Mapping of sovereign risks in small island economies: An application of contingent claim approach to Fiji

Jain, Devendra Kumar; Rup Singh; Patel, Arvind Article Mapping of sovereign risks in small island economies: An application of contingent claim approach to Fiji Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Jain, Devendra Kumar; Rup Singh; Patel, Arvind (2020) : Mapping of sovereign risks in small island economies: An application of contingent claim approach to Fiji, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 8, Iss. 1, pp. 1-17, https://doi.org/10.1080/23322039.2020.1727158 This Version is available at: https://hdl.handle.net/10419/245281 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. 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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/4.0/ Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/oaef20 Mapping of sovereign risks in small island economies: An application of contingent claim approach to fiji Devendra Kumar Jain, Rup Singh & Arvind Patel | To cite this article: Devendra Kumar Jain, Rup Singh & Arvind Patel | (2020) Mapping of sovereign risks in small island economies: An application of contingent claim approach to fiji, Cogent Economics & Finance, 8:1, 1727158, DOI: 10.1080/23322039.2020.1727158 To link to this article: https://doi.org/10.1080/23322039.2020.1727158 © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 17 Feb 2020. Submit your article to this journal Article views: 611 View related articles View Crossmark data FINANCIAL ECONOMICS | RESEARCH ARTICLE Mapping of sovereign risks in small island economies: An application of contingent claim approach to fiji Devendra Kumar Jain 1 *, Rup Singh 2 and Arvind Patel 3 Abstract: While a decline in the market value of sovereign assets (below a benchmark level of liabilities) can trigger sovereign distress/default risk, volatility in sovereign assets can increase the risk premium on domestic debt and credit spread on external debt. These can escalate the probability of debt default. Therefore, measuring the probability and distance to debt distress associated with sovereign positions is important for assessing the macro-financial risks of an aggregate economy. This paper presents an application of the Contingent Claim Approach (CCA) for measuring the implied asset value and its volatility for the case of Fiji. The CCA captures non-linear changes to sovereign assets and liabilities that are hardly captured by other macroeconomic variables. Our consistent empirical findings indicate no sovereign debt distress for Fiji. Unavailability of partial data on the certain composition of sovereign assets and liabilities is a limitation, but our results are consistent and useful guide to debt policy in Fiji. It is also useful for Devendra Kumar Jain ABOUT THE AUTHORS Devendra Jain Senior Lecturer in Finance, Westminster International University of Tashkent. Had a distinguished banking career specializing in financial market, banking treasury, currency trading, financial risk management and AML Compliance. Research interests: MacroFinancial Risks, Foreign Currency Exposure and Macro-Financial Risk Indicators. Rup Singh Acting Head and Senior Lecturer in Economics, Faculty of Business and Economics (FBE) University of South Pacific (Suva) Fiji. Former Central Banker. Research interests: Economic Growth, Monetary Policy and Applied Econometrics. Arvind Patel Acting Dean and Professor of Accounting and Finance, Faculty of Business and Economics (FBE), University of South Pacific (Suva) Fiji. Distinguished career as an academic and administrator. Research interest: Corporate Governance, Fraud, Ethics and Auditing. Professional memberships to Fiji Institute of Accountants, CPA Australia, American Accounting Association, Accounting Association of Australia and New Zealand and Institute of Internal Auditors. PUBLIC INTEREST STATEMENT Sovereign liabilities (Government and Monetary Authority combined) are promised payments on the debts against the uncertain market value of sovereign assets. A decline in the value of assets below a certain level of promised payments can trigger sovereign distress or default. This paper employs the Contingent Claim Approach to assess the sustainability of Government debt in a small economy (Fiji) by analyzing the distance to debt distress and the probability of default. It finds that despite the internal-external shocks, Fiji has low levels of debt stress and between 