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This chapter estimates a regime switching Taylor Rule for the European Central Bank (ECB) in order to investigate some potential nonlinearities in the forward-looking policy reaction function within a real-time framework. In order to compare observed and predicted policy behavior, the chapter estimates Actual and Perceived regime switching Taylor Rules for the ECB. The former is based on the refi rate set by the Governing Council while the latter relies on the professional point forecasts of the refi rate performed by a large investment bank before the upcoming policy rate decision. The empirical evidence shows that the Central Bank’s main policy rate has switched between two regimes: in the first one the Taylor Principle is satisfied and the ECB stabilizes the economic outlook, while in the second regime the Central Bank cuts rates more aggressively and puts a higher emphasis on stabilizing real output growth expectations. Second, the results point out that the professional forecasters have broadly well predicted the actual policy regimes. The estimation results are also robust to using consensus forecasts of inflation and real output growth. The empirical evidence from the augmented Taylor Rules shows that the Central Bank has most likely not responded to the growth rates of M3 and the nominal effective exchange rate and the estimated regimes are robust to including these additional variables in the regressions. Finally, after the bankruptcy of Lehman Brothers the policy rate has switched to a crisis regime as the ECB has focused on preventing a further decline in economic activity and on securing the stability of the financial system.

This paper aims to extend the Fama and French (FF) three‐factor model in studying time‐varying risk premiums of Sector Select Exchange Traded Funds (ETFs) under a Markov regime‐switching framework.

First, the original FF model is augmented to include three additional macro factors – market volatility, yield spread, and credit spread. Then, the FF model is extended to a model with a Markov regime switching mechanism for bull, bear, and transition market regimes.

It is found that all market regimes are persistent, with the bull market regime being the most persistent, and the bear market regime being the least persistent. Both the risk premiums of the Sector Select ETFs and their sensitivities to the risk factors are highly regime dependent.

The regime‐switching model has a superior performance in capturing the risk sensitivities of the Sector Select ETFs, that would otherwise be missed by both the FF and the augmented FF models.

This is the first research on Sector Select ETFs with Markov regime switching.

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 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.

Introduction: Studies on the insurance sector/companies have, in recent years, taken their place in literature at an increasing rate. Especially after the 2008 global financial crisis, the need for people to ensure their assets has structurally changed both the transaction volume and the yield structure of insurance sector. The increase in demand for insurance has also increased the appetite of investors to make an investment on this sector. The transaction volume of the insurance sector has increased year by year coupled with the number of insurance companies traded on the stock exchanges has started to increase in the same direction.

Aim: This chapter aims to determine the return structure of the Borsa Istanbul Insurance Index (XSGRT) based on daily closing values.

Method: Markedly with similar studies in the literature review, the authors determined that the Markov Regime Switching (MRS) model is the best-suited model for the current research. It was applied for the data set of XSGRT Index from 1997 to 2020.

Results: The result shows that XSGRT has three regimes named as expansion regime, normal regime and recession regime. Subsequently, it has been determined that the index generally attends to transition from the recession regime to the expansion regime and normal regime. This outcome is statistically significant at a 5% significance level and confirmed by backtesting results. Likewise, the duration of the recession regime is longer than the normal and expansion regime.

Conclusion: Despite the fact that the XSGRT has not yet completed its development compared to other main and sectoral indices, it is one of the indices that offer attractive earnings for investors. To put it differently, the desire of insurance companies to stay longer totally in the normal and expansion period and their immediate exit from the recession period provides them with a significant competitive advantage in contrast to other indices.

Originality/Value: This research contributes to the literature by providing additional evidence for existing studies using the longer duration of data set and applying the MRS model for Insurance Index. Best of our knowledge, it is the first study that examines the return structure of XSGRT based on its daily closing values from 1997 to 2020. In essence, investors can use the result of this study and compare it with other stock indices to make the accurate investment decision to maximise their welfare and return on their equity investments. The authors suggest that not only the return but also the regime structures of the invested shares (indices) should be taken into account for investment decisions.

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

The paper tries to analyse empirically the impact of India's economic policy uncertainty (EPU) index on different macro-economic variables of India, like import, export, interest rate, exchange rate, inflation rate and stock market during pre-COVID-19 and COVID-19 era.

