## _wp10255 - References ## Source details **Canonical URL:** [_wp10255 - References](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2010/_wp10255.pdf) ## Other formats - [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2010/_wp10255.pdf.md) - [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2010/_wp10255.pdf.json) --- ### Context and motivation - Currency stability is a central element of macroeconomic policy in most emerging economies. - Measures of the foreign exchange risk premium (interest parity condition, forward differential) have been used since Hansen and Hodrick (1980) and Fama (1984) to quantify downside and upside pressures on a currency. - Empirical literature (e.g., Engel (1996), Lustig et al. (2008)) finds that risk premia in currency markets are large and time-varying. - Pegged exchange rate regimes have received less attention historically because of lower apparent uncertainty about future exchange rates; the global financial crisis increased volatility in forward contract prices even for pegged emerging market currencies. ### Aim and scope of the paper - Objective: analyze the relationship between macroeconomic fundamentals and foreign exchange risk in Saudi Arabia and the United Arab Emirates. - Both countries have pegged their currencies to the U.S. dollar for more than a decade; forward contracts traded on these currencies have fluctuated around the peg, with increased volatility over the last two years of the sample driven by the global financial crisis, U.S. monetary policy, and oil market developments. - Prior work acknowledged the role of U.S. developments but lacked rigorous quantitative assessment; this paper addresses that gap. ### Methodology and contribution - Employs theoretically-grounded and frontier empirical techniques. - Section 2 reviews approaches and introduces the stochastic discount factor (SDF) methodology used to identify foreign exchange risk premia with observable, theoretically-motivated factors. ### Theoretical background: approaches to modeling foreign exchange risk - Literature strands: - Two-country asset pricing models with strong theoretical restrictions; often yield implausible deep-parameter estimates and rejected over-identifying restrictions. - Pure time-series approaches (e.g., ARCH, ARCH-in-mean): capture regularities but lack theoretical interpretation linking predictability to risk premia. - Semi-structural SDF approach: balances structure to identify risk premia while remaining flexible; paper follows this approach with observable factors. - Key relations and definitions preserved: - Covered interest parity (implicit): R_t = R*_t F_t / S_t. - No-arbitrage SDF relation: E_t[M_{t+1} R_{t+1}] = 1. - Foreign exchange risk premium: rp_t = S_{t+1} - F_t, time-varying and dependent on M_{t+1}. - Under joint log-normality, log-form relations (equations (5)-(7)) link expected excess returns to conditional variances and covariances with the SDF. - Implication: the FX risk premium is a function of its time-varying covariance with the SDF and uncertainty about future exchange rates. ### Stochastic discount factor (SDF) modeling choices - Two principal strategies: - Unobservable factor models (Kalman filter): good fit but ad-hoc economic interpretation. - Observable factor models from general equilibrium (C-CAPM and generalizations): interpret SDF as intertemporal marginal rate of substitution. - C-CAPM (power utility) specifications preserved: - m_{t+1} = θ - σ Δc_{t+1} (real form, equation (8)). - Nominal form including inflation π_{t+1}: m_{t+1} = θ - σ Δc_{t+1} - π_{t+1} (equation (9)). - Nominal C-CAPM implies risk premium driven by consumption risk; allows separation of nominal and real determinants (equation (10)). - Generalized multi-factor SDF: - m_{t+1} = α + Σ_{i=1}^K β_i z_{i,t+1} (equation (11)), relaxing C-CAPM parameter restrictions. - No-arbitrage excess return expression given by equation (12), with β_i (i =1,2,...K+1) estimated. ### Econometric methodology - Aim: model joint distribution of excess return and macroeconomic factors so conditional mean of excess return satisfies no-arbitrage (equation (12)). - Multivariate GARCH-in-Mean specification to allow time-varying variance-covariance: - y_{t+1} = μ + Φ z_{t+1} + vech{H_{t+1}}'γ + ε_{t+1}, ε_{t+1} | I_t ~ N(0, H_{t+1}) (equation (13); notation preserved). - First row of Φ restricted to the vector of β_i’s to satisfy no-arbitrage; other rows set to zero. - Conditional variance specified using BEKK formulation (Engle and Kroner, 1995) to ensure positive definiteness and flexibility. - Estimation challenges: heavy parameterization, computational and convergence difficulties, and