2%-3% probability of default. Jain et al., Cogent Economics & Finance (2020), 8: 1727158 https://doi.org/10.1080/23322039.2020.1727158 © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Received: 29 November 2019 Accepted: 02 February 2020 *Corresponding author: Devendra Kumar Jain, Department of Finance & Accounting, Westminster International University in Tashkent, Tashkent, Uzbekistan E-mail: [email protected] Reviewing editor: Evan Lau, Department of Economics, Universiti Malaysia Sarawak, Kuching, Malaysia Additional information is available at the end of the article Page 1 of 17 future research on debt sustainability in other similar smaller developing economies. Subjects: Asian Economics; Macroeconomics; International Economics Keywords: Sovereign risk; contingent claim approach; macro-financial risk analysis; south pacific; Fiji Jel: F31; F32; E52; F4 1. Introduction Traditional analysis of sovereign debt based on vulnerability indices and macro fundamentals do not adequately capture the non-linear changes to sovereign assets and liabilities. Macro-financial risk assessment based on Contingent Claim Analysis (CCA) adequately supplements the overall macro-financial risk assessment of an aggregate economy because CCA assesses the evaluation of uncertainty in the future value of sovereign assets related to sovereign liabilities, where option pricing is used to construct risk-adjusted balance sheet factoring market information. The core of CCA is to consider sovereign liabilities as contingent claims 1 on sovereign assets whose valuations are uncertain. This uncertainty is calculated by a probability distribution along a time horizon. It derives the implied value of sovereign assets and their volatility from the market value of liabilities to establish sovereign sustainability. Therefore, it is a method of embedding financial market developments with accounting information to deal with market imperfections. The CCA applied to a national balance sheet can effectively analyze the value of assets and liabilities based on the uncertain value of assets, risk transmission between alternative sectors and risk exposure subdued within the national balance sheet. The volatility in sovereign assets increases the risk premium on domestic debt and credit spread on international bonds. Therefore, this paper discusses various aspects of sovereign risks of Fiji to help manage foreign debt and attract sustainable capital inflows to support national economic development. The South Pacific region consists of about a dozen small island states with all having “population penalty 2 ”syndrome unhealthy for scale-effects. Additionally, there are other challenges (dependence on foreign aid and foreign-capital, capacity constraints, and lack of adequate resources, primitive technology, small domestic market, and greater vulnerability to climate-related events). Further, a small domestic/external friction can create a big impact on the macro-financial health of such economies. As such, assessing the macro-financial risks are important for such economies who have limited capacity to mitigate large shocks to their incomes and welfare. The proposed work in this paper is a pioneering attempt to study CCA using the Balance Sheet Framework for Fiji. Although it is primarily based on Gray, Merton, and Bodie (2009) method, with few modifications being introduced to contextualize the analysis. These are (i) equity (base money and other local currency debt) is used as one-composite variable (ii) historical spot-rate is used instead of the forward rate due to lack of data. This paper is organized as follows. The following briefly introduces the Pacific context and the Fijian economy. The next section surveys prominent works on the CCA with important models based on this are discussed in section 4. The conceptual framework of the paper is presented in Section 5. Model specification, variable definitions, and the empirical results for Fiji’s sovereign sector balance sheet analysis are in sections 7and 8, respectively. The final section 8concludes. 