Search results

The purpose of this paper is to explore a regime switching asset allocation model that includes six major real estate security markets (USA, UK, Japan, Australia, Hong Kong and Singapore) and focuses on how the presence of regimes affects portfolio composition.

A Markov switching model is first developed to characterize real estate security markets’ risk‐return in two regimes. The mean‐variance portfolio construction methodology is then deployed in the presence of the two regimes. Finally, the out‐of‐sample analyzes are conducted to examine whether the regime switching allocation outperforms the conventional allocation strategy.

Strong evidence of regimes in the six real estate security markets in detected. The correlations between the various real estate security markets’ returns are higher in the bear market regime than in the bull market regime. Consequently the optimal real estate portfolio in the bear market regime is very different from that in the bull market regime. The out‐of‐sample tests reveal that the regime‐switching model outperforms the non‐regime dependent model, the world real estate portfolio and equally‐weighted portfolio from risk‐adjusted performance perspective.

The application of the Markov switching technique to real estate markets is relatively new and has great significance for international real estate diversification. With increased significance of international securitized property as a real estate investment vehicle for institutional investors to gain worldwide real estate exposure, this study provides significant insights into the investment behavior and optimal asset allocation implications of the listed real estate when returns follow a regime switching process.

This study aims to investigate the impact of sentiment shocks based on US investor sentiments, bearish and bullish market conditions. Earlier studies, though very few, only consider the effect of investor sentiments on stock returns of emerging frontier Asian (EFA) markets.

This study uses the application of regime switching model because of its capability to explore time-varying causality across different regimes unlike traditional linear models. The Markov regime switching model uses regime switching probabilities for capturing the potential asymmetries or non-linearity in a model, in this study’s case, thereby adjusting investor sentiments shocks to stock market returns.

The results of the Markov regime switching method suggests that US sentiment, bullish and bearish market shocks act as a main contributors for inducing variation in EFA stock market returns. The study’s non-parametric robustness results highlight an asymmetric relationship across the mean series, whereas a symmetric relationship across variance series. The study also reports Thailand as the most sensitive market to global sentiment shocks.

The sensitivity of the EFA markets to these global sentiment shocks highlights their sensitivity and implications for investors relying merely on returns correlation and spillover. These findings also suggest that spillover from developed to emerging and frontier equity markets only in the form of returns following traditional linear models may not be appropriate.

This paper supports the behavioral aspect of investors and resultant spillover from developed market sentiments to emerging and frontier market returns across international equity markets offering more rational justification for an irrational behavior.

The study’s motivation to use the application of regime switching models is because of its capability to explore time-varying causality across different regimes unlike traditional linear models. The Markov regime switching model uses regime switching probabilities for capturing the potential asymmetries or non-linearity in a model, in the study’s case, thereby adjusting investor sentiments shocks to stock market returns. It is also useful of the adjustment attributable to exogenous events.

The purpose of this paper is to examine the effects of parameter uncertainty in the returns process with regime shifts on optimal portfolio choice over the long run for a static buy-and-hold investor who is investing in industry portfolios.

This paper uses a Markov switching model to model returns on industry portfolios and propose a Gibbs sampling approach to take into account parameter uncertainty. This paper compares the results with a linear benchmark model and estimates without taking into account parameter uncertainty. This paper also checks the predictive power of additional predictive variables.

Incorporating parameter uncertainty and taking into account the possible regime shifts in the returns process, this paper finds that such investors can allocate less in the long run to stocks. This holds true for both the NASDAQ portfolio and the individual high tech and manufacturing industry portfolios. Along with regime switching returns, this paper examines dividend yields and Treasury bill rates as potential predictor variables, and conclude that their predictive effect is minimal: the allocation to stocks in the long run is still generally smaller.

Studying the effect of regime switching behavior in returns on the optimal portfolio choice with parameter uncertainty is our main contribution.

This paper aims at examining the co-movement dependent regime and causality relationships between conventional and Islamic returns for emerging, frontier and developed markets from November 2008 to August 2020.

First, the authors used the Markov-switching autoregression (MS–AR) model to capture the regime-switching behavior in the stock market returns. Second, the authors applied the Markov-switching regression and vector autoregression (MS-VAR) models in order to study, respectively, the co-movement and causality relationship between returns of conventional and Islamic indexes across market states.

Results show the presence of two different regimes for the three studied markets, namely, stability and crisis periods. Also, the authors found evidence of a co-movement relationship between the conventional and Islamic indexes for the three studied markets whatever the regime. For the Granger causality, it is proved only for emerging and developed markets and only during the stability regime. Finally, the authors conclude that Islamic indexes can act as diversifiers, or safe-haven assets are not strongly supported.

This paper is the first study that examines the co-movement and the causal relationship between conventional and Islamic indexes not only across different financial markets' regimes but also during the COVID-19 period. The findings may help investors in making educated decisions about whether or not to add Islamic indexes to their portfolios especially during the recent outbreak.

The purpose of this paper is to investigate regime-switching and single-regime GARCH models for the extreme risk forecast of the developed and the emerging crude oil markets.

The regime-switching GARCH-type models and their single-regime counterparts are used in risk forecast of crude oil.

The author finds that the regime-switching GARCH-type models are suitable for the developed and the emerging crude oil markets in that they effectively measure the extreme risk of crude oil in different cases. Meanwhile, the model with switching regimes captures dynamic structures in financial markets, and these models are just only better than the corresponding single-regime in terms of long position risk forecast, instead of short position. That is, it just outperforms the single-regime on the downside risk forecast.

