Macroeconomic Policies Interaction & the Symmetry of Financial Markets’ Responses

Nasir, Muhammad Ali; Soliman, Alaa M.; Yago, Milton; Wu, Junjie Article Macroeconomic Policies Interaction & the Symmetry of Financial Markets’ Responses Journal of Central Banking Theory and Practice Provided in Cooperation with: Central Bank of Montenegro, Podgorica Suggested Citation: Nasir, Muhammad Ali; Soliman, Alaa M.; Yago, Milton; Wu, Junjie (2016) : Macroeconomic Policies Interaction & the Symmetry of Financial Markets’ Responses, Journal of Central Banking Theory and Practice, ISSN 2336-9205, De Gruyter Open, Warsaw, Vol. 5, Iss. 1, pp. 53-69, https://doi.org/10.1515/jcbtp-2016-0003 This Version is available at: https://hdl.handle.net/10419/217585 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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-nc-nd/3.0/ Macroeconomic Policies Interaction & the Symmetry of Financial Markets’ Responses 53 * Faculty of Business & Law, Leeds Beckett University, LS1 3HS, UK E-mail (corresponding author): [email protected] Journal of Central Banking Theory and Practice, 2016, 1, pp. 53-69 Received: 27 July 2015; accepted: 18 August 2015 UDK: 336.11(410) DOI: 10.1515/jcbtp-2016-0003 Muhammad Ali Nasir, Alaa M. Soliman, Milton Yago, Junjie Wu * Macroeconomic Policies Interaction & the Symmetry of Financial Markets’ Responses Abstract: This concise study analyses the symmetry of financial markets` responses to macroeconomic policy interaction in the United Kingdom. Employing the Vector Auto-regression (VAR) model on monthly data of the British financial sector and macroeconomic policies from January 1985 to August 2008, this study found that the equity and sovereign debt markets showed identical symmetry in response to macroeconomic policy interaction. Keywords: - Financial Markets, Macroeconomic Policy Interaction, Symmetry of financial markets responses JEL Classification: E44, E61, G12, G18 1. Introduction Macroeconomic policies are the most vital tool for the achievement of economic objectives, whether it is monetary policy to control the availability and cost of monetary and credit or fiscal policy to accomplish the government’s financial obligation. There is a fairly broad consensus among academics and policy circles that the macroeconomic policies have significant effects which are not limited to real economy and a number of studies, for instance, Bredin et al. (2005), Ardagna (2009) and Arnold et al. (2010) that reported significant impact of macroeconomic policies on the financial sector. Concomitantly, this study focuses on analysing the symmetry of financial market i.e. sovereign debt and equity markets` responses to macroeconomic policy interactions. Journal of Central Banking Theory and Practice 54 The first and legitimate question could concern why particular segments of the financial market on which this study is focused are only Stock (equity) and Bond (sovereign debt) markets? A simple answer and reason of this choice could be the limited scope of this treatise as we are unable to consider all segments of financial markets. However, it is particularly because of the Wealth Effects of the stock and bond markets which are very important for the economy (See Malikane and Semmler (2008), Funke et al. (2010) and Airaudo (2011). In addition, Broome and Morley (2004) also found that stock prices are a significant indicator of future financial outlook. The third and final reason of this choice specifying the term financial stability, for which we followed the footsteps of Foot (2003) and Khorasgani (2010), can be defined as price oscillations of financial assets and generality of financial markets and institutions. Also, a unique characteristic of this study considers the symmetry of financial market responses to policy interaction rather than to solely focus on the response of the financial market. Perhaps it is due to the fact that an important and rationale aspect of analysing the responses of the stock and bond markets to macroeconomic policies is the symmetry of their responses. Particularly if the policy target is a specific financial market, for instance the stock market, it would be equally important to consider the response of the bond market to any policy decision aiming at the former, perhaps the logic of doing so becomes more explicit if we refer to earlier cited studies which considered stock as well as bond markets important for economic stability. Hence, potential wealth effects raised by positive performance of one financial market due to policy measures may be offset by adverse outcome in the other if the