2. Macro-financials of the Fiji economy Fiji is an emerging, open economy. Since 2008, Fiji has recorded an average economic growth rate of 3% (IMF, 2019-October) 3 while the growth of per capita income has been modest to average just over 1% per annum. Fiji has sustained many shocks such as trade boost (1989), devaluation (1988, 1998 & 2009), political crises (1987, 2000 & 2006) and fuel price hikes (various years) and tropical cyclones & flash-flooding (once every year). The impact of these events has been Jain et al., Cogent Economics & Finance (2020), 8: 1727158 https://doi.org/10.1080/23322039.2020.1727158 Page 2 of 17 significant. So far, the implications have not been analyzed using the BSA framework for Fiji or for the Pacific region. There are several reasons for selecting Fiji. First, it is perhaps the best economy of the region which is fast emerging to be an upper-middle-income country. Second, most financial crises have impacted countries where financial markets were not well developed. Third, Fiji remains dependent on external debt (government) and domestic debt (private) financing. Finally, since financial risks are not evenly distributed across economic sectors, the Fijian economy is a good case study for small and vulnerable economies. However, before we go into details, it is important to review the past monetary and fiscal policy responses to various shocks. In the period 1970–2007, the first major reform (after independence in 1970) was the pegging of the Fiji dollar with USD in 1974. Earlier it was pegged with the British Pound. The Fiji Dollar was pegged against a basket of currencies (based on major trading partners) in 1975. The monetary function was managed by then the Central Monetary Authority, until the formation of Reserve Bank of Fiji (RBF) in 1984. Initially, the RBF had been using direct quantitative policy, until 1988 (Jayaraman & Choong, 2008) when the first major devaluation of the Fiji Dollar (by 33%) was announced in 1988. Under a fixed exchange rate regime, monetary authority usually practices devaluation of the currency to absorb serious shocks that may impact the level of foreign reserves. Subsequently, large-scale financial sector reforms were undertaken in 1989 in order to ease lending and promote financial market activity. Financial liberalization also promoted the RBF to start conducting open market operations using its own bond (RBF Notes). The PIR (Policy Indicative Rate) and MLR (Minimum Lending Rate) were introduced and managed through monetary policy. At the onset of the Asian Financial Crisis, the RBF had to devaluate the Fiji Dollar by 20%. Subsequent years from 2000 to 2005, Fiji has recorded growth due to fiscal stimulus and growth in credit to private sectors. This has led to increases in current account deficit promoting unprecedented monetary policy measures (increase in PIR and SRD (Statutory Reserve Deposit)) to counter widening current account deficit in 2005 and 2006. With a new Government in power in 2006, no major tightening of monetary policy was announced—RBF continued to accommodate an expansionary fiscal stance. The effects of the 2008/9 global financial crisis were visible in exports, tourism earnings, remittances, FDI, exchange rate, and the rate of economic growth 4 (Reddy, 2008). Fiji’s growth rate averaged 0.8% during the 2006–2010 period mainly due to high oil and food prices, domestic political ill-will, and climate-related events (Wainiquolo, 2013). Capital controls were introduced to maintain foreign reserves without any change to the interest rate. The current account deficit was high with imports being as much as 70% of GDP in 2008. A large budget deficit was also financed through an internationally raising Government bond of USD150 Million around that time (in 2006). With weakening trade performance, the Fiji dollar was further devalued in April 2009 to avoid a currency crisis. Foreign exchange reserves and currency positions were stabilized after the new allocation of SDR quota of $ 168 million from IMF in 2009. By the end of 2009, the economy rebounded with an annual growth rate being over 3%. This was largely driven by tourism receipts and government and private sector spending. Fiji continued to broaden its economic base and experimented with bold reforms. Its debt ratio went over 50% but the sovereign rating remained stable and positive. 5 Core inflation remained low compared to its regional peers and absolute Government debt declined but remained relatively higher the comparators. 