Although there exist several works where relationship and volatility among the stock markets and macro-economic indicators during the COVID-19 pandemic have been estimated, but till now none of the studies examined the effect of EPU index on different macro-economic variables in the Indian context along with the stock market due to the outbreak of COVID-19 pandemic. This is considered a noteworthy gap and hence opens up a new dimension for examination. To get a clear picture, monthly data from January, 2012 to September, 2021 have been considered where January, 2012–February, 2020 is taken as the pre-COVID-19 period and March, 2020–September, 2021 as COVID-19 period. All the data are converted into log natural. The authors applied DCC-GARCH model to investigate the impact of EPU index on volatility of selected variables over the study period across a multivariate framework and Markov regime-switching model to examine the switching over of the variables.

The results of dynamic conditional correlation - multivariate generalized autoregressive conditional heteroskedasticity (DCC-MGARCH) model indicates the presence of volatility in the dependent variables arising out of economic policy uncertainty considering the segmentation of the study period into pre-COVID-19 and COVID-19. The results of Markov regime-switching model show the variables make a significant move from low-volatility regime to high-volatility regime due to the presence of COVID-19.

It can be implied that impact of EPU in terms of volatility on the Indian Stock Market will lead to unfavourable investment conditions for the prospective investors. Even, the different macro-economic variables are to suffer from the volatility arising out of EPU across a long time horizon as confirmed from the DCC-MGARCH model.

The study is original in nature. It adds superior values from the new and significant findings from the study empirically. Application of DCC-MGARCH model and Markov regime switching model makes the study an innovative one in terms of methodology and findings.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

It has been said that oil price shocks affect stock market returns. However, empirical studies remain inconclusive regarding the nexus between oil price shocks and stock market returns. Consequently, the purpose of this study is to investigate the asymmetric impact of oil price shocks on stock returns in Nigeria.

A two-stage Markov regime-switching approach is used to examine the asymmetric effects of three different structural oil shocks on stock returns. The oil shocks, which include oil supply shock, aggregate demand shock and oil-specific demand shock, are derived using structural vector autoregressive. Monthly data that spans the period between January 1990 and December 2018 are deployed for estimation.

The linear estimation results show that only oil demand shock negatively and significantly affects the stock market returns. The Markov-switching regime results reveal that oil supply shock has a significant positive impact on the stock returns in a low-volatility state, whereas oil-specific demand shock negatively impacts the stock returns in a high-volatility state.

There is a need for policymakers and investors to take cognizance of not only the positive outcomes of a relatively stable state of oil price but also the negative consequences of a high-volatility state when formulating policy and making investment decisions, respectively.

This study differs from other similar studies in Nigeria that have examined the asymmetric relationship between oil price shocks and stock market return by using a two-stage Markov regime-switching approach. To the best of the authors’ knowledge, this is the first attempt at using this methodology.

This paper aims to attempt to capture the intertemporal/time-varying risk–return relationship in the Brazil, Russia, India and China (BRIC) equity markets after the global financial crisis (2007-2009), i.e. during a relative calm period. There has been a significant increase in advanced economies’ equity allocations to the emerging markets ever since the financial crisis. So, the present study is an attempt to account for the said relationship, thereby justifying investments made by the international investors.

The study uses non-linear models comprising asymmetric component generalised autoregressive conditional heteroskedastic model in mean (CGARCH-M) (1,1) model, generalised impulse response functions under vector autoregressive framework and Markov regime switching in mean and standard deviation model. The span of data ranges from 1 July 2009 to 31 December 2014.

The ACGARCH-M (1,1) model reports a positive and significant risk-return relationship in the Russian and Chinese equity markets only. There is leverage and volatility feedback effect in the Russian market because falling returns further increase conditional variance making the investors to expect a risk premium in the expected returns. The impulse responses indicate that for all of the BRIC markets, the ex-ante returns respond positively to a shock in the long-term risk component, whereas the response is negative to a shock in the short-term risk component. Finally, the Markov regime switching model confirms the existence of two regimes in all of the BRIC markets, namely, Bull and Bear regimes. Both the regimes exhibit negative relationship between risk and return.

It is an imperative task to comprehend the relationship shared between risk and returns for an investor. The investors in the emerging economies should understand the risk-return dynamics well ahead of time so that the returns justify the investments made under riskier environment.

The present study contributes to the literature in three senses. First, the data relate to a period especially after the global financial crisis (2007-2009). Second, the study has used a relatively newer version of GARCH based model [ACGARCH-M (1,1) model], generalised impulse response functions and Markov regime switching model to account for the relationship between risk and return. Finally, the study provides an insightful understanding of the risk–return relationship in the most promising emerging markets group “BRIC nations”, making the study first of its kind in all the perspectives.