Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

Liu, Xin-Yi; Margaritis, Dimitris; Wang, Peiming

doi:10.1016/j.jempfin.2012.04.011

Cited by 78 publications

(24 citation statements)

References 47 publications

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“…It is based on the exponential GARCH (EGARCH) model proposed by Nelson [1991]. As before, the "X" refers to the inclusion of the exogenous variables v i,t-1 as in Liu, Margaritis and Wang [2012]. There are two advantages to using an EGARCH specification as opposed to the GARCH(m,s) structure of Bollerslev [1986] as in Chebbi, Louafi and Hedhli [2013] and Han and Kristensen [2012].…”

Section: Model Frameworkmentioning

confidence: 99%

Predicting Equity Markets with Digital Online Media Sentiment: Evidence from Markov-switching Models

Nooijen¹,

Broda

2016

Journal of Behavioral Finance

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The authors examine the predictive capabilities of online investor sentiment for the returns and volatility of MSCI U.S. Equity Sector Indices by including exogenous variables in the mean and volatility specifications of a Markov-switching model. As predicted by the semistrong efficient market hypothesis, they find that the Thomson Reuters Marketpsych Indices (TRMI) predict volatility to a greater extent than they do returns. The TRMI derived from equity specific digital news are better predictors than similar sentiment from social media. In the two-regime setting, there is evidence supporting the hypothesis of emotions playing a more important role during stressed markets compared to calm periods. The authors also find differences in sentiment sensitivity between different industries: it is greatest for financials, whereas the energy and information technology sectors are scarcely affected by sentiment. Results are obtained with the R programming language. Code is available from the authors upon request.

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Section: Model Frameworkmentioning

confidence: 99%

Predicting Equity Markets with Digital Online Media Sentiment: Evidence from Markov-switching Models

Nooijen¹,

Broda

2016

Journal of Behavioral Finance

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“…Liu et al . (), however, show that trading volume innovations are not a significant determinant of state‐dependent volatilities. They used a two‐state Markov model characterized by transition probabilities associated with a vector of regressors and parameters through a logit link function.…”

Section: Introductionmentioning

confidence: 91%

Forecasting realised volatility: a Markov switching approach with time‐varying transition probabilities

Wang

Shrestha

Sun

2019

Accounting & Finance

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This paper introduces a markov-switching heterogeneous autoregressive (MS-HAR) model with time-varying transition probabilities (TVTP) for the realised volatility of Shanghai securities composite index returns. Its various extensions have been obtained by including negative returns outside trading hours in addition to the leverage effects and trading volume. The findings show asymmetries in the impact of explanatory variables on the realised volatility. Moreover, the out-of-sample results show that the benchmark MS-HAR with TVTP model and its extensions consistently outperform the simple HAR model, MS-HAR model with constant transition probabilities (CTP) and their extensions. These results are robust to alternative realised measurements, and have economic implications.

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“…Like for example, Turner et al (1989), Chu et al (1996), Schaller and Norden (1997) and Maheu and McCurdy (2000) have applied MS model to identify the occurrence of number of bullish and bearish 387 BRIICKS stock markets regimes in stock market [5]. Some later studies like Brooks and Katsaris (2005), Guidolin and Timmermann (2005, 2006, Maheu et al (2009) and Liu et al (2012) used MS model in either confirming the non-linear behvaiour of stock market returns or applying the identified regimes in capturing the speculative and predictive powers of various assets. Continuing with the same idea, some recent studies implemented this model in analyzing the cross-country asset allocation and risk diversification opportunties.…”

Section: Literature Reviewmentioning

confidence: 99%

Regime shifts and volatility in BRIICKS stock markets: an asset allocation perspective

Ahmad

Sehgal

2015

International Journal of Emerging Markets

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Purpose -The purpose of this paper is to examine the regime shifts and stock market volatility in the stock market returns of seven emerging economies popularly called as "BRIICKS" which stands for Brazil, Russia, India, Indonesia, China, South Korea and South Africa, over the period from February 1996 to January 2012 by applying Markov regime switching (MS) in mean-variance model. Design/methodology/approach -The authors apply MS model developed by Hamilton (1989) using its mean-variance switching framework on the monthly returns data of BRIICKS stock markets. Further, the estimated probabilities along with variances have been used to calculate the time-varying volatility. The authors also examine market synchronization and portfolio diversification possibilities in sample markets by calculating the Logit transformation based crossmarket correlations and Sharpe ratios.Findings -The applied model finds two regimes in each of these markets. The estimated results also helped in formulating the asset allocation strategies based on market synchronization and Sharpe ratio. The results suggest that BRIICKS is not a homogeneous asset class and each market should be independently evaluated in terms of its regime-switching behavior, volatility persistence and level of synchronization with other emerging markets. The study finally concludes that Russia, India and China as the best assets to invest within this emerging market basket which can be pooled with a mature market portfolio to achieve further benefits of risk diversification.Research limitations/implications -The study does not provide macroeconomic and financial explanations of the observed differences in dynamics among sample emerging stock markets. The study does not examine these markets under multivariate framework. Practical implications -The results highlight the role of regime shifts and stock market volatility in the asset allocation and risk management. This study has important implications for international asset allocation and stock market regulation by way of identifying and recognizing the differences on regimes and on the dynamics of the swings which can be very useful in the field of portfolio and public financial management. Originality/value -The paper is novel in employing tests of MS under mean-variance framework to examine the regime shifts and volatility switching behavior in seven promising BRIICKS stock market. Further, using MS model, the authors analyze the duration (persistence) of each identified regime across sample markets. The empirical results of MS model have been used for making portfolio allocation

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Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

Cited by 78 publications

References 47 publications

Predicting Equity Markets with Digital Online Media Sentiment: Evidence from Markov-switching Models

Predicting Equity Markets with Digital Online Media Sentiment: Evidence from Markov-switching Models

Forecasting realised volatility: a Markov switching approach with time‐varying transition probabilities

Regime shifts and volatility in BRIICKS stock markets: an asset allocation perspective

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