extreme volatility in returns requiring careful modeling to avoid variance-instability. ### Data - Countries: Saudi Arabia and the United Arab Emirates (pegged to U.S. dollar). - Sample: monthly data June 1995-February 2010; 177 observations per series. - Data sources reported: IMF International Financial Statistics, U.S. Bureau of Economic Analysis, and Datastream. - Variables and construction: - Forward and spot exchange rates vis-à-vis U.S. dollar used to estimate log risk premium rp_t for 1-, 2-, 3-, and 6-month maturities. - SDF determinants: U.S. personal consumption expenditures (consumption growth) and U.S. consumer price index (inflation). - Oil prices included to capture oil-export dependence of GCC economies. - Stylized features: - Risk premium is time-varying in both positive and negative directions across maturities; fluctuations comparable across maturities. - Volatility particularly large during 2007-09: - Risk premium declined by more than 30 basis points and turned negative in 2008. - At end-2008 the risk premium jumped by more than 60 basis points and turned positive. - Towards end of sample, risk premium reduced to near zero. - Descriptive statistics (Table 1 exact means): - Saudi Arabia forward differential means: - 1 month: 0.0398 - 2 months: 0.0262 - 3 months: 0.0225 - 6 months: 0.0186 - United Arab Emirates forward differential means: - 1 month: 0.0094 - 2 months: 0.0028 - 3 months: 0.0012 - 6 months: 0.0001 - U.S. consumption growth rates mean: 0.4103 - U.S. inflation mean: 0.2037 - Oil price changes mean: 0.8418 - Note: forward differentials exhibit excess skewness and kurtosis; oil prices show higher variation than inflation and consumption growth. ### Estimation results — empirical findings (summary) - Model: multivariate BEKK GARCH-in-Mean estimated for Saudi Arabia and United Arab Emirates; results reported for 1-, 2-, 3-, and 6-month risk premia (Tables 2 and 3). - Fit: - Root mean square error indicates better fit for higher-maturity risk premia (smaller root mean square error). - Reported root mean square errors: - Saudi Arabia: 1m = 0.8568, 2m = 0.5294, 3m = 0.3771, 6m = 0.2655 - United Arab Emirates: 1m = 0.4300, 2m = 0.5669, 3m = 0.8658, 6m = 0.8177 - On average the multivariate model improves fit relative to a univariate GARCH(1,1) (comparison RMSEs reported in footnote in the source). ### Determinants of FX risk premium (empirical evidence) - Intercepts (μ_i) in mean equation: - All positive and significant except for the two-month risk premium equation for the United Arab Emirates, consistent with an average market expectation of depreciation (the “peso problem”). - In-mean β_i coefficients (macroeconomic factors): - Both nominal (U.S. CPI inflation) and real (U.S. consumption growth) factors significantly impact the risk premium across maturities. - Implication: by pegging to the U.S. dollar, Saudi Arabia and UAE effectively import U.S. monetary policy; FX risks are closely connected to U.S. macroeconomic developments. - Oil prices: - Oil price changes are an important determinant of the GCC FX risk premium. - Oil coefficient magnitude is lower relative to inflation and consumption coefficients, possibly due to higher volatility and conditional covariance of oil prices. - Model fit to actual series: - GARCH-in-Mean model fits actual risk premium relatively well and captures the 2007-09 volatility episode. - Fit is slightly better for longer maturities, suggesting higher explanatory power of macro determinants in the long run. ### Diagnostics and specification tests - Post-estimation tests performed: - Breusch-Godfrey LM test for serial correlation on standardized residuals. - ARCH tests for conditional heteroskedasticity by regressing residual-variance dependent variable on lags (up to eight lags). - Reported p-values (Table 4) indicate: - Null of no serial correlation cannot be rejected at the 5% confidence level in most cases. - Null of no remaining ARCH effects in residuals cannot be rejected for most residuals in at least one of the specifications (four or eight lags). - Specific p-values are reported in Table 4 for Saudi Arabia and UAE across maturities and the three factors (inflation, consumption, oil). ### Conclusions and policy-relevant implications - Main empirical conclusions: - U.S. macroeconomic variables and international oil prices are important determinants of foreign exchange risk in the GCC (Saudi Arabia and United Arab Emirates) under pegged exchange rate regimes. - Findings hold for 1-, 2-, 3-, and 6-month risk premia. - Both nominal (U.S. CPI inflation) and real (U.S. consumption growth) factors play important roles, consistent with C-CAPM