** 2.413 * 1.516 * 2.211 *** 2.099 *** 1.439 *** 2.041 *** (0.236) (0.777) (0.604) (0.369) (0.354) (0.225) (0.323) CPI −0.111 −114.9 −0.111 −36.31 −39.36 * 6.738 −41.17 * (70.83) (83.95) (76.25) (20.05) (18.62) (58.05) (17.57) PNT 122.2 *** 87.28 122.2 *** 81.84 *** 86.78 *** 108.2 *** 90.49 *** (14.34) (43.65) (35.76) (23.67) (19.52) (12.33) (17.12) DBT −0.372 4.985 −0.372 6.239 0.0403 1.965 4.727 (1.322) (7.994) (4.188) (6.983) (3.674) (1.133) (4.738) ToT 20.76 *** 40.55 *** 20.76 18.90 *** 20.58 *** 25.34 *** 20.81 *** (3.577) (6.494) (13.36) (4.258) (3.951) (3.354) (3.792) NFA 0.119 0.128 0.119 0.0764 0.120 0.114 * 0.120 (0.0654) (0.150) (0.113) (0.189) (0.128) (0.0535) (0.0988) Constant −58.76 64.09 −58.76 16.25 *** 13.99 −58.02 6.294 (75.75) (115.1) (116.2) (3.304) (29.23) (60.28) (26.29) Observations 447 447 447 438 447 447 447 Adjusted R20.241 0.333 0.131 F 31.50 99.01 11.57 Standard errors in parentheses, FE/RE-HAR = Fixed/Random-Effects heteroskedasticity and autocorrelation robust, GLS(h or p) = Generalized . Least Square Regression with heteroscedastic but uncorrelated error structure or panel-specific AR1 autocorrelation structure. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Table A17. Panel Regression Table Random-Effects OM Model 2 (HAR). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 GDP 0.0212 −0.0888 −0.0341 −0.0494 −0.0747 −0.0238 −0.0935 (0.153) (0.155) (0.155) (0.156) (0.160) (0.156) (0.155) IR 2.099 *** 1.799 *** 1.909 *** 2.047 *** 2.108 *** 1.978 *** 1.795 *** (0.354) (0.347) (0.343) (0.340) (0.357) (0.342) (0.347) CPI −39.36 * −51.57 * −44.72 * −64.41 ** −47.80 * −43.97 * −50.04 * (18.62) (21.45) (21.64) (21.93) (22.51) (21.32) (21.59) PNT 86.78 *** 74.17 *** 74.26 *** 79.59 *** 75.29 *** 77.02 *** 74.07 *** (19.52) (15.59) (15.58) (16.05) (16.15) (15.59) (15.60) DBT 0.0403 (3.674) ToT 20.58 *** 20.37 *** 21.23 *** 20.51 *** 21.57 *** 21.13 *** 20.52 *** (3.951) (4.006) (4.019) (4.000) (4.083) (4.018) (4.014) J. Risk Financial Manag. 2021,14, 512 32 of 40 Table A17. Cont. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 NFA 0.120 (0.128) dmm −0.110 −0.148 −0.119 −0.0723 −0.178 −0.103 (0.140) (0.140) (0.141) (0.146) (0.141) (0.140) ddbt 11.07 8.267 8.819 7.278 8.556 10.35 (15.59) (15.64) (15.51) (15.75) (15.68) (15.64) dnfa 0.00239 0.0684 0.134 0.0204 0.0911 −0.00846 (0.135) (0.133) (0.137) (0.140) (0.133) (0.136) SI_INF 0.00558 (0.0171) SI_GDP 0.00445 (0.0188) SI_CPI 0.0112 (0.0184) SI_IR −0.0666 *** (0.0182) SI_LOAN −0.00624 (0.0199) SI_ATM 0.00728 (0.0210) SI_JO 0.0359 * (0.0183) SI_SHOP −0.000263 (0.0188) SI_ExR −0.0509 * (0.0229) SIINF −0.00489 (0.0170) SIIR −0.0860 ** (0.0301) SICON 0.0368 (0.0290) ACSI-INF 0.00422 (0.0402) ACSI-GDP 0.0773 (0.0791) ACSI-CPI 0.0702 (0.0755) ACSI-IR −0.206 ** (0.0632) ACSI-LOAN −0.0723 (0.0727) ACSI-ATM −0.0285 (0.0629) ACSI-JO 0.171 ** (0.0612) ACSI-VC 0.0411 (0.0385) ACSI-SHOP 0.101 (0.0799) ACSI-ExR −0.0236 (0.0532) LASI-INF −0.0405 (1.642) LASI-GDP −0.337 (1.988) LASI-CPI 1.374 (1.622) LASI-IR −5.998 *** (1.740) LASI-LOAN −0.777 (1.500) LASI-ATM −0.619 (1.628) J. Risk Financial Manag. 2021,14, 512 33 of 40 Table A17. Cont. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 LASI-JO 2.807 * (1.259) LASI-VC 1.597 (1.346) LASI-SHOP −0.760 (2.087) LASI-ExR −0.594 (1.166) SII −0.0585 (0.0379) ASSI-INF 0.0908 (0.325) ASSI-GDP 0.0818 (0.273) ASSI-CPI 0.214 (0.349) ASSI-IR −1.031 *** (0.278) ASSI-LOAN −0.136 (0.445) ASSI-ATM 0.114 (0.519) ASSI-JO 0.681 (0.373) ASSI-VC 0.137 (0.206) ASSI-SHOP 0.0378 (0.319) ASSI-ExR −1.211 * (0.528) Constant 13.99 53.84 * 46.25 * 58.79 ** 45.08 * 43.04 * 51.98 * (29.23) (21.74) (22.07) (22.50) (22.85) (21.58) (21.93) Observations 447 438 438 433 426 438 438 Adjusted R2 F Standard errors in parentheses. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Table A18. Panel Regression OMAnd AM-Model 2 with Random-Effects (Robust). OM-Model OM-FE Model AM-Model AM-RE Model ModelAM_FEH2 ModelAM_FEH7 dmm −0.183 −0.141 −0.140 −0.110 −0.103 (0.131) (0.133) (0.133) (0.140) (0.140) GDP −0.0508 −0.139 −0.141 0.0212 −0.0888 −0.0935 (0.159) (0.160) (0.160) (0.153) (0.155) (0.155) IR 1.361 *** 1.259 *** 1.255 *** 2.099 *** 1.799 *** 1.795 *** (0.341) (0.345) (0.346) (0.354) (0.347) (0.347) CPI −23.38 −34.08 * −33.99 * −39.36 * −51.57 * −50.04 * (16.69) (17.06) (17.09) (18.62) (21.45) (21.59) SI_INF 0.000549 0.00558 (0.0165) (0.0171) SI_GDP 0.0269 0.00445 (0.0178) (0.0188) SI_CPI 0.00479 0.0112 (0.0187) (0.0184) SI_IR −0.0659 *** −0.0666 *** (0.0182) (0.0182) SI_LOAN −0.0140 −0.00624 (0.0181) (0.0199) SI_ATM 0.0212 0.00728 (0.0195) (0.0210) SI_JO 0.0193 0.0359 * (0.0184) (0.0183) J. Risk Financial Manag. 2021,14, 512 34 of 40 Table A18. Cont. OM-Model OM-FE Model AM-Model AM-RE Model ModelAM_FEH2 ModelAM_FEH7 SI_SHOP 0.0201 −0.000263 (0.0166) (0.0188) SI_ExR −0.0161 −0.0509 * (0.0209) (0.0229) ASSI-INF 0.00877 0.0908 (0.314) (0.325) ASSI-GDP 0.391 0.0818 (0.257) (0.273) ASSI-CPI 0.0908 0.214 (0.355) (0.349) ASSI-IR −1.004 *** −1.031 *** (0.278) (0.278) ASSI-LOAN −0.314 −0.136 (0.404) (0.445) ASSI-ATM 0.499 0.114 (0.488) (0.519) ASSI-JO 0.380 0.681 (0.378) (0.373) ASSI-VC 0.0243 0.137 (0.198) (0.206) ASSI-SHOP 0.342 0.0378 (0.284) (0.319) ASSI-ExE −0.377 −1.211 * (0.481) (0.528) PNT 86.78 *** 74.17 *** 74.07 *** (19.52) (15.59) (15.60) DBT 0.0403 (3.674) ToT 20.58 *** 20.37 *** 20.52 *** (3.951) (4.006) (4.014) NFA 0.120 (0.128) ddbt 11.07 10.35 (15.59) (15.64) dnfa 0.00239 −0.00846 (0.135) (0.136) Constant 121.7 *** 132.3 *** 132.2 *** 13.99 53.84 * 51.98 * (16.99) (17.68) (17.72) (29.23) (21.74) (21.93) Observations 506 506 506 447 438 438 Standard errors in parentheses, FE/RE = Fixed/Radome Effects, FEH = Fixed-Effects Robust Std. Errors. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Table A19. Panel Regression OM Model for Countries. Australia China Canada Germany Japan Mexico Sweden Switzerland UK US dmm 1.732 −1.024 0.548 − 3.791 *** − 15.03 *** 0.133 −0.813 * 0.728 2.047 * 0.890 (1.299) (1.073) (1.298) (0.600) (3.935) (0.731) (0.390) (0.641) (0.932) (1.476) GDP 4.660 1.933 −20.59 ** −0.912 −1.135 1.767 1.942 ** 0.707 4.991 *** 0.923 (3.117) (1.620) (6.787) (0.515) (1.185) (1.158) (0.605) (1.320) (1.026) (1.851) IR −2.248 ** 0.826 −0.390 4.133 *** −25.93 ** 2.077 ** 2.892 *** − 6.669 *** 3.101 *** 0.908 (0.730) (0.748) (6.502) (0.384) (9.354) (0.700) (0.448) (0.716) (0.373) (0.625) CPI 600.6 * 64.63 −246.2 −142.3 −645.1 * −248.0 −10.03 −104.4 −395.9 ** −173.5 (293.5) (178.4) (187.4) (155.7) (244.9) (166.4) (111.4) (108.8) (129.2) (142.3) Constant −506.9 28.06 399.3 * 242.5 745.2 ** 335.3 * 107.9 205.3 498.0 *** 276.3 (295.1) (179.1) (189.3) (156.1) (245.0) (166.8) (111.3) (108.9) (130.0) (143.2) Observations 55 52 28 53 52 55 52 53 54 52 Adjusted R20.114 −0.000 0.347 0.712 0.298 0.135 0.486 0.680 0.759 0.015 F 2.735 1.000 4.595 33.18 6.410 3.108 13.05 28.64 42.63 0.810 Standard errors in parentheses. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. J. Risk Financial Manag. 2021,14, 512 35 of 40 Table A20. Panel Regression OM Model with SI for Countries. Australia China Canada Germany Japan Mexico Sweden Switzerland UK US dmm 2.448 0.340 1.368 −3.035 ** 2.860 0.669 −0.108 0.409 0.597 −1.246 (1.468) (1.033) (0.836) (1.008) (3.806) (0.534) (0.395) (0.541) (1.297) (0.813) GDP 4.035 0.955 −5.698 −1.176 * −1.265 * 0.971 1.825 ** 0.424 3.415 ** −0.534 (2.660) (1.472) (4.591) (0.532) (0.610) (0.814) (0.588) (1.269) (1.223) (1.083) IR −0.198 −1.435 −1.951 2.155 * − 31.15 *** 1.290 3.079 *** −2.036 * 4.607 *** 1.620 ** (1.936) (1.041) (3.522) (0.859) (4.752) (1.087) (0.712) (0.992) (0.967) (0.531) CPI 888.0 ** 93.61 −165.3 52.50 −228.4 −146.8 −80.15 −91.92 −328.4 * −121.9 (279.3) (154.6) (115.1) (156.1) (135.2) (124.2) (93.32) (108.3) (139.5) (96.63) SI-INF −0.322 0.0349 0.104 0.158 −0.134 −0.258 0.103 0.0462 −0.0243 −0.0745 (0.190) (0.150) (0.0798) (0.0787) (0.0686) (0.225) (0.0589) (0.0690) (0.125) (0.117) SI-GDP −0.0808 −0.0869 0.0917 −0.0940 0.101 0.0488 −0.0483 0.0434 0.0657 0.222 * (0.203) (0.111) (0.111) (0.0824) (0.0856) (0.273) (0.0701) (0.104) (0.129) (0.107) SI-CPI −0.121 −0.362 ** 0.109 −0.0414 −0.194 −0.156 −0.0552 −0.181 −0.148 0.510 ** (0.184) (0.113) (0.130) (0.0784) (0.110) (0.173) (0.0551) (0.114) (0.106) (0.160) SI-IR 0.114 −0.165 0.240 −0.129 −0.128 −0.0514 −0.0299 0.0237 −0.0791 −0.0968 (0.133) (0.204) (0.234) (0.0719) (0.107) (0.216) (0.0844) (0.0657) (0.116) (0.121) SI-LOAN −0.385 −0.178 0.0559 0.131 0.0332 − 0.346 *** −0.126 0.0521 0.207 0.324 *** (0.265) (0.131) (0.105) (0.0678) (0.104) (0.0915) (0.0735) (0.0711) (0.133) (0.0902) SI-ATM 0.134 −0.281 * −0.309 −0.159 * −0.0645 0.307 −0.0530 0.145 −0.313 * 0.674 *** (0.175) (0.135) (0.229) (0.0757) (0.0976) (0.162) (0.0808) (0.117) (0.126) (0.160) SI-JO 0.255 * −0.167 −0.667 0.0278 −0.143 −0.141 0.158 −0.0633 0.0479 −0.248 (0.102) (0.0972) (0.315) (0.0676) (0.139) (0.150) (0.0822) (0.0643) (0.0651) (0.181) SI-SHOP 0.265 −0.0918 0.140 −0.0824 0.310 * 0.343 0.419 *** 0.211 ** −0.176 0.362 *** (0.164) (0.123) (0.136) (0.0625) (0.127) (0.200) (0.106) (0.0710) (0.116) (0.0884) SI-ExR −0.0913 −0.224 0.266 * −0.0316 − 0.406 *** 0.00885 −0.0550 0.00953 0.222 0.166 ** (0.115) (0.119) (0.0970) (0.0472) (0.0581) (0.0982) (0.0492) (0.0981) (0.126) (0.0607) Constant −785.6 ** 78.85 270.8 * 58.03 334.9 * 225.6 154.8 174.5 457.8 ** 118.6 (281.5) (159.7) (118.8) (156.2) (140.9) (125.6) (94.05) (105.2) (142.4) (95.06) Obs. 55 52 28 53 52 55 52 53 54 52 Adj. R20.452 0.577 0.865 0.775 0.854 0.658 0.700 0.843 0.797 0.802 F 4.429 6.346 14.29 14.79 23.87 9.002 10.15 22.42 16.97 16.94 Standard errors in parentheses. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Table A21. Panel Regression OM Model with ASSI for Countries. Australia China Canada Germany Japan Mexico Sweden Swiss UK US dmm 2.411 −0.0523 1.561 −3.090 ** 2.500 0.671 −0.0313 0.357 1.699 −1.176 (1.455) (1.068) (0.786) (1.037) (3.719) (0.541) (0.386) (0.535) (1.232) (0.791) GDP 3.929 0.953 −3.980 −1.143 * −1.564 * 0.992 1.803 ** 0.806 3.148 ** −1.163 (2.638) (1.459) (4.384) (0.550) (0.621) (0.836) (0.572) (1.282) (1.115) (1.110) IR 0.564 −1.061 −0.135 2.117 * − 32.83 *** 1.254 3.601 *** −2.095 * 3.782 *** 1.343 * (2.003) (1.072) (3.435) (0.879) (4.738) (1.127) (0.752) (0.980) (0.919) (0.539) CPI 883.0 ** 177.8 −133.3 71.37 −262.1 −150.6 −145.8 −93.54 −278.3 * −156.6 (276.8) (166.6) (108.7) (170.4) (133.3) (128.4) (97.98) (107.0) (127.8) (95.95) ASSI-INF −7.237 0.141 2.455 2.975 −2.250 −4.818 2.544 * 1.397 −0.952 −0.0424 (3.670) (2.859) (1.437) (1.515) (1.281) (4.346) (1.138) (1.343) (2.163) (2.292) ASSIGDP −1.347 −2.019 2.227 −1.449 1.192 0.806 −0.550 1.211 0.817 2.171 (2.909) (1.699) (1.576) (1.243) (1.218) (4.059) (0.989) (1.546) (1.694) (1.616) ASSI-CPI −0.0891 −6.420 ** 1.527 −0.869 −2.890 −2.936 −1.889 −2.726 −2.989 6.331 (3.841) (2.148) (2.314) (1.531) (2.079) (3.331) (1.121) (2.198) (1.828) (3.500) ASSI-IR 1.871 −2.338 4.002 −1.810 −2.647 −0.862 −1.138 0.620 −1.710 −1.505 (2.004) (3.072) (3.317) (1.207) (1.642) (3.359) (1.304) (1.003) (1.609) (1.787) ASSILOAN −10.27 −5.412 2.112 3.078 0.422 −7.573 ** −3.887 * 1.565 1.543 6.844 ** (6.001) (3.112) (2.232) (1.626) (2.275) (2.304) (1.705) (1.593) (2.881) (1.972) ASSIATM 4.895 −5.734 −6.500 −3.718 −3.106 7.558 −1.685 0.655 −7.319 * 13.83 ** (4.395) (3.353) (5.204) (1.920) (2.480) (4.046) (1.920) (3.466) (2.774) (4.044) ASSI-JO 5.147 * −3.448 −11.87 0.681 −3.155 −2.751 3.270 * −1.155 1.851 −7.540 (2.044) (1.942) (5.964) (1.437) (2.732) (3.123) (1.611) (1.282) (1.226) (3.816) ASSI-VC −3.535 2.348 −2.308 −0.256 2.695 −0.410 −1.336 1.856 3.826 ** 3.085 (2.668) (1.819) (1.301) (0.866) (1.575) (2.815) (0.751) (1.312) (1.247) (1.727) J. Risk Financial Manag. 2021,14, 512 36 of 40 Table A21. Cont. Australia China Canada Germany Japan Mexico Sweden Swiss UK US ASSISHOP 4.955 −0.592 2.217 −1.350 7.872 ** 5.628 6.287 ** 3.888 ** −2.817 7.380 *** (2.728) (2.159) (2.108) (1.055) (2.592) (3.419) (1.762) (1.197) (1.758) (1.618) ASSI-ExR 0.115 −3.597 6.898 ** −0.655 − 8.503 *** 0.303 −0.322 −0.608 2.418 3.933 ** (3.080) (2.938) (2.109) (1.111) (1.364) (2.367) (1.209) (2.283) (2.739) (1.348) Constant −780.4 ** −15.50 231.7 39.20 349.5 * 230.0 227.6 * 168.8 403.6 ** 163.4 (278.9) (174.4) (112.8) (170.4) (137.7) (130.7) (100.2) (103.9) (130.6) (95.78) Obs. 55 52 28 53 52 55 52 53 54 52 Adj. R20.462 0.584 0.883 0.770 0.861 0.650 0.716 0.847 0.832 0.813 F 4.314 6.115 15.52 13.42 23.50 8.161 10.19 21.50 19.74 16.86 Standard errors in parentheses. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Table A22. Panel Regression AM Model for Countries. Australia China Canada Germany Japan Mexico Sweden Switzerland UK dmm 0.277 0.750 −2.618 *** −6.784 0.124 −0.783 * 0.140 −0.661 −0.447 (0.866) (0.519) (0.674) (5.287) (0.323) (0.300) (0.417) (0.997) (1.032) GDP 2.052 −2.638 ** 0.0985 0.483 −0.439 1.726 *** −1.297 3.298 ** −1.414 (1.794) (0.947) (0.522) (1.117) (0.542) (0.472) (0.961) (0.990) (1.460) IR 1.782 * −3.958 *** 3.290 *** −42.86 *** 0.243 3.103 *** −3.072 ** 4.811 *** 1.608 *** (0.809) (0.522) (0.586) (10.73) (0.377) (0.456) (0.936) (0.458) (0.416) CPI 305.8 56.57 −16.35 −657.8 −74.39 −39.73 −253.3 ** 162.2 3.320 (189.4) (110.2) (173.9) (332.3) (65.18) (85.79) (71.37) (136.1) (143.7) PNT 191.3 *** 6.358 −9.541 −146.3 189.4 *** 83.24 *** 260.5 *** 188.3 *** −95.05 * (37.43) (33.72) (50.53) (154.6) (33.42) (12.61) (53.16) (41.77) (41.86) ddbt −100.6 −823.0 *** 346.2 *** 1455.1 ** −18.85 −1.093 491.7 *** −100.2 −378.4 ** (74.34) (130.2) (76.74) (484.0) (74.25) (13.57) (99.65) (68.78) (116.2) ToT 61.39 *** 203.2 *** 28.53 34.78 78.89 *** −80.14 *** 22.48 *** −96.36 241.7 *** (7.108) (15.79) (48.31) (18.37) (10.30) (21.17) (5.610) (51.27) (38.33) dnfa 1.610 ** −1.149 * −0.255 1.204 −0.914 −0.491 1.418 −2.721 * 0.245 (0.525) (0.464) (0.661) (1.461) (0.878) (0.547) (1.013) (1.040) (0.578) Constant −469.0 * −160.7 96.23 865.3 * −93.22 130.7 64.38 −155.4 −42.88 (195.4) (113.5) (177.9) (363.4) (71.01) (92.48) (79.07) (151.9) (143.2) Observations 52 52 48 48 45 51 38 52 52 Adjusted R20.742 0.792 0.782 0.534 0.745 0.746 0.896 0.875 0.612 F 19.32 25.22 22.12 7.738 17.11 19.40 40.63 45.55 11.07 Standard errors in parentheses. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Table A23. Panel Regression AM Model with SI for Countries. Australia China Canada Germany Japan Mexico Sweden Switzerland UK dmm −0.590 −0.295 −0.643 3.074 −0.366 −0.0776 0.147 −1.251 −1.298 * (1.163) (0.640) (1.044) (3.708) (0.363) (0.342) (0.436) (1.291) (0.637) GDP 0.848 −2.061 * −0.161 −0.234 −0.499 1.302 * −1.572 2.988 * −0.518 (1.943) (0.967) (0.493) (0.593) (0.513) (0.537) (1.083) (1.175) (0.963) IR 2.185 −2.236 * 0.251 −25.25 *** 1.895 * 2.334 ** −0.845 5.781 *** 1.194 ** (1.520) (0.824) (1.010) (6.205) (0.770) (0.679) (1.421) (0.920) (0.433) CPI 334.6 50.81 −130.8 22.74 −88.84 −50.87 −260.7 ** 296.1 5.838 (219.0) (103.9) (173.2) (207.4) (72.28) (89.84) (90.99) (167.7) (116.9) PNT 187.8 ** 38.84 63.03 236.3 183.5 ** 79.40 ** 371.6 *** 198.9 ** −80.23 (53.80) (31.81) (52.24) (117.5) (58.35) (24.03) (71.89) (66.22) (39.82) ddbt −65.80 −504.5 ** 474.0 *** 913.9 ** 88.89 15.87 315.4 * −10.08 −257.2 * (82.97) (148.6) (104.1) (286.6) (73.12) (16.91) (130.4) (100.7) (98.08) ToT 54.18 *** 163.2 *** −120.8 * −14.35 77.59 *** −66.51 * −3.128 −71.28 130.9 *** (8.782) (17.49) (54.49) (15.48) (9.742) (25.23) (11.68) (62.00) (32.32) dnfa 1.186 −0.884 * −1.504 * −0.444 0.000573 −0.0723 2.468 * −4.263 ** −0.151 (0.651) (0.427) (0.705) (0.844) (0.919) (0.557) (1.145) (1.531) (0.366) SI-INF 0.0518 0.00218 −0.00659 −0.0851 −0.164 0.0449 0.116 * 0.0451 −0.00463 (0.140) (0.0978) (0.0793) (0.0617) (0.110) (0.0505) (0.0515) (0.0981) (0.0980) SI-GDP −0.274 −0.0195 −0.0152 0.0387 0.00161 0.00729 −0.103 −0.0189 0.0958 (0.143) (0.0736) (0.0725) (0.0980) (0.145) (0.0635) (0.0846) (0.107) (0.0891) J. Risk Financial Manag. 