6 Foreign direct investment has not been robust but the economy is growing above the regional average rate of 2.7% and the world average of 2.4%. However, the short term (2019–2021) outlook for Fiji not very promising (RBF, 2019), although economic fundamental remains intact and promising for the medium to long term. 3. Survey of related literature The application of CCA to measure sovereign risks was initiated by Merton (1973), see Gray and Malone (2008). The CCA framework uses the basic structure of a conventional balance sheet and adds market prices and uncertainty as key inputs to derive forward-looking risk indicators (Gapen, Gray, Lim, & Xiao, 2008). A summary of the key developments in CCA can be documented as Jain et al., Cogent Economics & Finance (2020), 8: 1727158 https://doi.org/10.1080/23322039.2020.1727158 Page 3 of 17 follows: Black & Scholes (1973) 7 and Merton (1973) are considered as the pioneers in asset pricing and modeling credit risk at firm-level. Their combined (BSM) model forms the basis of the modern CCA and is now applied at national level balance sheets. Gray et al. (2009) present a simple framework to analyze four key economic sectors 8 while Gapen et al. (2008) developed a CCA framework for analyzing sovereign balance sheets and credit risk indicators. This work compared the risk indicators calculated using the CCA with actual market data (risk-neutral credit spread, actual credit default swap (CDS) spread and EMBI (emerging market bond index) spread) and found the model had high predictive power. Gray, Merton, and Bodie (2007) regress CCA indicators (distance to distress) on sovereign credit default spreads and suggest that these indicators are helpful to investors in finding the right kind of sovereign bond investment and possible arbitrage opportunities. Keller, Kunzel, and Souto (2007) further confirm that the risk indicators developed under CCA are highly correlated with market data (CDS and EMPI). 9 Thus, the application of CCA has been varied and useful. There is a good discussion in the literature on the predictability of Merton’s BSM model. Bharath and Shumway (2004) draw attention to the complex computational procedures of solving the two unknowns 10 in BSM. Another important feature of BSM is that it relies on the market price of equity, assuming a perfectly competitive market where all the information is available to all participants. There are instances in the past where high equity prices indicated a low probability of default (PD) for a firm, whereas, these high equity prices were manipulated through insider trading or other market imperfections. The authors also argue that the predictive power of other models (e.g. the KMV-Merton model, see below), is superior due to its functional form. They subsequently propose a Naïve model that takes similar inputs and captures approximates the probability of default. While the results are similar, Duffie, Saita, and Wang (2007) argue in favor of the KMV-Merton Model. As explained in equations from 15 to 17, this model incorporates equity price and market information to estimate the probability of default. Subsequently, Campbell, Hilscher, and Szilagyi (2008) extend and combine the KMV-Model with Hazard models (Cox and Oakes (1984)) to improve on its predictive power. Chava and Jarrow (2004) label the Hazard model as superior to other credit risk models. Lu (2008) has detailed default forecasting models with their critical properties. Afik, Arad, and Galil (2016) critically analyze the KMV-Merton Model on three parameters such as the default barrier, expected return on the firm’s assets, and the asset return volatility of the firm. The authors question the predictable power of historical equity return for the forward-looking expected return. They suggest minimizing negative returns by using procedures of the Capital Asset Pricing Model. 4. Analytical framework of contingent claim analysis The original Black & Scholes (1973) model was modeled for a European option that can be exercised only at the expiry of the option. The equity of a firm is a residual claim on the assets or cash flows of the company. The debts are considered as senior claims. A call option holder expects the price will go up and a put option holder’s expectations are otherwise. If the current value of a firm’s equity is greater than total debts and external liabilities, the equity holders will get a payoff. The option pricing for Call are: C¼S0Nd 1 ðÞKertNd 2 ðÞ (1) P¼KertNd2 ðÞS0Nd1 ðÞ (2) d1¼ln S0=kðÞþrþσ2=2  T  σffiffiffiffiffi T p(3) d2¼d1σffiffiffi T p(4) where C is the price of Call, P the price of Put option, S 0 is stock price at time t = 0, N(d 1 )is cumulative standard probability distribution, K is strike price of the option, r is risk-free rate of Jain et al., Cogent Economics & Finance (2020), 8: 1727158 