same policy stance is not appropriate for the latter. On the symmetry of stock and bond market responses, Gulley and Sultan (2003) showed that bond and stock markets exhibit a positive response to expansionary monetary stance and a negative response to contractionary monetary stance. On the other hand, Tavares and Valkanov (2003) showed that contractionary fiscal policy negatively affects stock and bond markets. These two studies by Gulley and Sultan (2003) on monetary policy and the study by Tavares and Valkanov (2003) on fiscal policy documented a homogeneous response of both markets to a monetary policy decision. However, there are two caveats and limitations in these two studies. Firstly, both of the studies were limited to a single policy, i.e. either monetary or fiscal policy, although there is strong support for analysing monetary and fiscal policies together (see e.g. Porqueras and Alva, 2010; Sims, 2011). Secondly, on the dynamic relationship between stock and bond markets, Paulson (2013) argues that the association between stock and bond markets in US has been dynamic in the last few decades and although it has remained mostly positive, there have been some periods of negative relationship. Macroeconomic Policies Interaction & the Symmetry of Financial Markets’ Responses 55 Intention to consider policy interaction also stemmed from the role of fiscal in complimenting monetary policy efforts, in this regard we acknowledge the earlier seminal work and argument by Sargent and Wallace (1981) that the fiscal policy should complement monetary policy for price stability. On the policy combination analysis, Dixit and Lambertini (2001; 2003) strongly argued for the importance of policy coordination. In this regard, it is worth mentioning the argument by Leeper that “Analysing one policy is like dancing a tango solo: it’s a lot easier, but it is incomplete and ultimately unfulfilling” (1993, p.3). The interdependence between monetary and fiscal policies is also documented by Zubairy (2009) and Davig and Leeper (2011) as monetary stance may counteract fiscal policy impact and vice versa. Moreover, the bond market is equally important as the stock market (see e.g. Campbell, 1995; Johnson et al., 2003). Hence, it is vital to consider the symmetry of financial market responses to policy interaction because a policy combination positively affecting one market might have negative effects on the other. On this aspect, the nearest we can get is the recent seminal work by Nasir and Soliman (2014) on the implications of policy combination for financial sector; however, despite the earlier cited rationale, the symmetry of the response of financial markets to policy decision could not gain any attention. Therefore, the main theme of this study is to analyse the symmetry or whether the responses of both markets are homogeneous or heterogeneous. To serve this purpose, we would analyse the simultaneous responses of stock and bond markets to macroeconomic policy interaction. 2. Theoretical framework The theoretical model has representative household with the income constraint utility and preferences. The Euler equation would be as follows: (1) With the objective of household utility (U) maximization from streams of consumption (c) and leisure (l); E0 is the expectations operator (rational expectations) based on agent observing all current macroeconomic variables; ß (0; 1) is the discount factor, while u is instantaneous utility function and ct and lt are levels of consumption and leisure at time t. The household portfolio constitutes two types of assets i.e. Stock(s) and Government Bonds (b). The wealth of household is generated by two sources which are financial wealth (A) i.e. the income from financial assets (stocks and bonds) and the non-financial wealth (H) which is the labour income. Therefore, the total financial wealth would be as follow: Journal of Central Banking Theory and Practice 56 (2) The theoretical model follows the work of Altissimo et al (2005) and Nasir and Soliman (2014) where the intertemporal consumption of the household also depends on financial wealth and takes the following form: (3) Where C denotes consumption, A is the financial wealth constituting stock and bonds ( ), H represents human wealth and Y denotes value of expected labour income net of taxes. The proportionality coefficient (mpc) measures the marginal propensity to consume out of financial wealth and income, respectively. Equation (3) can be transformed and written as: (4) Equation (4) implies that wealth elasticity of consumption (ew ) depends on mpcw as well as wealth consumption ratio of