2021,14, 512 37 of 40 Table A23. Cont. Australia China Canada Germany Japan Mexico Sweden Switzerland UK SI-CPI 0.108 −0.164 * 0.0260 −0.0987 −0.00406 −0.0585 −0.168 0.0286 0.215 (0.136) (0.0687) (0.0642) (0.112) (0.0811) (0.0482) (0.0995) (0.0877) (0.142) SI-IR −0.0614 −0.157 −0.142 0.0894 0.0224 −0.0166 0.0832 −0.0676 0.0502 (0.103) (0.117) (0.0842) (0.140) (0.117) (0.0758) (0.0582) (0.113) (0.107) SI-LOAN 0.109 −0.0442 0.0889 0.104 0.182 −0.0442 0.0969 0.268 * 0.149 (0.202) (0.0781) (0.0562) (0.104) (0.232) (0.0696) (0.0786) (0.103) (0.0931) SI-ATM 0.0309 0.0318 −0.196 ** −0.00328 0.115 −0.0618 −0.128 −0.189 0.528 *** (0.139) (0.0866) (0.0641) (0.108) (0.101) (0.0688) (0.116) (0.153) (0.141) SI-JO 0.0989 −0.105 0.112 −0.231 −0.133 0.116 −0.0889 0.00254 −0.235 (0.0759) (0.0535) (0.0694) (0.160) (0.0707) (0.115) (0.0642) (0.0665) (0.142) SI-SHOP 0.186 −0.109 −0.0940 0.324 * 0.0795 0.199 0.0310 −0.233 * 0.367 *** (0.112) (0.0791) (0.0549) (0.126) (0.122) (0.105) (0.0775) (0.105) (0.0812) SI-ExR −0.0449 −0.0566 −0.0143 −0.455 *** 0.0703 −0.121 * 0.0229 0.113 0.118 * (0.0901) (0.0768) (0.0418) (0.0683) (0.0834) (0.0530) (0.157) (0.121) (0.0505) Constant −501.3 * −116.9 299.6 −158.5 −88.08 124.5 −1.169 −312.9 −24.61 (215.2) (103.7) (188.1) (275.0) (69.16) (98.71) (101.3) (174.5) (111.8) Observations 52 52 48 48 45 51 38 52 52 Adjusted R20.772 0.874 0.858 0.900 0.816 0.791 0.916 0.890 0.884 F 11.15 21.90 17.72 25.86 12.49 12.12 24.85 25.29 23.78 Standard errors in parentheses. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Table A24. Panel Regression AM Model with ASSI for Countries. Australia China Canada Germany Japan Mexico Sweden Switzerland UK dmm −0.550 −0.312 −0.662 3.114 −0.359 −0.0662 0.186 −0.354 −1.292 (1.171) (0.646) (1.053) (3.642) (0.369) (0.345) (0.435) (1.245) (0.649) GDP 0.790 −2.022 * −0.0927 −0.544 −0.469 1.292 * −1.012 3.396 ** −0.557 (1.956) (0.978) (0.506) (0.620) (0.526) (0.542) (1.191) (1.100) (1.047) IR 2.538 −2.274 * −0.000386 −27.38 *** 1.846 * 2.513 ** −1.009 5.386 *** 1.178 * (1.596) (0.833) (1.081) (6.267) (0.792) (0.735) (1.421) (0.865) (0.467) CPI 346.4 97.69 −91.10 −37.03 −98.97 −74.40 −247.3 * 326.4 * 1.423 (220.9) (127.4) (183.9) (207.8) (77.75) (97.16) (91.32) (155.7) (126.1) PNT 188.7 ** 35.39 50.77 227.0 183.5 ** 76.20 ** 363.7 *** 137.5 * −80.10 (54.13) (32.53) (55.58) (115.6) (59.29) (24.70) (71.87) (65.76) (40.43) ddbt −55.00 −526.8 ** 491.3 *** 864.6 ** 86.81 16.20 332.7 * −19.01 −258.5 * (84.62) (153.8) (107.9) (283.5) (74.48) (17.06) (130.7) (93.30) (100.4) ToT 52.83 *** 161.2 *** −112.9 −17.05 77.62 *** −63.22 * 0.620 −72.48 129.9 *** (9.007) (17.89) (56.14) (15.31) (9.897) (25.92) (12.10) (57.40) (34.24) dnfa 1.094 −0.997 * −1.342 −0.393 0.0160 0.00496 1.824 −4.535 ** −0.143 (0.665) (0.465) (0.748) (0.830) (0.934) (0.574) (1.280) (1.422) (0.379) ASSI-INF 0.462 0.148 −0.406 −1.453 −3.079 1.127 2.438 * 0.0850 −0.0230 (2.765) (1.880) (1.571) (1.156) (2.115) (1.050) (0.996) (1.749) (1.989) ASSI-GDP −3.997 −0.700 −0.377 0.497 0.280 0.129 −0.934 0.192 1.348 (2.080) (1.252) (1.081) (1.392) (2.224) (0.927) (1.318) (1.446) (1.357) ASSI-CPI 3.022 −3.086 * 0.389 −1.015 −0.0229 −1.472 −2.189 0.282 3.912 (2.890) (1.315) (1.238) (2.163) (1.569) (1.069) (2.084) (1.543) (3.157) ASSI-IR −0.921 −2.167 −1.805 0.229 0.177 −0.507 1.136 −1.289 0.758 (1.568) (1.822) (1.386) (2.233) (1.845) (1.221) (0.888) (1.591) (1.643) ASSILOAN 1.771 −1.444 2.289 2.261 4.710 −1.485 2.639 3.639 3.286 (4.619) (1.895) (1.339) (2.273) (5.512) (1.734) (1.798) (2.310) (2.137) ASSI-ATM 1.677 0.925 −4.544 ** −1.708 2.934 −1.534 −4.643 −5.050 12.77 *** (3.597) (2.133) (1.603) (2.813) (2.515) (1.685) (3.131) (3.441) (3.535) ASSI-JO 2.055 −2.181 2.552 −4.374 −2.590 2.169 −1.453 0.895 −4.896 (1.540) (1.091) (1.473) (3.169) (1.466) (2.355) (1.322) (1.281) (3.305) ASSI-VC −1.495 0.757 −0.561 2.116 −0.761 −0.492 1.261 2.669 * 0.169 (1.935) (1.171) (0.807) (1.457) (1.918) (0.734) (1.145) (1.034) (1.627) J. Risk Financial Manag. 2021,14, 512 38 of 40 Table A24. Cont. Australia China Canada Germany Japan Mexico Sweden Switzerland UK ASSISHOP 3.347 −1.534 −1.461 7.476 ** 1.221 3.190 0.248 −4.054 * 6.198 *** (1.899) (1.395) (0.933) (2.503) (2.085) (1.779) (1.306) (1.619) (1.616) ASSI-ExR −0.251 −1.114 −0.0863 −9.938 *** 1.585 −2.293 −0.0998 2.190 2.700 * (2.294) (1.786) (1.021) (1.556) (1.931) (1.395) (3.613) (2.558) (1.180) Constant −513.6 * −161.0 264.1 −101.3 −76.95 150.5 −16.79 −282.8 −19.01 (217.1) (125.0) (196.5) (272.9) (75.66) (106.9) (101.8) (162.0) (125.6) Observations 52 52 48 48 45 51 38 52 52 Adjusted R20.769 0.872 0.856 0.903 0.810 0.787 0.917 0.906 0.880 F 10.44 20.35 16.47 25.45 11.44 11.28 23.79 28.23 21.81 Standard errors in parentheses. Note: Independent variable is the Real Exchange Rate; Source: Authors’ estimations. * p< 0.05, ** p< 0.01, *** p< 0.001. Notes 1Appendix Acontains the summary statistics of all variables in detail. 