Regime shifts in equity risk premium: international evidence
Abstract
Introduction. The academic discussion dedicated to the specific features of financial crises is focused on the analysis of historical volatility patterns. Most researchers point out that strong long-term fluctuations on stock markets are typical for financial crises. Similar reactions to economic crises can be found throughout financial history. Both academic communities and stock markets participants are interested in the search for driving forces that stimulate such kind of behaviour.
Purpose. Studying the influence of the scale of national stock markets and various economic crises on the risk level of operations with financial instruments.
Methods. Latent modes were identified using Markov switching models. The determination of latent market states was based on the ratio of the components of financial turbulence that characterise the isolated changes in correlated risks and volatility. The influence of the scale of stock markets on the profitability and risk of operations with financial instruments was assessed using the vector autoregression model.
Results. The influence of economic crises on the quantitative characters of the excess returns of the world’s largest stock markets was assessed using two-mode Markov switching models and revealed significant differences in the behaviour of stock markets at different time intervals. It was established that the high volatility regime dominates the market during the periods accompanied by economic crises. Through the combination of typical and atypical values of the financial turbulence components we identified four latent market states that provide additional information on the further changes in the current market regimes or lack thereof. Unilateral causal connections are typical for markets with high capitalisation. Markets adapt to the USA market shocks almost twice as slower as compared to the shocks of developing markets.
Conclusions. The identified dependences allow increasing the explanatory and prognostic potential of statistical risk analysis in national stock markets.
Metrics
References
Aimer, N. M., & Lusta, A. A. (2021). Exchange rates and oil price under uncertainty and regime switching: A Markov-switching VAR approach. Economic Journal of Emerging Markets, 13(2), 200–215. https://doi.org/10.20885/ejem.vol13.iss2.art9
Alemany, N., Aragó, V., & Salvador, E. (2020). Lead-lag relationship between spot and futures stock indexes: Intraday data and regime-switching models. International Review of Economics and Finance, 68, 269–280. https://doi.org/10.1016/j.iref.2020.03.009
Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5(1), 31–56. https://doi.org/10.1016/S1386-4181(01)00024-6
Amihud, Y., & Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of Financial Economics, 17(2), 223–249. https://doi.org/10.1016/0304-405X(86)90065-6
Andersson, M., Krylova, E., & Vähämaa, S. (2008). Why does the correlation between stock and bond returns vary over time? Applied Financial Economics, 18(2), 139–151. https://doi.org/10.1080/09603100601057854
Baiardi, L. C., Costabile, M., De Giovanni, D., Lamantia, F., Leccadito, A., Massabó, I., Menzietti, M., Pirra, M., Russo, E., & Staino, A. (2020). The dynamics of the S&P 500 under a crisis context: Insights from a three-regime switching model. Risks, 8(3), 1–15. https://doi.org/10.3390/risks8030071
Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad Is Stronger Than Good. Review of General Psychology, 5(4), 323–370. https://doi.org/10.1037/1089-2680.5.4.323
Baur, D. G., & Lucey, B. M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2), 217–229. https://doi.org/10.1111/j.1540-6288.2010.00244.x
Carpantier, J. F. (2021). Anything but gold - The golden constant revisited. Journal of Commodity Markets. https://doi.org/10.1016/j.jcomm.2021.100170
Chaieb, I., Errunza, V., & Langlois, H. (2021). How is liquidity priced in global markets? Review of Financial Studies, 34(9), 4216–4268. https://doi.org/10.1093/rfs/hhaa125
Charles, A., & Darné, O. (2014). Large shocks in the volatility of the Dow Jones Industrial Average index: 1928-2013. Journal of Banking and Finance, 43(1), 188–199. https://doi.org/10.1016/j.jbankfin.2014.03.022
Chen, S. S. (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators. Journal of Banking and Finance, 33(2), 211–223. https://doi.org/10.1016/j.jbankfin.2008.07.013
Chevallier, J., & Goutte, S. (2015). Detecting jumps and regime switches in international stock markets returns. Applied Economics Letters, 22(13), 1011–1019. https://doi.org/10.1080/13504851.2014.995356