This study comprehensively compares risk forecast of crude oil in different situations through the competitive models. The obtained findings have strong implications to investors and policymakers for selecting a suitable model to forecast extreme risk of crude oil when they are faced with portfolio selection, asset allocation and risk management.

The purpose of this paper is to deal with the different phases of volatility behavior and the dependence of the variability of the time series on its own past, models allowing for heteroscedasticity like autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), or regime‐switching models have been suggested by reserachers. Both types of models are widely used in practice.

Both regime‐switching models and GARCH are used in this paper to model and explain the behavior of crude oil prices in order to forecast their volatility. In regime‐switching models, the oil return volatility has a dynamic process whose mean is subject to shifts, which is governed by a two‐state first‐order Markov process.

The GARCH models are found to be very useful in modeling a unique stochastic process with conditional variance; regime‐switching models have the advantage of dividing the observed stochastic behavior of a time series into several separate phases with different underlying stochastic processes.

The regime‐switching models show similar goodness‐of‐fit result to GARCH modeling, while has the advantage of capturing major events affecting the oil market. Daily data of crude oil prices are used from NYMEX Crude Oil market for the period 13 February 2006 up to 21 July 2009.

This paper aims to study the asset pricing implications for stock and bond markets in a long-run risks (LRR) model with regime shifts. This general equilibrium framework can not only generate sign-switching stock-bond correlations and bond risk premium, but also quantitatively reproduce various other salient empirical features in stock and bond markets, including time-varying equity and bond return premia, regime shifts in real and nominal yield curves, the violation of the expectations hypothesis of bond returns.

The researchers study the joint determinants of stock and bond returns in a LRR model framework with regime shifts in consumption and inflation dynamics. In particular, the means, volatilities, and the correlation structure between consumption growth and inflation are regime-dependent.

The model shows that the term structure of interest rates and stock-bond correlation are intimately related to business cycles, while LRR play a more important role in accounting for high equity premium than do business cycle risks.

This paper studies the joint determinants of stock and bond returns in a Bansal and Yaron (2004) type of LRR framework. This rational expectations general equilibrium framework can (1) jointly match the dynamics of consumption, inflation and cash flow; (2) generate time-varying and sign-switching stock and bond correlations, as well as generating sign-switching bond risk premium; and (3) coherently explain another long list of salient empirical features in stock and bond markets, including time-varying equity and bond return premia, regime shifts in real and nominal yield curves, the violation of the expectations hypothesis of bond returns.

In asset management, what if clients want to purchase protection from risk factors, under the form of variance risk premia. This paper aims to address this topic by developing a portfolio optimization framework based on the criterion of the minimum variance risk premium (VRP) for any investor selecting stocks with an expected target return while minimizing the risk aversion associated to the portfolio according to “good” and “bad” times.

To accomplish this portfolio selection problem, the authors compute variance risk-premium as the difference from high-frequencies' realized volatility and options' implied volatility stemming from 19 stock markets, estimate a 2-state Markov-switching model on the variance risk-premia and optimize variance risk-premia portfolios across non-overlapping regions. The period goes from March 16, 2011, to March 28, 2018.

The authors find that optimized portfolios based on variance-covariance matrices stemming from VRP do not consistently outperform the benchmark based on daily returns. Several robustness checks are investigated by minimizing historical, realized or implicit variances, with/without regime switching. In a boundary case, accounting for the realized variance risk factor in portfolio decisions can be seen as a promising alternative from a portfolio performance perspective.

As a new management “style”, the realized volatility approach can, therefore, bring incremental value to construct the conditional covariance matrix estimates.

The authors assess the portfolio performance determined by the variance-covariance matrices that are derived by four models: “naive” (Markowitz returns benchmark), non-switching VRP, maximum likelihood regime-switching VRP and Bayesian regime switching VRP. The authors examine the best return-risk combination through the calculation of the Sharpe ratio. They also assess another different portfolio strategy: the risk parity approach.

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.

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 cross-market correlations and Sharpe ratios.

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.

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.

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.

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 strategies and also examine the synchronization across markets. All these aspects of stock market regime have been largely ignored by the existing studies in emerging market context particularly the BRIICKS markets.

This paper aims to examine the dynamic of volatility of Malaysian listed property companies within pan-Asian public property markets based on different volatility perspective over the past 18 years, especially during the global financial crisis (GFC).

This study uses several statistical methods and formulas for analysing the dynamic of volatility of Malaysian listed property companies such as exponential generalised autoregressive conditional heteroscedasticity (EGARCH) and Markov-switching (MS) EGARCH. The MS-EGARCH model provides new insights on the volatility dynamics of Malaysian listed property companies compared to conventional volatility modelling techniques, particularly EGARCH. Additionally, this paper will analyse the volatility movement based on three different sub-periods such as pre-GFC, GFC and post-GFC.

The findings reveal that the markets perform differently under different volatility conditions. Moreover, the application of MS-EGARCH provides a different view on the volatility dynamics compared to the conventional EGARCH model, as MS-EGARCH provides more comprehensive findings, especially during extreme market conditions.

This study contributes to the literature on the dynamics of Malaysian listed property companies within pan-Asian countries, as the approach for assessing the volatility performance based on different volatility conditions is less explored by previous researchers.