2 First we include NFAt in our regression models. However, NFAt creates a multi-collinearity problem with the highest VIF-factor of 8.81. Thus, we drop NFAtfrom our main regression models. Upon request, we provide the estimates including NFAt. 3 In order to check the robustness of our regression, we estimate the fourteen variants based on our row data as well ( ModelRowDat ). The results are largely robust. The Tables are in the online Appendix. 4Alternatively we conduct Kao’s cointegration test (Kao 1999). 5 All non-stationary variables must be transformed into stationary. This is done through a process called differencing: yt:= ln(xt+1)−ln(xt). 6We also check the standard Prais-Winsten test-statistics. 7Tables 5and A12 represent the output of the basic regression model. 8 The variable NFA and its significance was discussed by Branson and Henderson (1985), who argue that changes in net holdings of foreign asset directly affect a country’s currency. 9Results for the estimates with non-transformed data are reported in the Appendix C(Table A13). 10 Note, however, R-square can be rather meaningless (Blackwell 2005). 11 In addition, Tables A9 and A10 summarize the results of our first regression exercise. 12 All other country related impulse response functions are available upon request. References Althouse, Benjamin M., Yih Yng Ng, and Derek A. T. Cummings. 2011. Prediction of dengue incidence using search query surveillance. PLOS Neglected Tropical Diseases 5: 12–58. [CrossRef] Andersen, Torben G., Tim Bollerslev, Francis X. Diebold, and Clara Vega. 2003. Micro effects of macro announcements: Real-time price discovery in foreign exchange. American Economic Review 93: 38–62. [CrossRef] Askitas, Nikolaos, and Klaus F. Zimmermann. 2009. Google econometrics and unemployment forecasting. Applied Economics Quarterly 55: 107–20. [CrossRef] Baker, Scott R., Nicholas Bloom, and Steven J. Davis. 2016. Measuring Economic Policy Uncertainty. Quarterly Journal of Economics 131: 1593–636. [CrossRef] Balassa, Bela 1964. The purchasing-power partiy doctrine: A Reappraisal. Journal of Political Economy 72: 584–96. [CrossRef] Bank, Matthias, Martin Larch, and Georg Peter. 2011. Google search volume and its influence on liquidity and returns on german stocks. Financial Markets and Portfolio Management 25: 239–64. [CrossRef] Berkowitz, Jeremy, and Lorenzo Giorgianni. 2001. Long-horizon exchange rate predictability? Review of Economics and Statistics 83: 81–91. [CrossRef] BIS. 2016. Triennial Central Bank Survy. Basel: Bank for International Settlements. Blackwell, J. Lloyd. 2005. Estimation and testing of fixed-effect panel-data systems. Stata Journal 5: 202–7. [CrossRef] Branson, William H., and Dale W. Henderson. 1985. The specification and influence of asset markets. In Handbook of International Economics. Amsterdam: Elsevier, vol. 2, pp. 749–805. Breusch, T. S., and A. R. Pagan. 1979. A simple test for heteroscedasticity and random coefficient variation. Econometrica 47: 1287–94. [CrossRef] Breusch, T. S., and A. R. Pagan. 1980. The lagrange multiplier test and its application to model specification in econometrics. Review of Economic Studies 47: 239–53. [CrossRef] J. Risk Financial Manag. 2021,14, 512 39 of 40 Brissimis, Sophoeles N., Dimitris A. Sideris, and Fragiska K. Voumvaki. 2005. Testing long-run purchasing power partiy under exchange rate targeting. Journal of International Money and Finance 24: 959–81. [CrossRef] Buiter, Willem H., and Marcus Hay Miller. 