Document Type : Research Article
Authors
1 Department of Economics, University of Tehran, Tehran, Iran
2 Department of Mathematics, Tarbiat Modares University, Tehran, Iran
Abstract
This paper estimates systematic risk in Iran’s foreign exchange market using a stochastic volatility model, analyzing five distinct episodes shaped by varying economic and political conditions. By tracing the evolution of volatility dynamics across these episodes, we reveal critical shifts in market behavior under different risk regimes. Our results show that during low-risk episodes, volatility shocks exhibit high persistence, causing market disturbances to linger. In contrast, as systematic risk intensifies, volatility shocks dissipate more rapidly—yet this reduced persistence coincides with a marked rise in average volatility. We identify three particularly turbulent episodes in the past seven years, each characterized by exceptionally high levels of systematic risk. Strikingly, both the mean and variance of volatility increased during these high-risk periods, signaling not only heightened instability but also deeper Knightian uncertainty. These findings carry significant policy implications: when direct reduction of volatility proves challenging, policymakers should prioritize reducing the volatility of volatility to mitigate uncertainty and stabilize expectations. Notably, our analysis indicates that a 1% reduction in volatility corresponds to a 1.7% decline in the variance of daily exchange rate returns, underscoring the leverage policymakers have over market uncertainty.
Keywords
Likelihood Methods and Applications, pages 323–356. Emerald Group Publishing Limited,
2010.
[2] O. E. Barndorff-Nielsen, E. Nicolato, and N. Shephard. Some recent developments in stochastic volatility modelling. Quantitative Finance, 2(1):11, 2002.
[3] D. S. Bates. Jumps and stochastic volatility: Exchange rate processes implicit in deutsche
mark options. The Review of Financial Studies, 9(1):69–107, 1996.
[4] D. S. Bates. Post-’87 crash fears in the S&P 500 futures option market. Journal of Econometrics, 94(1-2):181–238, 2000.
[5] C. Broto and E. Ruiz. Estimation methods for stochastic volatility models: a survey. Journal
of Economic Surveys, 18(5):613–649, 2004.
[6] H. Dargahi and R. Ansari. Improved neural network forecasting models for foreign exchange
rates using volatility indices. Journal of Economic Research (Tahghighat-E-Eghtesadi), 43(4),
2009.
[7] D. Duffie and K. J. Singleton. Simulated moments estimation of Markov models of asset
prices. 1990.
[8] D. Duffie, J. Pan, and K. Singleton. Transform analysis and asset pricing for affine jumpdiffusions. Econometrica, 68(6):1343–1376, 2000.
[9] B. Eraker. Do stock prices and volatility jump? Reconciling evidence from spot and option
prices. The Journal of Finance, 59(3):1367–1403, 2004.
[10] B. Eraker, M. Johannes, and N. Polson. The impact of jumps in volatility and returns. The
Journal of Finance, 58(3):1269–1300, 2003.
[11] A. Gelman and D. B. Rubin. Inference from iterative simulation using multiple sequences.
Statistical Science, 7(4):457–472, 1992.
[12] A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis. 2025.
[13] R. Heybati, S. Samadi, and M. Vaez Barazani. The importance of regression equations
specification in measuring uncertainty of macroeconomic variables. Journal of Economic
Research (Tahghighat-E-Eghtesadi), 52(4):963–996, 2017.
[14] M. D. Hoffman and A. Gelman. The No-U-Turn sampler: adaptively setting path lengths in
Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1):1593–1623, 2014.
[15] S. B. Imandoust, S. M. Fahimifard, and S. Shirzady. Iran’s exchange rate forecasting using
ANFIS, NNARX and ARIMA models (2002-2008). Monetary and Financial Economics,
16(28), 2010.
[16] E. Jacquier, N. G. Polson, and P. E. Rossi. Bayesian analysis of stochastic volatility models.
Journal of Business & Economic Statistics, 20(1):69–87, 2002.
[17] E. Jacquier, N. G. Polson, and P. E. Rossi. Bayesian analysis of stochastic volatility models
with fat-tails and correlated errors. Journal of Econometrics, 122(1):185–212, 2004.
[18] M. Johannes and N. Polson. MCMC methods for continuous-time financial econometrics. In
Handbook of Financial Econometrics: Applications, pages 1–72. Elsevier, 2010.
[19] R. E. Kass, B. P. Carlin, A. Gelman, and R. M. Neal. Markov chain Monte Carlo in practice:
a roundtable discussion. The American Statistician, 52(2):93–100, 1998.
[20] H. Khodavaisi and A. Molabahrami. Modeling and prediction Iranian exchange rate based on
stochastic differential equations. Journal of Economic Research (Tahghighat-E-Eghtesadi),
47(3):129–144, 2012.
[21] F. H. Knight. Risk, Uncertainty and Profit. Houghton Mifflin, 1921.
[22] J. S. Liu. Monte Carlo Strategies in Scientific Computing. Springer, 2001.
[23] O. A. Martin, R. Kumar, and J. Lao. Bayesian Modeling and Computation in Python.
Chapman and Hall/CRC, 2021.
[24] A. Melino and S. M. Turnbull. Pricing foreign currency options with stochastic volatility.
Journal of Econometrics, 45(1-2):239–265, 1990.
[25] M. M. Momenzadeh, M. Nilchi, and M. Rostami. Measuring the volatility persistence of the
Tehran Stock Exchange using stochastic volatility models with jump in return. Journal of
Asset Management and Financing, 11(4):121–140, 2023.
[26] D. B. Nelson. Conditional heteroskedasticity in asset returns: A new approach. Econometrica,
59(2):347–370, 1991.
[27] F. Black. Studies of stock price volatility changes. In Proceedings from the American Statistical Association, Business and Economic Statistics Section, page 177, 1976.
[28] J. Pan. The jump-risk premia implicit in options: Evidence from an integrated time-series
study. Journal of Financial Economics, 63(1):3–50, 2002.
[29] J. Salvatier, T. V. Wiecki, and C. Fonnesbeck. Probabilistic programming in Python using
PyMC3. PeerJ Computer Science, 2:e55, 2016.
[30] S. K. Tayebi, N. Movahednia, and M. Kazemeyni. A comparison of artificial neural networks
(ANN) with econometrics methods for forecasting economic variables: An application to the
Iran’s exchange rate. 2008.
[31] S. J. Taylor. Modelling Financial Time Series. 1986.
[32] A. Vehtari, A. Gelman, D. Simpson, B. Carpenter, and P.-C. Burkner. Rank-normalization, ¨
folding, and localization: An improved Rˆ for assessing convergence of MCMC (with discussion). Bayesian Analysis, 16(2):667–718, 2021.
)