Markov regime switching

This paper investigates the performance of various conditional volatility models to forecast the second moment of tanker freight rates. Justified by existing theoretical and empirical evidence, we focus on asymmetric Markov regime-switching models to study the major global routes for long-haul trade of crude oil during the sample period from June 2000 to May 2015. Moreover, in contrast to a number of existing studies, we examine seasonally adjusted freight rates. We find that regime-switching GARCH models outperform their single-regime complements in terms of in-sample fit and out-of-sample forecasting accuracy. In particular, the asymmetric MRS-EGARCH and MRS-APARCH exhibit superior in- and out-of-sample performance. To additionally examine the applicability in freight risk management, we compare Value-at-Risk and Expected Shortfall forecasts. Our results show that accounting for volatility regimes and asymmetry does not enhance the performance of one-day-ahead forecasts of either risk measure for both long and short trading positions.

Abstract. Many researchers use GARCH models to generate volatility fore-casts. Using data on three major US dollar exchange rates we show that such forecasts are too high in volatile periods. We argue that this is due to the high persistence of shocks in GARCH forecasts. ...

Empirical evidence shows that there is a close link between regime shifts and business cycle fluctuations. A standard term structure of interest rates, such as the Cox et al. (1985 Econometrica, 53, 385–407; CIR) model, is sharply rejected in the Treasury bond data. Only Markov regime-switching models on the entire yield curve of the Treasury bond data can account for the observed behavior of the yield curve. In this paper, we examine the impact of regime shifts on AAA-rated and BBB-rated corporate bonds through the use of a reduced-form model. The model is estimated by the Efficient Method of Moments (EMM). Our empirical results suggest that regime-switching risk has significant implications for corporate bond prices and hence has a material impact on the entire corporate bond yield curve, providing evidence for the approach of rating through the cycle employed by rating agencies.

This paper estimates constant and dynamic hedge ratios in the New York Mercantile Exchange oil futures markets and examines their hedging performance. We also introduce a Markov regime switching vector error correction model with GARCH error structure. This specification links the concept of disequilibrium with that of uncertainty (as measured by the conditional second moments) across high and low volatility regimes. Overall, in and out-of-sample tests indicate that state dependent hedge ratios are able to provide significant reduction in portfolio risk.

This study tests for and models non-linearities in inflation deviations from the target in five OECD countries that adopted inflation targeting over the 1990s. Our tests reject the linearity hypothesis and we show that the exponential smooth transition autoregressive (ESTAR) model is capable of capturing the non-linear behavior of inflation misalignments. The extent of inflation deviations from the target varies

In this era of shaky global economic and financial conditions for about a decade now since the global financial crisis 2008, how the volatilities of Islamic equities worldwide are behaving, especially in terms of their regime changing behavior, if any, is the main issue of concern in this paper. To this end, a relatively novel technique, namely, Markov regime switching GARCH (MSGARCH) is applied to some selected broad based Islamic equity indices from both advanced and emerging world and of their combinations. The results tend to indicate that in general there is no persistence in any particular regime to prevail, rather a high regime switching behavior between volatile and less volatile regimes are present in Islamic equities around the world. This perhaps reflects the prolonged uncertainties prevailing in the world economies and therefore implies higher risk for the investors in predicting their investment outcome.

In this era of shaky global economic and financial conditions for about a decade now since the global financial crisis 2008, how the volatilities of Islamic equities worldwide are behaving, especially in terms of their regime changing behavior, if any, is the main issue of concern in this
paper. To this end, a relatively novel technique, namely, Markov regime switching GARCH (MSGARCH) is applied to some selected broad based Islamic equity indices from both advanced and emerging world and of their combinations. The results tend to indicate that in general there
is no persistence in any particular regime to prevail, rather a high regime switching behavior between volatile and less volatile regimes are present in Islamic equities around the world. This perhaps reflects the prolonged uncertainties prevailing in the world economies and therefore
implies higher risk for the investors in predicting their investment outcome.

This article focuses on the asset price volatility at the stock exchange
that result from the regime switching behaviour in the market. This
study is devoted to the question about how the asset price volatility
affects the US sovereign debt market. The efficient market hypothesis
has been a base for the asset pricing. This hypothesis is discussed in
this study. The review of the literature reveals nuances of behavioural
finance theory, and allows us to better understand the regime
switching behaviour in the market. The object of empirical study is
the US sovereign debt market. We use the Markov Regime-Switching
ARCH (SWARCH) model to analyse data. The results show that there
is high volatility regime in both the 2012 and 2017 bonds US market,
which significantly affects bond prices.

We calibrate Markov regime-switching (MRS) mod- els to spot (log-)prices from two major power markets. We show that while the price-capped (or truncated) spike distributions do not give any advantage over the standard specification in case of moderately spiky markets (such as NEPOOL), they improve the fit and yield significantly different results in case of extremely spiky markets (such as

In this paper we propose a new goodness-of-fit testing scheme for the marginal distribution of regime-switching models. We consider models with an observable (like threshold autoregressions), as well as, a latent state process (like Markov regime-switching). The test is based on the Kolmogorov-Smirnov supremum-distance statistic and the concept of the weighted empirical distribution function. The motivation for this research comes