predictions and evidence from other economies. - International oil prices are relevant for pricing FX risk in the GCC given oil-revenue dependence. - The multivariate BEKK GARCH-in-Mean model fits the actual risk premium well (including the 2007-09 episode), implying usefulness for timely monitoring of FX risks in the GCC. - Additional interpretation: - Inclusion of U.S. macro determinants and oil prices substantially improves multivariate model fit relative to univariate specifications. - After accounting for macro fundamentals, the risk premium generally remains positive (significant positive intercepts), consistent with the “peso problem” explanation: a small placed probability on exchange rate devaluation can generate a positive risk premium even under a peg. - Policy-relevant insight: - For pegged economies importing U.S. monetary policy, monitoring U.S. macroeconomic developments and international oil price dynamics is critical for understanding and managing foreign exchange risk. *Source — content based on the supplied IMF PDF content unit _wp10255 (sections II–V, tables and figures as provided).* ### References ............................................................................................................. ### _wp10255 - References ### Context and motivation - Currency stability has been an important part of the macroeconomic policies of most emerging economies worldwide. - Since the early work of Hansen and Hodrick (1980) and Fama (1984), various measures of the foreign exchange risk premium, such as the interest parity condition and forward differential, have been used in the literature to quantify downside and upside pressures on a currency. - Engel (1996) and Lustig et al. (2008) show that risk premia in currency markets are large and time-varying. - Most of the literature studies countries with a flexible exchange rate regime; analysis of foreign exchange risk in countries pegging their currencies has been given less attention because uncertainty about the future exchange rate is much lower when the currency is pegged to an external anchor. - The global financial crisis has changed this perception, as indicated by the increased volatility in prices of forward contracts traded on pegged emerging market currencies. ### Aim and scope of the paper - The aim of this paper is to analyze the relationship between macroeconomic fundamentals and foreign exchange risk in Saudi Arabia and the United Arab Emirates. - Both countries have pegged their currencies to the U.S. dollar for more than a decade. - During this period, forward contracts traded on currencies of these countries have fluctuated around the peg. - Triggered by the global financial crisis, U.S. monetary policy and developments in the oil market, these fluctuations have become much more volatile during the last two years. - While the importance of macroeconomic developments in the U.S. for foreign exchange risks in these countries has been widely acknowledged, the quantitative assessment of these factors has not been performed in a rigorous manner in previous work. ### Methodology and contribution - The study attempts to carry out a quantitative analysis based on theoretical foundations and frontier empirical techniques. - Section 2 reviews the existing approaches for studying foreign exchange risk and introduces the stochastic discount factor (SDF) methodology. *Source: _wp10255 - References .............................................................................................................* ### Section 3 provides the econometric specification of the model and data description. Section 4 ### _wp10255 - Section 3 provides the econometric specification of the model and data description. Section 4 ### Theoretical background: approaches to modeling foreign exchange risk - Literature categorized into three strands: - Two-country asset pricing models with strong theoretical restrictions; common issues include incredible estimates of deep structural parameters and rejection of over-identifying restrictions. - Pure time-series approaches (e.g., ARCH and ARCH-in-mean): capture empirical regularities but lack theoretical interpretation of predictable components as risk premium. - Semi-structural stochastic discount factor (SDF) approach: places sufficient structure to identify a foreign exchange risk premium while remaining largely unconstrained. The paper follows the SDF approach with observable, theoretically-motivated factors to explain foreign exchange risk in selected GCC countries. - Key asset-pricing relations and definitions: - Covered interest parity: R_t = R*_t F_t / S_t (implicit in text as relationship (1)). - Generalized