Chiang, S.-M. (2012). The Relationships between Implied Volatility Indexes and Spot Indexes. Procedia - Social and Behavioral Sciences, 57, 231–235. https://doi.org/10.1016/j.sbspro.2012.09.1179
Chordia, T., Roll, R., & Subrahmanyam, A. (2000). Commonality in liquidity. Journal of Financial Economics, 56(1), 3–28. https://doi.org/10.1016/S0304-405X(99)00057-4
Chow, G., Jacquier, E., Kritzman, M., & Lowry, K. (1999). Optimal Portfolios in Good Times and Bad. Financial Analysts Journal, 55(3), 65–73. https://doi.org/10.2469/faj.v55.n3.2273
Coakley, J., & Fuertes, A. M. (2006). Valuation ratios and price deviations from fundamentals. Journal of Banking and Finance, 30(8), 2325–2346. https://doi.org/10.1016/j.jbankfin.2005.08.004
Costa, G., & Kwon, R. H. (2019). Risk parity portfolio optimization under a Markov regime-switching framework. Quantitative Finance, 19(3), 453–471. https://doi.org/10.1080/14697688.2018.1486036
Diebold, F. X., Lee, J.-H., Weinbach, G., & C. (2020). Regime Switching with Time-Varying Transition Probabilities. Business Cycles, 144–166. https://doi.org/10.2307/j.ctv15r57n1.11
Drake, P. P. (2021). The gold-stock market relationship during COVID-19. Finance Research Letters. https://doi.org/10.1016/j.frl.2021.102111
Endovitsky, D. A., Korobeinikova, L. S., & Korotkikh, V. V. (2021). Adaptive Portfolio Analysis based on the Trend Decomposition of a Financial Time Series: Case Study of the Moscow Exchange. Universal Journal of Accounting and Finance, 9(5), 1159–1168. https://doi.org/10.13189/ujaf.2021.090525
Endovitsky, D. A., Korotkikh, V. V., & Khripushin, D. A. (2021). Equity Risk and Return across Hidden Market Regimes. Risks, 9(11). https://doi.org/10.3390/risks9110188
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339–350. https://doi.org/10.1198/073500102288618487
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3). https://doi.org/10.3905/jpm.2005.500363
Gong, C. M., Luo, D., & Zhao, H. (2021). Liquidity risk and the beta premium. Journal of Financial Research. https://doi.org/10.1111/jfir.12263
Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357. https://doi.org/10.2307/1912559
Hamilton, J. D. (1990). Analysis of time series subject to changes in regime. Journal of Econometrics, 45(1–2), 39–70. https://doi.org/10.1016/0304-4076(90)90093-9
Hauptmann, J., Hoppenkamps, A., Min, A., Ramsauer, F., & Zagst, R. (2014). Forecasting market turbulence using regime-switching models. Financial Markets and Portfolio Management, 28(2), 139–164. https://doi.org/10.1007/s11408-014-0226-0
Johnson, N., Naik, V., Page, S., Pedersen, N., & Sapra, S. (2014). The stock–bond correlation. The Journal of Investment Strategies, 4(1), 3–18. https://doi.org/10.21314/jois.2015.034
Kambouroudis, D. S., McMillan, D. G., & Tsakou, K. (2021). Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility. Journal of Futures Markets, 41(10), 1618–1639. https://doi.org/10.1002/fut.22241
Kangalli Uyar, S. G., Uyar, U., & Balkan, E. (2021). The role of precious metals in extreme market conditions: evidence from stock markets. Studies in Economics and Finance. https://doi.org/10.1108/SEF-04-2021-0128
Kinlaw, W., & Turkington, D. (2013). Correlation surprise. Journal of Asset Management, 14(6), 385–399. https://doi.org/10.1057/jam.2013.27
Kirkby, J., & Nguyen, D. (2020). Efficient Asian Option Pricing Under Regime Switching Jump Diffusions and Stochastic Volatility Models. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3575594
Kritzman, M., & Li, Y. (2010). Skulls, Financial Turbulence, and Risk Management. Financial Analysts Journal, 66(5), 30–41. https://doi.org/10.2469/faj.v66.n5.3
Li, H., Novy-Marx, R., & Velikov, M. (2019). Liquidity Risk and Asset Pricing. Critical Finance Review, 8(1–2), 223–255. https://doi.org/10.1561/104.00000076
Liu, P. P., xu, K., & Zhao, Y. (2011). Market regimes, sectorial investments, and time-varying risk premiums. International Journal of Managerial Finance, 7(2), 107–133. https://doi.org/10.1108/17439131111122120
Liu, Y., Sun, H., Zhang, J., & Taghizadeh-Hesary, F. (2020). Detection of volatility regime-switching for crude oil price modeling and forecasting. Resources Policy, 69. https://doi.org/10.1016/j.resourpol.2020.101669
Mahalanobis, P. C. (1925). Analysis of race-mixture in Bengal. Journal of the Asiatic Society of Bengal, 24, 301. http://library.isical.ac.in/jspui/handle/10263/1622
Mahalanobis, P. C. (1936). On the Generalized Distance in Statistics. Proceedings of the National Institute of Sciences of India, 2(1), 49–55.