1981. Monetary policy and international competitiveness. Oxford Economic Papers 33: 303–15. [CrossRef] Bulut, Levent 2017. Google trends and the forecasting performance of exchange rate models. Journal of Forecasting 37: 303–15. [CrossRef] Cassel, G. 1918. Abnormal deviations in international exchanges. Economic Journal 28: 413–15. [CrossRef] Chai, Daniel, Mengjia Dai, Philip Gharghori, and Barbara Hong. 2021. Internet search intensity and its relation with trading activity and stock returns International Review of Finance 30: 282–11. [CrossRef] Chatterjee, Samprit, and Ali S. Hadi. 1986. Influential observations, high leverage points, and outliers in linear regression. Statistical Science 1: 379–93. Chen, Joseph, Harrison Hong, and Jeremy C. Stein. 2001. Forecasting crashes: Trading volume, past returns, and conditional skewness in stock prices. Journal of Financial Economics 61: 345–81. [CrossRef] Cheung, Yin-Wong, Menzie D. Chinn, and Antonio Garcia Pascual. 2005. Empirical exchange rate models of the nineties: Are any fit to survive? Journal of International Money and Finance 24: 1150–75. [CrossRef] Choi, Hyunyoung, and Hal Varian. 2012. Predicting the present with google trends. Economic Record 88: 2–9. [CrossRef] Choi, In. 2001. Unit root tests for panel data. Journal of International Money and Finance 20: 249–72. [CrossRef] Clarida, Richard, and Jordi Gali. 1995. Sources of real exchange rate fluctuations: How important are nominal shocks? CanargieRochester Series on Public Policy 41: 1–56. Clark, Peter B., and Ronald MacDonald. 1998. Exchange rates and economic fundamentals: A methodological comparison of BEERs and FEERs. In Equilibrium Exchange Rates. Boston: Kluver, vol. 69, pp. 285–322. Cook, R. Dennis, and Sanford Weisberg. 1983. Diagnostics for heteroscedasticity in regression. Biometrika 70: 1–10. [CrossRef] Da, Zhi, Joseoh Engelberg, and Pengjie Gao. 2011. In search of attention. Journal of Finance 66: 1461–99. [CrossRef] Desjardins, Jeff. 2018. How Google Retains More Than 90% of Market Share. New York: Business Insider. Dornbusch, Rudiger. 1976a. Capital mobility. Flexible exchange rates and macroeconomic equilibrium. In Recent Issues in International Monetary Economics. Edited by Emil-Maria Claassen and Pascal Salin. Amsterdam: North Holland. Dornbusch, Rudiger. 1976b. Expectations and exchange rate dynamics. Journal of Political Economy 84: 1161–76. [CrossRef] Dornbusch, Rudiger, and Paul Krugman. 1976. Flexible exchange rates in the short run. Brookings Papers on Economic Activity 1976: 537–75. [CrossRef] Driskill, Robert A. 1981. Exchange-rate dynamics: An empirical investigation. Journal of Political Economy 89: 357–71. [CrossRef] Drukker, David M. 2003. Testing for serial correlation in linear panel-data models. Stata Journal 3: 169–77. [CrossRef] Engel, Charles, and Kenneth D. West. 2005. Exchange rates and fundamentals. Journal of Political Economy 113: 485–517. [CrossRef] Ettredge, Michael, John Gerdes, and Gilbert Karuga. 2005. Using web-based search data to predit macroeconomic statistics. Communications of the ACM 48: 87–92. [CrossRef] Ewards, Sebastian. 1989. Real Exchange Rates, Devaluation, and Adjustment. Cambridge: MIT Press. Fischer, Stanley. 1977. Long-term contracts, rational expectations, and the optimal money supply rule. Journal of Political Economy 85: 191–205. [CrossRef] Frankel, Jeffery A. 1979. On the mark: A theory of floating exchange rates based on real interest differentials. American Economic Review 69: 610–22. Ginsberg, Jeremy, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, and Larry Brilliant. 2009. Detecting influenza epidemics using search engine query data. Nature 457: 1012–14. [CrossRef] [PubMed] Goel, Sharad, Jake M. Hofman, Sebastien Lahaie, David M. Pennock, and Duncan J. Watts. 2010. Predicting Consumer Behavior with Web Search. Proc. Natl. Acad. Sci. USA 41: 17486–90. [CrossRef] Google. 2018. How Trends Data Is Adjusted. Mountain View: Google. Gray, Jo Anna. 1976. Wage indexation: A macroeconomic approach. Journal of Monetary Economics 2: 221–35. [CrossRef] Helft, Miguel 2009. Google’s new tool is meant for marketers. New York Times, August 6. Hooper, Peter, and John Morton. 