no-arbitrage SDF relation: E_t[M_{t+1} R_{t+1}] = 1 (equation (2)). - Foreign exchange risk premium definition: rp_t = S_{t+1} - F_t (time-varying and depends on M_{t+1}). - Under joint log-normality, log-form relations produce: - m_{t+1} relations (equations (5)-(7)) linking expected excess returns to conditional variances and covariances with the SDF. - Implication: the foreign exchange risk premium is a function of its time-varying covariance with the SDF and of uncertainty about future exchange rates. ### Stochastic discount factor (SDF) modeling choices - Two principal SDF modeling strategies: - Unobservable factor models (Kalman filter extraction): good fit, ad-hoc economic interpretation of factors. - Observable factor models from general equilibrium (C-CAPM and generalizations): interpret SDF as intertemporal marginal rate of substitution. - C-CAPM specification (power utility): - m_{t+1} = θ - σ Δc_{t+1} (equation (8)) in real terms; nominal form includes inflation π_{t+1}: m_{t+1} = θ - σ Δc_{t+1} - π_{t+1} (equation (9)). - Nominal C-CAPM implies risk premium driven by consumption risk; allows distinction between nominal and real determinants (equation (10)). - Generalized multi-factor SDF: - m_{t+1} = α + Σ_{i=1}^K β_i z_{i,t+1} (equation (11)), relaxes C-CAPM parameter restrictions. - No-arbitrage expression for excess return becomes equation (12), where β_i (i =1,2,...K+1) are coefficients to be estimated. ### Econometric methodology - Objective: model joint distribution of excess return and macroeconomic factors so conditional mean of excess return satisfies no-arbitrage condition (equation (12)). - Multivariate GARCH-in-Mean model specified to allow time-varying variance-covariance matrix: - General form: y_{t+1} = μ + Φ z_{t+1} + vech{H_{t+1}}'γ + ε_{t+1}, ε_{t+1} | I_t ~ N(0, H_{t+1}) (equation (13); notation preserved). - First row of Φ restricted to vector of β_i’s to satisfy no-arbitrage; other rows restricted to zero. - Conditional variance specification: - BEKK formulation (Engle and Kroner, 1995) used to guarantee positive definiteness of H_t and retain flexibility. - Estimation challenges: - Heavy parameterization → computational and convergence difficulties. - Extreme volatility in returns → potential instability in variance process; requires careful modeling. ### Data - Countries: Saudi Arabia and the United Arab Emirates (both peg to U.S. dollar). - Sample: monthly data covering the period June 1995-February 2010; the data set contains 177 observations for each series. - Data sources: IMF International Financial Statistics, U.S. Bureau of Economic Analysis, and Datastream (as reported). - Variables and construction: - Forward and spot exchange rates vis-à-vis U.S. dollar used to estimate logarithm of risk premium rp_t for 1-, 2-, 3-, and 6-month maturities. - SDF determinants modeled with U.S. macroeconomic variables: U.S. personal consumption expenditures (consumption growth) and U.S. consumer price index (inflation). - Oil prices added to SDF specification to account for oil-export dependence of GCC economies. - Stylized data features: - Risk premium appears time-varying in both positive and negative directions across maturities; fluctuations comparable across maturities. - Volatility of risk premium particularly large during the 2007-09 period: - Risk premium declined by more than 30 basis points and turned negative in 2008. - At end-2008 the risk premium jumped by more than 60 basis points and turned positive. - Towards the end of sample, risk premium reduced to near zero levels. - Descriptive statistics (Table 1; numbers preserved exactly): - Saudi Arabia forward differential means: - 1 month: 0.0398 - 2 months: 0.0262 - 3 months: 0.0225 - 6 months: 0.0186 - United Arab Emirates forward differential means: - 1 month: 0.0094 - 2 months: 0.0028 - 3 months: 0.0012 - 6 months: 0.0001 - U.S. consumption growth rates mean: 0.4103 - U.S. inflation mean: 0.2037 - Oil price changes mean: 0.8418 - Note: forward differentials exhibit excess skewness and kurtosis; oil prices show higher variation than inflation and consumption growth. ### Estimation results — empirical findings (summary) - Model: multivariate BEKK GARCH-in-Mean estimated for Saudi Arabia and United Arab Emirates; results reported for 1-, 2-, 3-, and 6-month risk premia (Tables 2 and 3). - Fit: - Root mean square error indicates better fit for higher-maturity risk premia (smaller root mean square error). - Reported root mean square errors (from Tables 2 and 3): - Saudi Arabia: 1m = 0.8568, 2m = 0.5294, 3m = 0.3771, 6m = 0.2655 - United Arab Emirates: 1m = 0.4300, 2m = 0.5669, 3m = 0.8658, 6m = 0.8177 - On average the