https://doi.org/10.1080/23322039.2020.1727158 Page 4 of 17 interest (continuously compounded), σ 2 is a variance and T is the time period of the option. When S 0 is large, the value of d 1 and d 2 also follows it and the value of N(d 1 ) and N(d 2 ) are very close to 1. To measure risk in quantifiable terms, Option Greeks are being calculated and monitored by option traders as hedging strategies. These are also calculated to determine an option’s sensitivity to price changes. They also capture the non-linearity of price changes and their possible impactions on the option portfolio. Option Greeks are: (1) δ(delta) = measures the sensitivity of an option to the actual price fluctuation of the underlying stock. (2) V(vega) = measures the changes in the volatility of the underlying stock. (3) Γ(gamma) = tracks the sensitivity of the delta of an option (4) Θ(theta) = captures the sensitivity of time decay of an option. Merton (1973) while clarifying the Black–Scholes Model (BSM), extended the option pricing analogy to corporate debt and liabilities in general. Merton Model is also called the default forecasting model. It has been used for pricing other financial instruments as well. He prescribes equity as a call option on the assets of a firm and book value of debt as strike price over a certain period of time. This basic model has several assumptions. We denote this as the BSM model below and is expressed as: At¼EtA;tðÞþDtA;tðÞ (5) The first assumption is that a public trading firm has a simple capital structure in the form of debt and equity. Equity (E) is a function A (asset) at a time (t) and likewise debt (D). Dt¼BerðTtÞPt(6) It also assumes that the entity under the study has a single and homogenous class of bond and maturity with a promise to pay at maturity. A(t) is the total market value of assets at any time. It consists of a total market value of equity (E(t)) and risky debts (D(t)) maturing at time T. The equity and risky debts derive their values from uncertain assets. The risky debt D(t) is defined as defaultfree debt (Be —r(T-t) ) minus expected loss due to default or debt guarantee (P(t)). Thus, as long as the assets are higher in value than debt, equity is realized. If A t< D t E t <0, This implies that there is no payout to equity holders, full claims of creditors no default may occur. The probability of default (PD) may take place until the end period. Equity holders will wait till the end of the period to walk away or declare a default. One of the assumptions of the BSM model is that the value of a firm or assets follow a stochastic process. 11 In mathematics, it is also called geometric Brownian motion (GBM). The notation under GBM is as follows: dA ¼μAAdt þσAAdW (7) where A is the total value of the firm, dA reflects the changes in A, µ A is expected rate of the return on the asset (continuously compounded), σ A is the volatility and dW is a standard Weiner process. 12 Based on these assumptions, the two important components (liabilities and the probability distribution) of debt are important to estimate the value of equity. E¼AN d1 ðÞDerTNd 2 ðÞ (8) where N(.) is cumulative standard normal distribution function, r is a risk-free interest rate, T is time to maturity and D is the face value of debt of the firm. The d 1 and d 2 have been defined in Equations (3) and (4). The most important assumption of this model is the relationship of equity Jain et al., Cogent Economics & Finance (2020), 8: 1727158 https://doi.org/10.1080/23322039.2020.1727158 Page 5 of 17 volatility (σ E ) and asset volatility (σ A ). As E is a function of A and T, the relationship between equity volatility and asset volatility is derived through Ito’lemma process 13 summarized as follows: σE¼A E @E @AσA(9) This is explained in the original BS model as: σE¼A ENd 1 ðÞσA(10) We have Equations (8) and (10) with two unknowns (A and σA). In the BS and BSM models, values of these two variables are unobservable and need to be solved using either of the two options below: (a) Iterative approach (b) Simultaneous equations of E and σE As mentioned earlier, asset price follows the Brownian motion. Using Weiner’s and Ito’lemma process, Equation (10) is derived in the model. For solving the system of equations, we can infer the initial value of A and σA as follows: At¼EtþDt(11) σA¼σEE EþD(12) σE is usually calculated using historical returns data from equity prices assuming a forecasting period of 1 year (T = 1). After having the values of all variables in the