Extreme Movements and Volatility Regimes: A Copula-Based Endogenous Regime Switching Perspective

Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market

This paper develops and estimates the quantity based Markov-switching(MS) monetary policy rule for China. The MS regression results show that China's monetary policy rule does exist regime changes, and the best fit is with a version that allows time variation both in disturbance variance and intercept. The volatility and steady state of M2 growth rate under the volatile regime are much larger than that in the moderate regime. I then build an MS-DSGE model allowing for regime switching in monetary shock's variance and steady-state of M2 growth rate. Based on the Bayesian estimation of MS-DSGE model, I find that monetary shock becomes the main driver of the variations in GDP growth rate and inflation rate under the volatile regime,and monetary policy regime shift between moderate regime and volatile regime can lead to the time-varying volatilities of inflation and output in China's bussiness cycle.

Is gold a hedge or safe haven for Islamic stock market movements? A Markov switching approach

Using a Markov regime switching model, this article presents evidence of the well-known January effect on stock returns. The specification allows a distinction to be drawn between two regimes: one with high volatility and another with low volatility. We obtain a time-varying January effect that is, in general, positive and significant in both volatility regimes. However, this effect is larger in the high-volatility regime. In sharp contrast with most of the previous literature, we find two major results: (1) the January effect exists for all sizes of portfolio; (2) the negative correlation between the magnitude of the January effect and portfolio size fails across volatility regimes. Moreover, our evidence supports a slight decline in the January effect for all sizes of portfolio except the smallest, for which it is even larger.

This paper uses the case of Spain to investigate whether and how disruptive technology impacts banking stock returns under a high volatility regime and a low volatility regime. For this purpose, a two-factor model with heteroscedastic Markov switching regimes has been applied. The results indicate that disruptive technologies have an impact on Spanish banking stock returns and that the effects are volatility regime dependent, having a relevant positive impact in high volatility regimes and a less relevant negative impact in low volatility regimes. These findings suggest that investors are informed about and acknowledge the advantages of disruptive technologies and will use their adoption as a business strategy to offset adverse market circumstances. During stable market conditions, on the other hand, Spanish banking seems to have less expectations about disruptive technology as a business strategy. To summarise, this paper provides insights into the role of the pricing of banking-related assets and has other relevant implications for investors that include disruptive technology or banking exposed investments in their portfolios.

The volatility of asset returns can be classified into market and firm-specific volatility, otherwise known as idiosyncratic volatility. Idiosyncratic volatility is increasing over time with some literature attributing this to the IT revolution. An understanding of the relationship between idiosyncratic risk and return is indeed relevant for idiosyncratic risk pricing and asset allocation, in a context of emerging technologies. The case of high-tech exchange traded funds (ETFs) is especially interesting, since ETFs introduce new noise to the market due to arbitrage activities and high frequency trading. This article examines the relevance of idiosyncratic risk in explaining the return of nine high-tech ETFs. The Markov regime-switching (MRS) methodology for heteroscedastic regimes has been applied. We found that high-tech ETF returns are negatively related to idiosyncratic risk during the high volatility regime and positively related to idiosyncratic risk during the low volatility regime. These results suggest that idiosyncratic volatility matters in high-tech ETF pricing, and that the effects are driven by volatility regimes, leading to changes across them.

Regime-dependent herding behavior in Asian and Latin American stock markets

This study is fore comparing GARCH models and Markov switching GARCH models in their ability to estimate and forecasting the volatility of Tehran stock market in some horizon of forecasting. This paper provides an analysis of regime switching in volatility and out-of-sample forecasting of the IRAN using daily data for the period 1995-2011. We first model volatility regime switching within a univariate Markov-Switching framework. Then We provide out-of-sample forecasts of the TEHRAN daily returns using two competing non-linear models, the GARCH Markov Switching model and the uniregime GARCH Model. The comparison of the out-of-sample forecasts is done on the basis of forecast accuracy, using the 7statistical loss function. The results, also, shows that SW-GARCH models can remove the high persistence of GARCH models and separately in each regime of volatility, the persistence are high. This shows the priority of SW-GARCH models. Another implication is that there is evidence of regime clustering.

Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching

We investigate how the volatility of the iShares Latin America 40 ETF (ILF) responds to key economic and market sentiment indicators associated with economic uncertainty. Specifically, we explore the regime-dependent nature of ILF volatility in relation to Economic Policy Uncertainty (EPU), U.S. Economic Uncertainty (ECU), Global Economic Policy Uncertainty (GEPU), and implied risk, as captured by the Chicago Board Options Exchange's VIX (CBOE VIX), from 2001 to 2023. Our findings highlight that the connection between market volatility and economic/market sentiment is influenced by distinct volatility regimes. Utilizing a two-covariate GARCH-MIDAS (GM) model, a regime-switching Markov Chain (MSR) model, and quantile regressions (QR), we reveal that the impact of sentiment on realized volatility varies depending on the prevailing volatility regime, reflecting investors’ differing responses to market uncertainty. Additionally, our results show a significant linkage between ILF’s short and long-term volatility and economic uncertainty/sentiment indicators, suggesting that these factors shape ILF volatility across different market conditions and quantiles of the volatility distribution. Overall, our findings indicate that investor sentiment and economic uncertainty extend beyond their domestic origins, influencing volatility patterns in U.S., global, and Latin American markets. JEL classification: G12, G14, G38. Keywords: Volatility, GARCH-MIDAS, VIX, Economic policy uncertainty, Global economic policy uncertainty, Quantile regression, Regime switching Markov Chain regression.

Does the Financial Leverage Effect Depend on Volatility Regimes?

Using a Markov regime switching model, this article presents evidence on the well-known January effect on stock returns. The specification allows a distinction to be drawn between two regimes, one with high volatility and other with low volatility. We obtain a time-varying January effect that is, in general, positive and arises mainly in the low volatility regime. By contrast with from most previous literature, the January effect exists for all size portfolios. Moreover, the negative correlation with size fails across volatility regimes. Our evidence supports a slight decline in the January effect for all size portfolios except the smallest, for which it is even larger.

The primary contribution of this study is to examine the changes in cross-market relationship in international public property markets from a volatility regime switching perspective from January 1990 to January 2012. We find that global developed public property markets can be adequately characterised by a SWARCH model. In particular, most of the persistence in real estate stock price volatility can be attributed to the persistence of low-, medium- and high-volatility regimes in international developed public property markets. Moreover, there is a significant volatility increase during the crises periods for all markets examined. However, the identified high-volatility regime appears short-lived. Based on the SWARCH results, we find that the dynamic linkages among the markets are positively dependent on volatility regime. Specifically, the market correlations, foreign market influence, aggregate variance spillover index and variance–covariance matrix have intensified as market volatility increases during this period. Moreover, the evolution of the cross-market linkages among the sample public property markets is influenced significantly by both a time trend and a volatility regime factor that are independent of the influences of the global stock market and national stock markets. Our results imply that risk-reduction via international diversification in public property markets may only hold true in low-volatility periods. Consequently, portfolio managers need to understand and implement volatility state-dependent optimal asset allocation in order to better advise their clients.

This paper explores the sensitivity of Romanian collective investment undertakings’ returns to changes in equity, fixed income and foreign exchange market returns. We use a sample of 80 open-end investment funds and pension funds with daily returns between 2016 and 2018. Our methodology consists of measuring changes in the daily conditional volatility for the fund returns (EGARCH) and changes in their conditional correlation with selected market risk factors (DCC MV-GARCH) throughout different volatility regimes identified using a Markov Regime Switching model. We argue that, on average, the level of conditional correlations between funds and market risk factors remained stable and unconcerned by the volatility regimes. In addition, for only less than half of the funds in the sample, their volatility regimes were synchronized with those of the selected market risk factors. We found that, on average, fund returns are more correlated with equity returns and less correlated with changes in local bond yields, while not being significantly influenced by changes in foreign bond yields or changes in foreign exchange. During the period investigated equity returns were the most volatile while the funds returns volatility were, on average, much more reduced. Overall, our results show the resilience of the Romanian collective investment sector to the selected market risk factors, during the investigated period.

The Regime Characteristics of Chinese Stock Market Industry Sectors