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Book part
Publication date: 1 July 2015

Nikolay Markov

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…

Abstract

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.

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Monetary Policy in the Context of the Financial Crisis: New Challenges and Lessons
Type: Book
ISBN: 978-1-78441-779-6

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Article
Publication date: 1 May 2007

K.H. Liow and H. Zhu

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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 regimeswitching model outperforms the non‐regime dependent model, the world real estate portfolio and equally‐weighted portfolio from risk‐adjusted performance perspective.

Originality/value

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.

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Journal of Property Investment & Finance, vol. 25 no. 3
Type: Research Article
ISSN: 1463-578X

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Book part
Publication date: 30 November 2011

Massimo Guidolin

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…

Abstract

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.

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Missing Data Methods: Time-Series Methods and Applications
Type: Book
ISBN: 978-1-78052-526-6

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Article
Publication date: 28 February 2020

Mobeen Ur Rehman and Nicholas Apergis

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

Journal of Economic Studies, vol. 47 no. 3
Type: Research Article
ISSN: 0144-3585

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Book part
Publication date: 21 November 2014

Alex Maynard and Dongmeng Ren

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…

Abstract

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.

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Essays in Honor of Peter C. B. Phillips
Type: Book
ISBN: 978-1-78441-183-1

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Article
Publication date: 30 September 2013

Deniz Kebabci Tudor

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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

Details

Studies in Economics and Finance, vol. 30 no. 4
Type: Research Article
ISSN: 1086-7376

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Article
Publication date: 23 July 2020

Yang Xiao

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.

Abstract

Purpose

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.

Design/methodology/approach

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

Findings

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.

Originality/value

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.

Details

International Journal of Emerging Markets, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-8809

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Book part
Publication date: 30 November 2011

Massimo Guidolin

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models…

Abstract

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

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Missing Data Methods: Time-Series Methods and Applications
Type: Book
ISBN: 978-1-78052-526-6

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Article
Publication date: 15 June 2010

Cuicui Luo, Luis A. Seco, Haofei Wang and Desheng Dash Wu

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…

Abstract

Purpose

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 regimeswitching models have been suggested by reserachers. Both types of models are widely used in practice.

Design/methodology/approach

Both regimeswitching 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 regimeswitching 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.

Findings

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

Originality/value

The regimeswitching 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.

Details

Kybernetes, vol. 39 no. 5
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 22 October 2019

Julien Chevallier and Dinh-Tri Vo

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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

Originality/value

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.

Details

The Journal of Risk Finance, vol. 20 no. 5
Type: Research Article
ISSN: 1526-5943

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