multivariate model improves fit relative to a univariate GARCH(1,1) (root mean squared errors reported in footnote for comparison). ### Determinants of FX risk premium (empirical evidence) - Intercepts (μ_i) in mean equation associated with excess returns: - All positive and significant except for the two-month risk premium equation for the United Arab Emirates, indicating average market expectation of depreciation (the “peso problem”). - “In-mean” β_i coefficients (importance of macroeconomic factors): - Both nominal (U.S. CPI inflation) and real (U.S. consumption growth) factors have a significant impact on the risk premium across maturities. - Implication: by pegging to the U.S. dollar, Saudi Arabia and UAE import U.S. monetary policy; FX risks in these countries are closely connected to U.S. macroeconomic developments. - Oil prices: - Oil price changes are an important determinant of the GCC FX risk premium. - Magnitude of the oil coefficient lower relative to inflation and consumption coefficients, possibly due to higher volatility (and conditional covariance) of oil prices. - Model fit to actual series: - Figures 6-7 show actual vs estimated risk premium; the GARCH-in-Mean model fits actual risk premium relatively well, successfully capturing 2007-09 volatility. - Fit slightly better for longer maturities, indicating higher explanatory power of macro determinants in the long run. ### Diagnostics and specification tests - Post-estimation tests performed: - Breusch-Godfrey LM test for serial correlation on standardized residuals. - ARCH tests for conditional heteroskedasticity by regressing residual-variance dependent variable on lags (up to eight lags). - Reported p-values (Table 4) indicate: - Overall validity: null hypothesis of no serial correlation cannot be rejected at the 5% confidence level in most cases. - Hypothesis of no remaining ARCH effects in residuals cannot be rejected for most residuals in at least one of the specifications (four or eight lags). - Specific p-values are presented in Table 4 for Saudi Arabia and UAE across 1-, 2-, 3-, and 6-month forward premium and three factors (inflation, consumption, oil); the paper reports these p-values as the basis for diagnostics. ### Conclusions and implications - Main empirical conclusions: - U.S. macroeconomic variables and international oil prices are important determinants of foreign exchange risk in the GCC (Saudi Arabia and United Arab Emirates) under pegged exchange rate regimes. - This result holds for risk premia computed for 1 month, 2 months, 3 months, and 6 months maturities. - Both nominal (U.S. CPI inflation) and real (U.S. consumption growth) factors play important roles in explaining variability in the FX risk premium; findings align with C-CAPM predictions and evidence from other advanced and emerging economies. - International oil prices are relevant for pricing FX risk in the GCC given historical oil-revenue dependence. - The multivariate BEKK GARCH-in-Mean model fits the actual risk premium well (including 2007-09 episode), implying usefulness for timely monitoring of FX risks in the GCC. - Additional interpretation: - Inclusion of U.S. macro determinants and oil prices substantially improves multivariate model fit relative to univariate specifications. - After accounting for macro fundamentals, the risk premium generally remains positive (significant positive intercepts), consistent with the “peso problem” explanation: a small placed probability on exchange rate devaluation can generate a positive risk premium even under a peg. - Policy-relevant insight: - For pegged economies importing U.S. monetary policy, monitoring U.S. macroeconomic developments and international oil price dynamics is critical for understanding and managing foreign exchange risk. *Italic: Source — content based on the supplied IMF PDF content unit _wp10255 (sections II–V, tables and figures as provided).* ### REFERENCES ### _wp10255 - REFERENCES ### Affine term structure / Term structure models - Backus, D., Foresi, S. and C. Telmer (2001), “Affine Term Structure Models and the Forward Premium Anomaly”, Journal of Finance 56: 279–304. - Benati, L. (2006), “Affine Term Structure Models for the Foreign Exchange Risk Premium”, Bank of England Working Paper No. 291. - Panigirtzouglou, N. (2001), “Using Affine Models of the Term Structure to Estimate Risk Premia”, EFMA 2001 Lugano Meetings. - Engle, R., Lilien, D., and R. Robbins (1987), “Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M model”, Econometrica 55: 391–408. ### Forward rates / Forward premium / Forward discount anomaly - Backus, D., Gregory, A. and C. Telmer (1993), “Accounting for Forward