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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.

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.

The authors propose novel tests for the detection of Markov switching deviations from forecast rationality. Existing forecast rationality tests either focus on constant deviations from forecast rationality over the full sample or are constructed to detect smooth deviations based on non-parametric techniques. In contrast, the proposed tests are parametric and have an advantage in detecting abrupt departures from unbiasedness and efficiency, which the authors demonstrate with Monte Carlo simulations. Using the proposed tests, the authors investigate whether Blue Chip Financial Forecasts (BCFF) for the Federal Funds Rate (FFR) are unbiased. The tests find evidence of a state-dependent bias: forecasters tend to systematically overpredict interest rates during periods of monetary easing, while the forecasts are unbiased otherwise. The authors show that a similar state-dependent bias is also present in market-based forecasts of interest rates, but not in the forecasts of real GDP growth and GDP deflator-based inflation. The results emphasize the special role played by monetary policy in shaping interest rate expectations above and beyond macroeconomic fundamentals.

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 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.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

The authors introduce a new approach to estimate high-dimensional factor-augmented vector autoregressive models (FAVAR) where the loadings are subject to idiosyncratic regime-switching dynamics. Our Bayesian estimation method alleviates computational challenges and makes the estimation of high-dimensional FAVAR with heterogeneous regime-switching straightforward to implement. The authors perform extensive simulation experiments to study the finite sample performance of our estimation method, demonstrating its relevance in high-dimensional settings. Next, the authors illustrate the performance of the proposed framework for studying the impact of credit market disruptions on a large set of macroeconomic variables. The results of this study underline the importance of accounting for non-linearities in factor loadings when evaluating the propagation of aggregate shocks.

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.