Matos, P., Costa, A., & da Silva, C. (2021). COVID-19, stock market and sectoral contagion in US: a time-frequency analysis. Research in International Business and Finance, 57. https://doi.org/10.1016/j.ribaf.2021.101400
Mittal, R., Narwal, K. P., & Sheera, V. P. (2020). Risk-return relationship in Asian, American and European stock markets. Singapore Economic Review. https://doi.org/10.1142/S0217590820500411
Naidu, D., & Ranjeeni, K. (2021). Effect of coronavirus fear on the performance of Australian stock returns: Evidence from an event study. Pacific Basin Finance Journal, 66. https://doi.org/10.1016/j.pacfin.2021.101520
Nejadmalayeri, A. (2021). Asset liquidity, business risk, and beta. Global Finance Journal, 48. https://doi.org/10.1016/j.gfj.2020.100560
Ni, Y., Day, M. Y., & Huang, P. (2020). Trading stocks following sharp movements in the USDX, GBP/USD, and USD/CNY. Financial Innovation, 6(1). https://doi.org/10.1186/s40854-020-00190-5
Nystrup, P., Madsen, H., & Lindström, E. (2018). Dynamic portfolio optimization across hidden market regimes. Quantitative Finance, 18(1), 83–95. https://doi.org/10.1080/14697688.2017.1342857
O’Donnell, N., Shannon, D., & Sheehan, B. (2021). Immune or at-risk? Stock markets and the significance of the COVID-19 pandemic. Journal of Behavioral and Experimental Finance, 30. https://doi.org/10.1016/j.jbef.2021.100477
Orcutt, G. H. (1948). A Study of the Autoregressive Nature of the Time Series Used for Tinbergen’s Model of the Economic System of the United States, 1919-1932. Journal of the Royal Statistical Society: Series B (Methodological), 10(1), 1–45. https://doi.org/10.1111/j.2517-6161.1948.tb00001.x
Page, S., & Panariello, R. A. (2018). When diversification fails. Financial Analysts Journal, 74(3), 19–32. https://doi.org/10.2469/faj.v74.n3.3
Paolella, M. S., Polak, P., & Walker, P. S. (2019). Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns. Journal of Econometrics, 213(2), 493–515. https://doi.org/10.1016/j.jeconom.2019.07.002
Pastor, L., & Stambaugh, R. F. (2003). Liquidity risk and expected stock returns. Journal of Political Economy, 111(3), 642–685. https://doi.org/10.1086/374184
Pastor, L., & Stambaugh, R. F. (2019). Liquidity Risk After 20 Years. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3371948
Perez-Quiros, G., & Timmermann, A. (2000). Firm size and cyclical variations in stock returns. Journal of Finance, 55(3), 1229–1262. https://doi.org/10.1111/0022-1082.00246
Pontiff, J., & Singla, R. (2019). Liquidity Risk? Critical Finance Review, 8(1–2), 257–276. https://doi.org/10.1561/104.00000075
Potrykus, M. (2021). The share of investments in gold and oil using the example of selected European stock exchanges– A comparative analysis. Cogent Economics and Finance, 9(1). https://doi.org/10.1080/23322039.2021.1929679
Roubaud, D., & Arouri, M. (2018). Oil prices, exchange rates and stock markets under uncertainty and regime-switching. Finance Research Letters, 27, 28–33. https://doi.org/10.1016/j.frl.2018.02.032
Sargent, T. J., & Sims, C. A. (2012). Empirical Macroeconomics. The Indian Economic Journal, 59(4), 3–33. https://doi.org/10.1177/0019466220120402
Schwert, G. W. (2012). Stock Volatility During the Recent Financial Crisis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1812970
Seven, Ü., & Yılmaz, F. (2021). World equity markets and COVID-19: Immediate response and recovery prospects. Research in International Business and Finance, 56. https://doi.org/10.1016/j.ribaf.2020.101349
Su, E.-D., & Fen, Y.-G. (2011). The affect of the U.S. dollar index, U.S. stocks and the currency exchange on the Taiwan stock market during the financial tsunami. Journal of Statistics and Management Systems, 14(4), 789–813. https://doi.org/10.1080/09720510.2011.10701586
Szulczyk, K. R., & Zhang, C. (2020). Switching-regime regression for modeling and predicting a stock market return. Empirical Economics, 59(5), 2385–2403. https://doi.org/10.1007/s00181-019-01763-9
van Beek, M., Mandjes, M., Spreij, P., & Winands, E. (2020). Regime switching affine processes with applications to finance. Finance and Stochastics, 24(2), 309–333. https://doi.org/10.1007/s00780-020-00419-2
Wold, H. O. A. (1960). A Generalization of Causal Chain Models. Econometrica, 28(2), 443–463.
Yaya, O. O. S., Gil-Alana, L. A., Adekoya, O. B., & Vo, X. V. (2021). How fearful are commodities and US stocks in response to global fear? Persistence and cointegration analyses. Resources Policy, 74. https://doi.org/10.1016/j.resourpol.2021.102273
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