Rates in Markets for Foreign Currency”, Journal of Finance 48(5): 1887–908. - Engel, C. (1996), “The Forward Discount Anomaly and the Risk Premium: A Survey of Recent Evidence”, Journal of Empirical Finance 3: 123–91. - Fama, Eugene F. (1984), “Forward and Spot Exchange Rates,” Journal of Monetary Economics, 14, 319–38. - Hansen, L.P. and Hodrick, R.J. (1980), “Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis”, Journal of Political Economy 88, 829–53. - Krasker, W. (1980), “The “Peso Problem” in Testing the Efficiency of Forward Exchange Markets,” Journal of Monetary Economics 6: 269-76. - Kaminski, G. and R. Peruga (1990), “Can a Time-Varying Risk Premium Explain Excess Returns in the Forward Market for Foreign Exchange?”, Journal of International Economics 28: 47–70. - Hodrick, R. (1987), The Empirical Evidence on the Efficiency of Forward and Futures Foreign Exchange Markets, University of Chicago Press, Chicago, IL. ### Foreign exchange risk premium; risk and return in FX markets - Bekaert, G. (1996), “The Time-Variation of Risk and Return in Foreign Exchange Markets: A General Equilibrium Perspective”, Review of Financial Studies 9: 427–70. - Bekaert, G., Hodrick, R. and D. Marshall (1997), “The Implications of First-Order Risk Aversion for Asset Market Risk Premiums”, Journal of Monetary Economics 40: 3–39. - Hollifield, B., and A. Yaron (2000), “The Foreign Exchange Risk Premium: Real and Nominal Factors”, Carnegie Mellon University, Graduate School of Industrial Administration, GSIA Working Papers: 2001-E13. - Mark, N. (1988), “Time-Varying Betas and Risk Premia in the Pricing of Forward Foreign Exchange Contracts”, Journal of Financial Economics 22: 335–54. - Lustig, H. and A. Verdelhan (2007), “The Cross-Section of Foreign Currency Risk Premia and Consumption Growth Risk”, American Economic Review 97 (1): 89–117. - Domowitz, I. and C. Hakkio (1985), “Conditional Variance and the Risk Premium in the Foreign Exchange Market”, Journal of International Economics 19: 47–66. - Kaminski, G. and R. Peruga (1990), “Can a Time-Varying Risk Premium Explain Excess Returns in the Forward Market for Foreign Exchange?”, Journal of International Economics 28: 47–70. ### Macroeconomic sources of risk - Balfoussia, H., and M. Wickens (2007), “Macroeconomic Sources of Risk in the Term Structure”, Journal of Money, Credit and Banking 39: 205–36. - Kocenda, E. and T. Poghosyan (2008), “Macroeconomic Sources of Foreign Exchange Risk in New EU Members,” Journal of Banking and Finance 33, 2164-173. - Smith, P., S. Soresen, and M. Wickens (2003), “Macroeconomic Sources of Equity Risk”, CEPR Discussion Paper No. 4070. - Wickens, M., and P. Smith (2001), “Macroeconomic Sources of FOREX Risk”, University of York, Working Paper No. 2001/13. - Smith, P., and M. Wickens (2002), “Asset Pricing with Observable Stochastic Discount Factors”, Journal of Economic Surveys 16: 397–446. ### Volatility modeling, ARCH/GARCH, and covariance estimation - Bollerslev, T. (1990), “Modeling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model”, The Review of Economics and Statistics 72: 498–506. - Engle, R. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation”, Econometrica 50: 987–1008. - Engle, R., and K. Kroner (1995), “Multivariate Simultaneous Generalized ARCH”, Econometric Theory 11: 122–50. - Ding, Z., and R. Engle (2001), “Large Scale Conditional Covariance Matrix Modeling, Estimation and Testing”, Academia Economic Papers 29: 157–84. ### Asset pricing, consumption risk, and theoretical frameworks - Epstein, L., and S. Zin (1989), “Substitution, Risk Aversion and the Temporal Behavior of Consumption and Asset Returns: A Theoretical Framework”, Econometrica 57: 937–68. - Frankel, J. and C. Engel (1984), “Do Asset Demand Functions Optimize over the Mean and Variance of Real Returns? A Six Currency Test”, Journal of International Economics 17: 309–23. - Rogoff, K. (1977), “Essays on Expectations and Exchange Rate Volatility,” unpublished Ph.D. Dissertation, MIT. - Cuthbertson, K. and D. Nitzsche (2005), “Quantitative Financial Economics: Stocks, Bonds and Foreign Exchange”, Second edition, John Wiley & Sons. ### Other country/region-specific and policy-related studies - Khan, M. (2009), “The GCC Monetary Union: Choice of Exchange Rate Regime”, Peterson Institute for International Economics, WP 09-1, Washington, DC. - Hollifield, B., and A. Yaron (2000), “The Foreign Exchange Risk Premium: Real and Nominal Factors”, Carnegie Mellon University, Graduate School of Industrial Administration, GSIA Working Papers: 2001-E13. *Source: _wp10255 - REFERENCES* --- _Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2010/_wp10255.pdf_