each component j. The national income (Y) is presented as follows Y = C + I + G + X - M (5) Whereas I is investment, G is Government spending, and (X-M) is the balance of trade, it is obvious that C consumption is the vital part of national income, hence the wealth (A) effects of financial assets (stocks and bonds) have considerable effects on consumption. Suppose that the household portfolio constitutes two classes of financial assets, stocks (s) and bonds (b) which are affected by the macroeconomic policies, therefore for monetary policy: (6) Macroeconomic Policies Interaction & the Symmetry of Financial Markets’ Responses 57 where Ae t is the expected financial wealth from financial assets (stocks and bonds) and r is the rates of interest (monetary policy). For fiscal policy we follow the specification by Ardagna (2009) for US stock market and fiscal policy: (7) where Financialijt is the stock market and Fiscalijt is fiscal stance in time ti. However, we include the bond market and monetary policy as specified in Equation (3). Hence our model would have the following representation: (8) where A is the financial wealth of the household constituting stocks and bonds, Fiscalijt is the fiscal policy stance, and Monetaryijt is the monetary policy stance in time t. Having access to two policies means there could be four possible policy combinations1. 3. Empirical Framework Employing a VAR model, we used reasonably high frequency (monthly) data, considering the fact that the financial markets exhibit a fairly volatile pattern and better estimates are obtained by high frequency data (Hautsch, 2011). The Bank of England’s official Bank Rate is used as a proxy for monetary policy stance. It is the official instrument used by the Bank of England to achieve its objective of price stability2. However, in the context of the existing literature, Bernanke and Blinder (1992) while analysing the various transmission mechanism of monetary policy in their study argued that the federal funds rates are good measures of monetary policy, in specific to the UK the Bank Rate is equivalent of the federal funds rates. The Public Sector Net Cash Requirements formally known as Public 1 (a) Expansionary Fiscal-Expansionary Monetary, (b) Expansionary Fiscal-Contractionary Monetary, (c) Contractionary Fiscal-Contractionary Monetary(d) Contractionary Fiscal-Expansionary Monetary. 2 The Bank’s monetary policy objective is to deliver price stability – low inflation and it targets inflation at 2% rate of consumer price index, details of monetary policy framework available at: http://www.bankofengland. co.uk/monetarypolicy/Pages/framework/framework.aspx Journal of Central Banking Theory and Practice 58 Sector Borrowing Requirements (PSBR) as a percentage of GDP is used to represent fiscal policy. It represents the monthly fiscal deficit. On the aspect of fiscal stance, we could have either used debt or deficit to income (GDP) ratios, however we chose the latter and the rationale of doing so is supported by the Muscatelli et al. (2004) as they declared deficit as better proxy for fiscal policy stance than the debt. The bond market is proxied by the monthly averages of the real yield on UK Government bonds (Gilts) which is Inverse of bond prices as a proxy for bond markets response; this is due to the importance of Yield for economic agents (Campbell, 1995). The Yield on bonds is also important for the government as it represents its borrowing cost; it also reflects the confidence of market participants and investor in bonds and, importantly, returns on investment. The monthly average values of the FTSE-100 index are used as a proxy for the stock market. Most of the studies acknowledged earlier used stock prices and indices. In particular in the context of subject analysis there are studies such as Funke et al. (2010) and most recent evidence by Airaudo (2011) which reported the Wealth Effect of stock market and used stock indices to represent stock market. On practical side, the choice of FTSE-100 is due to the reason that it represents the major companies with more than 80% of the total capitalisation of the entire London Stock Exchange (L.S.E), moreover it is the official index maintained by the FTSE group which is a joint venture of Financial Times and the London Stock Exchange. Data is obtained from the Office of National Statistics, FTSE Group, and the Bank of England. The data spans the period from January 1985 to August 2008 which, in our view, provides sufficient time horizon and avoids the extraordinary events of the Global Financial Crises (2008) which led to massive fluctuations in