model, distance to default (DD) can be written as follows: DD ¼ln A D þr:5σA2  T σApT(13) The distance to default (DD) is log-normal and usually found in between A and D. The probability of default (PD) can be estimated from DD as follows: PD ¼NDDðÞ (14) where normally distributed value of assets falls below distance to default. If it is calculated for a Call Option, it will not be exercised because of zero-intrinsic value. A commercial extension of the Merton Model was introduced by Kealhofer, McQuown & Vasicek as KMV model (Crosbie & Bohn 2003). This model redefines the concept of D (face value of debt). It is argued that not all debt mature in 1 year and default barrier should be short-term debt maturity during 1 year and approximately half of long-term debt can be included in the calculation of D. The variable D is re-introduced as default barrier. This has been empirically applied in several studies including Campbell et al. (2008) for example. Another important point to note in Equation (13) is that it uses (r) to calculate the distance to default (DD) and the probability of default (PD). It may not be correct as the value of a firm may actually follow the expected return on a firm’s assets instead of risk-free interest rate (r). Thus, Equation (13) can be rewritten as follows: DDkmv ¼ln A D þμA:5σA2  T σApT(15) The term µA is expected return on assets included to replace r (risk-free interest rate). In Equation (7) while defining the Brownian motion, the variable µA is used as a drift rate. There are few methods in the literature related to the calculation of the drift rate. Afik et al. (2016) illustrate three methods to calculate this term but the most popular method is the use of capital asset price Jain et al., Cogent Economics & Finance (2020), 8: 1727158 https://doi.org/10.1080/23322039.2020.1727158 Page 6 of 17 model (CAPM). It is expected that an asset must generate a return above the risk-free return. If the β A (beta coefficient) is equal to 1, it indicates the market return on asset is equal to risk-free return. Under CAPM, µA is stated as follows: μA¼rþβARP (16) where β A is a beta coefficient of asset and RP is a risk premium on the market asset. In some empirical testing, if the stock is traded in exchange, the exchange index such as the S&P 500 is used to calculate the beta coefficient. The estimation of β A is: βA¼βEσA=σE ðÞ (17) The β A represents the slope of the line for σ A (excess return on asset) and σ E (excess return on equity). The high beta coefficient indicates high volatility and vice versa. The KMV-Merton model is more accurate in empirical studies as it incorporates equity price and market information to estimate default probability. The Moody Credit Rating Agency took over the KMV model in 2002 and named it as Moody’s KMV model with certain propriety modifications in the original model. It is an implementation of the Vasicek-Kealhofer model (VK model). The VK model can evaluate barrier options and perpetual options. 14 Moody’s KMV does not use a cumulative normal distribution to calculate DD and PD. It uses its own historical database to calculate the empirical distribution. This model is more dynamic as it allows us to account for classes and maturities of debt. This model is also mapped to the actual default database by a software called Credit Monitor. In this model, PD is defined as the expected default frequency (EDF). The conceptual grounding of EDF is provided by Nazeran and Dwyer (2015). The forward-looking asset volatility has been added for mapping the EDF. This model describes default point (DP) as short-term debt plus half of the long-term debts. The distance to default (DD) is the distance between the market value of the firm’s asset and DP. Bharath and Shumway (2004) suggest a simplified version of the KMV-Merton model also. They called this the Naïve Predictor which avoids the simultaneous determination of parameters. It assumes that debt value is equal to the accounting value of debt. It captures the same information as KMV-Merton model to approximate the functional form of the Merton Model (BSM model). The model estimates the volatility of debt (assuming a link between the risks of debt with the firm’s equity) as follows: σDnaive ¼0:05 þ0:25 σEnaiv (18) where 0.05 represents term structure volatility, 0.25 times historical equity volatility (σE naive )to calculate debt volatility or the volatility linked to default risk. Asset volatility can be estimated as follows: σAnaive ¼ðE=EþDðÞðÞσEnaive þðD=EþDðÞðÞσDnaiv (19) It is assumed that expected returns on a firm’s assets are equal to the firm’s actual return of previous year µ naive =r it −1 where µ is expected return on the asset in naïve predictor and r it-1 is the asset return of the firm of the previous year. The model estimates the distance to default as follows: DDNaive ¼ln½EþDðÞ=DÞþ rit10:5σA2naive  T σAnaivepT(20) DD naive is the distance to default in this model. The authors claim that the Naïve model is easy and does not require any complicated iterative procedure but still captures all information of the KMVMerton model (Bharath & Shumway, 2004). This model is also not free from criticism. For example, Jain et al., Cogent Economics & Finance (2020), 8: 1727158 https://doi.org/10.1080/23322039.2020.1727158 Page 7 of 17 Haque, T., Knight, D., & Jayasuriya, D. (2012). Capacity constraints and public financial management in small pacific Island. The World Bank, Policy Research Working Paper,6297,1–36. Hull, J. (2003). Options, futures and other derivatives (Fifth ed.). New Jersey: Prentice Hall. IMF. (2019, October). World economic outlook. Washignton DC: Author. Jayaraman, T., & Choong, C. (2008). Exchange market pressure in a small pacific Island country: A study of Fiji. International Journal of Social Economics, 35(12), 985–1004. doi:10.1108/ 03068290810911507 Keller, C., Kunzel, P., & Souto, M. (2007). Measuring sovereign risk in Turkey: An application of the contingent claims approach. IMF, wp 07/233. pp. 1–27. Lu, Y. (2008, June 19). Default forecasting in KMV. Retrieved from www.core.ac.uk:https://core.ac.uk/ download/pdf/97111.pdf Merton, R. C. (1973). Theory of rational option pricing. The Bell Journal of Economics and Management Science,4 (1), 141–183. doi:10.2307/3003143 MOE. (2018, December). Government of Fiji: Debt Report. Suva: Ministry of Economy, Government of Fiji. Narayan, P. (2003). Macroeconomic impact of natural disasters on a small Island economy. Applied Economic Letters,10(11), 721–723. doi:10.1080/ 1350485032000133372 Nazeran, P., & Dwyer, D. (2015). Credit risk modeling of public firms: EDF9.NewYork,NY:Moody’s Analytics. RBF. Quarterly review. (2019). June, Dec 2018, Dec 2016. Suva: Reserve Bank of Fiji. Reddy, S. (2008). Impact of the global crisis on Fiji’s economy. BIS Review, (138/2008). pp. 1–4. Wainiquolo, I. (2013). Towards a macroeconomic model for Fiji. Reserve Bank of Fiji, Economics Group wp 2013/02. pp. 1–15. Jain et al., Cogent Economics & Finance (2020), 8: 1727158 https://doi.org/10.1080/23322039.2020.1727158 Page 14 of 17 Appendix A Figure A1. Real GDP Growth (Annual Percentage Change). Source: IMF: World economic outlook (October 2019) Figure A2. General government debt (As percentage of GDP). Source: IMF: World economic outlook (October 2019) Jain et al., Cogent Economics & Finance (2020), 8: 1727158 https://doi.org/10.1080/23322039.2020.1727158 Page 15 of 17 Appendix B Table 4 List of Variables Used Asov Implied Value of sovereign assets σAsov Implied Volatility of sovereign assets σAsov $ Implied Volatility of sovereign assets in USD DB Distress Barrier DB $ Distress Barrier in USD DCD Domestic Currency Debt DCD $ Domestic Currency Debt in USD BM Base Money BM $ Base Money in USD FCD Foreign Currency Debts DL Domestic Liabilities A Assets σA Volatility of assets DCL $ Domestic Currency Liabilities in USD DCL Domestic Currency Liabilities in local currency σDCL $ Volatility of Domestic Currency Liabilities in USD DCD Domestic Currency Debt DCD $ Domestic Currency Debt in USD σDCD $ Volatility of Domestic Currency Debt in USD FR Forward Rate σFR Volatility of Forward Rate R,f,t foreign interest rate up to time DDsov Distance to Default or Distress d1 Conditional probability d2 Probability adjusted value Note: σis volatility, ρindicates correlation and $ is used to express value in foreign currency Jain et al., Cogent Economics & Finance (2020), 8: 1727158 https://doi.org/10.1080/23322039.2020.1727158 Page 16 of 17 © 2020 The Author(s). This open access article is distributed undera Creative Commons Attribution(CC-BY) 4.0 license. You are free to: Share —copy and redistribute the material in any medium or format. Adapt —remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution —You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Cogent Economics & Finance (ISSN: 2332-2039) is published by Cogent OA, part of Taylor & Francis Group. 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