1982. Fluctuations in the dollar: A model of nominal and real exchange rate determination. Journal of International Money and Finance 1: 39–56. [CrossRef] Im, Kyung So, M. Hashem Pesaran, and Yongcheol Shin. 2003. Testing for unit roots in heterogeneous panels. Journal of Econometrics 115: 53–74. [CrossRef] IMF. 2018. Macroeconomic and Financial Data. Available online: https://www.imf.org/en/Data (accessed on 15 April 2021). Jun, Seung-Pyo, Tae-Eung Sung, and Hyun-Woo Park. 2017. Forecasting by analogy using the web search traffic. Technological Forecasting and Social Change 115: 37–51. [CrossRef] Kao, Chihwa. 1999. Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics 90: 1–44. [CrossRef] Kouwenberg, Roy, Agnieszka Markiewicz, Ralph Verhoeks, and Remco C. J. Zwinkels. 2017. Model uncertainty and exchange rate forecasting. Journal of Financial and Quantitative Analysis 52: 341–63. [CrossRef] Lazer, David, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. The parable of google flu: Traps in big data analysis. Science 343: 1203–05. [CrossRef] J. Risk Financial Manag. 2021,14, 512 40 of 40 Liang, Hong. 1998. Real Exchange Rate Volatility: Does the Nominla Exchagne Raate Regime Matter? Working Paper. Washington, DC: International Monetary Fund, pp. 1–38. Mark, Nelson C. 1995. Exchange rates and fundamentals: Evidence on long-horizon predictability. American Economic Review 85: 201–18. Mark, Nelson C., and Donggyu Sul. 2001. Nominal exchange rates and monetary fundamentals: Evidence from a small post-Bretton woods panel. Journal of International Economics 53: 29–52. [CrossRef] Meese, Richard A., and Kenneth Rogoff. 1983. Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics 14: 3–24. [CrossRef] Molodtsova, Tanya, and David H. Papell. 2009. Out-of-sample exchange rate predictability with Taylor rule fundamentals. Journal of International Economics 77: 167–80. [CrossRef] Mundell, Robert A. 1963. Capital mobility and stabilization policy under fixed and flexible exchange rates. Canadian Journal of Economics and Political Science 29: 475–85. [CrossRef] Mundell, Robert. 1968. International Economics. New York: Macmillan. Naccarato, Alessia, Stefano Falorsi, Silvia Loriga, and Andrea Pierini. 2018. Combining official and Google Trends data to forecast the Italian youth unemplyment rate. Technological Forecasting and Social Change 130: 114–22. [CrossRef] OECD. 2018. OECD Main Economic Indicators (Mei). Paris: Organization for Economic Cooperation and Development. Rogoff, Kenneth. 1995. The purchasing power parity puzzle. Journal of Economic Literature 34: 647–68. Rogoff, Kenneth. 2002. Dornbusch’s Overshooting Model after Twenty-Five Years. IMF Staff Papers 01-39. Washington, DC: International Monetary Fund. Rossi, Babara. 2013, December. Exchange rate predictability. Journal of Economic Literature 51: 1063–19. [CrossRef] Samuelson, Paul. 1964. Theoretical notes on trade problems. The Review of Economics Statistics 46: 145–54. [CrossRef] Sarno, Lucio, and Maik Schmeling. 2014. Which fundamentals drive exchange rates? A cross-sectional perspective. Journal of Money, Credit and Banking 46: 267–92. [CrossRef] Shim, Soyeon, Mary Ann Eastlick, Sherry L. Lotz, and Patricia Warrington. 2001. An online prepurchase intentions model: The role of intention to search. Journal of Retail 77: 397–416. [CrossRef] Takeda, Fumiko, and Takumi Wakao. 2013. Googe search intensity and its relationship with returns and trading volume of Japanese stocks. Pacific-Basin Finance Journal 27: 1–18. [CrossRef] Vaughan, Liwen, and Esterban Romero-Frias. 2013. Web search volume as a predictor of academic fame: An exploration of google trends. Journal of the Association for Information Science and Technology 65: 707–20. [CrossRef] Vaughan, Liwen, and Yue Chen. 2015. Data mining from web search queries: A comparison of google trends and baidu index. Journal of the Associaton for Information Science and Technology 66: 13–22. [CrossRef] Vlastakis, Nikolaos, and Raphael N. Markellos. 2012. Information demand and stock market volatility. Journal of Banking and Finance 36: 1808–21. [CrossRef]