stock and bond markets (Nasir and Soliman, 2014). Considering the fact that the dataset includes multiple variables and a time series, the employed Vector Auto Regressive (VAR) model is widely used for such datasets (see Basu and Michailidis 2013). The VAR is used with interrelated time series and for analysing the dynamic impact of random disturbances on the system of variables.In the VAR model, the endogenous and explanatory variables interact simultaneously, hence there is an extended information set, which makes it a more adequate presentation of key aspects of an economic system than a standard multiple regression model (Pecican, 2010). The VAR approach sidesteps the need for structural modelling by modelling every endogenous variable in the system as a function of the lagged values of all endogenous variables in the system. Since only lagged values of the endogenous variables appear on the right-hand side of each equation there is no issue of simultaneity. Importantly, the assumption that any disturbances are not serially correlated is not restrictive because any serial correlation could be absorbed by adding more laggedy’s. As such, using VAR, any serial correlation of errors does not become an issue. Macroeconomic Policies Interaction & the Symmetry of Financial Markets’ Responses 59 To start with we performed the ARCH analysis in order to check if the volatility of financial markets persists, results shown in Table 1. Table 1: Auto Regressive Conditional Hetroskadicity (ARCH) Test Lnstock F-statistic 1.746 Prob. F(3,277) 0.158 Obs*R-squared 5.216 Prob. Chi-Square(S) 0.157 LnBond Fstatistic 1.980 Prob. F(3,277) 0.117 Obs*R-squared 5.898 Prob. Chi-Square(3) 0.117 *ARCH (Marquardt) test The ARCH effects were not found at 5% level of significance. Hence we proceeded to our VAR analysis. The Unit root tests using the ADF method were performed to satisfy the assumption that our data series is stationary. The results are presented in Table 2. The test statistics were greater than the critical values; hence the null of unit root was rejected after taking the first difference at 5% as well as 1% significance level. ADF test results imply that all data series are 1st difference stationary or I (1) variables. Table 2: Augmented Dickey-Fuller Unit Root Test Variable ADF Test Stat* 1 % level** 5% level P-value At level l(o) LnBond -0.402 -3.453 -2.871 0.905 LnStock -1.876 -3.990 -3.425 0.664 Fiscal -1.995 -3.992 -3.426 0.600 LnMonetary -2.238 -3.991 -3.425 0.466 1st Difference I(1) LnBond -16.775 -3.991 -3.426 0.000 LnStock -16.455 -3.991 -3.426 0.000 Fiscal -4.252 -3.993 -3.427 0.004 LnMonetary -8.424 -3.991 -3.425 0.000 Residual -16.723 -3.991 -3.426 0.000 *ADF test statistics of LnBond, Fiscal and Monetary Policy. **Critical value at 1% level of significance. ***Critical value at 5% level of significance. Journal of Central Banking Theory and Practice 60 Symmetry of financial markets’ responses The Vector Auto regression (VAR) model adopts the following form: Yt(LnStock)=Constant+ßYt-1(LnStock)+ßYt-2(LnStock)...+ßXt-1(fiscal)+ ßXt-2 (fiscal)..ßXt-1(LnMonetary)+ßXt-2(LnMonetary)…ßYt-1(LnBond)+ Yt-2(LnBond) (9) Yt(LnBond)=Constant+ßYt-1(LnBond)+ßYt-2(LnBond)...+ßXt-1(fiscal)+ ßXt-2(fiscal)…..ßXt-1(LnMonetary)+ ßXt-2(LnMonetary).. ßYt-1(LnStock)+ ßYt-2 (LnStock)..+ et et ˜ N (0, σ2), (10) Where Yt and Xt are (n x 1) vector of time series endogenous variables, ßi are the (n x n) coefficient matrixes and et is the (n x 1) white noise or unobservable vector process with assumptions of no autocorrelation and independent distribution, i.e. et ˜ N (0, σ2). To find the most appropriate number of lags to be included in the model, the optimal lag selection tests were performed as shown in Table 3. Table 3: Optimal Lag Selection Lags LR FPE AIC SC HQ 11 78.082 0.000 -5.8 -3.408 -4.84 12 124. 010* 2.33e-08* -6.240* -3.635 -5.194* 13 10.431 0.000 -6.17 -3.353 -5.039 *Significance level (5%), LR: sequential modified LR test statistic, FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion, HQ: Hannan-Quinn information criterion. Our results presented in the table above should that the unanimous and Twelve lags were suggested as optimal by all criteria, hence twelve lags were considered. Thereafter, the Johansen Co-integration tests (Table 4) were performed to find if the variables are co-integrated and if there is a long-run association among the variables. In case there is the co-integration relationship, we employ the Vector Error Correction (VEC) model which is a restricted form of the Vector Autoregression (VAR) model. 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