High Volatility Regime Research Articles

Purchasing power parity under high and low volatility regimes

This article adopts Markov switching models to establish and examine several types of nonlinear dynamics in exchange rate returns and provide a new test to analyse presence of purchasing power parity (PPP) after controlling for various market states. In contrast with Engle and Hamilton (1990) focusing on discussing the dual state setting on the first moment of quarterly data for major industrial countries’ currencies, we concentrate more on the second moment for monthly data and add an analysis of developing countries’ currencies. Our empirical findings are consistent with the following notions. First, volatility-switching behaviours are more (less) remarkable for developing (industrialized) countries’ currencies. Second, we denote the high volatility state of exchange markets of developing (industrialized) countries as a crisis (an unusual) condition. Moreover, PPP would be valid at the low (high) volatility state for developing (industrialized) countries.

Regime Shifts in a Dynamic Term Structure Model of U.S. Treasury Bond Yields

This article develops and empirically implements an arbitrage-free, dynamic term structure model with “priced” factor and regime-shift risks. The risk factors are assumed to follow a discrete-time Gaussian process, and regime shifts are governed by a discrete-time Markov process with state-dependent transition probabilities. This model gives closed-form solutions for zero-coupon bond prices, an analytic representation of the likelihood function for bond yields, and a natural decomposition of expected excess returns to components corresponding to regime-shift and factor risks. Using monthly data on U.S. Treasury zero-coupon bond yields, we show a critical role of priced, state-dependent regime-shift risks in capturing the time variations in expected excess returns, and document notable differences in the behaviors of the factor risk component of the expected returns across high and low volatility regimes. Additionally, the state dependence of the regime-switching probabilities is shown to capture an interesting asymmetry in the cyclical behavior of interest rates. The shapes of the term structure of volatility of bond yield changes are also very different across regimes, with the well-known hump being largely a low-volatility regime phenomenon.

Regime dependence between the official and parallel foreign currency markets for US dollars in Greece

This paper models the short-run as well as the long-run relationship between the parallel and official markets for US dollars in Greece in a bivariate Markov Switching Vector Error Correction Model (MS-VECM) framework. Modelling exchange rates within this context can be motivated by the fact that the change in regime should be considered as a random event and not predictable. The results show that linearity is rejected in favour of a MS-VECM specification, which forms statistically an adequate representation of the data. Two regimes are implied by the model; the high volatility regime and the low volatility one and they provide quite accurately the state of volatility associated with economic and political events that took place in Greece during the 1970s and 1980s. Another implication is that there is evidence of regime clustering. Finally, Granger causality seems to be regime independent when we consider the hypothesis that official rate causes the parallel rate but it is regime dependent when we consider the opposite direction.

Estimates of the ICAPM with regime-switching betas: evidence from four pacific rim economies

This article examines the relation between stock returns and the World Index for four Pacific Rim economies, i.e. that of Taiwan, Hong Kong, South Korea and Malaysia. When the constant International Capital Asset Pricing Model (ICAPM) and the regime-switching ICAPM are considered, the evidence shows that the estimated beta coefficients from the constant ICAPM model underestimates systemic risk under the high-volatility regime, but overestimates systemic risk under the low-volatility regime. In addition, the evidence is strong that the stock markets of Taiwan and Malaysia are less risky for traders, whereas that of South Korea is risk-neutral. The Hong Kong Hang Seng stock index, on the other hand, is highly risky for both speculators and investors. On the weight of the evidence, it is suggested that estimates of the ICAPM should account for the changes in betas over time and over different variance regimes.

Volatility in stock returns for new EU member states: Markov regime switching model

In this paper, we investigate the volatility in stock markets for the new European Union (EU) member states of the Czech Republic, Hungary, Poland, Slovenia and Slovakia by utilising the Markov regime switching model. The model detects that there are two or three volatility states for the emerging stock markets. The result reveals that there is a tendency that the emerging stock markets move from the high volatility regime in the earlier period of transition into the low volatility regime as they move into the EU. Entry to the EU appears to be associated with a reduction of volatility in unstable emerging markets.

Regime Switching and Artificial Neural Network Forecasting of the Cyprus Stock Exchange Daily Returns

This paper provides an analysis of regime switching in volatility and out-of-sample forecasting of the Cyprus Stock Exchange using daily data for the period 1996-2002. We first model volatility regime switching within a univariate Markov-Switching framework. Modelling stock returns within this context can be motivated by the fact that the change in regime should be considered as a random event and not predictable. The results show that linearity is rejected in favour of a MS specification, which forms statistically an adequate representation of the data. Two regimes are implied by the model; the high volatility regime and the low volatility one and they provide quite accurately the state of volatility associated with the presence of a rational bubble in the capital market of Cyprus. Another implication is that there is evidence of regime clustering. We then provide out-of-sample forecasts of the CSE daily returns using two competing non-linear models, the univariate Markov Switching model and the Artificial Neural Network Model. The comparison of the out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano (1995) test and forecast encompassing, using the Clements and Hendry (1998) test. The results suggest that both non-linear models equivalent in forecasting accuracy and forecasting encompassing and therefore on forecasting performance.

Asymmetric temporary and permanent stock-price innovations

This paper estimates a nonlinear, structural bivariate threshold model in order to identify independent temporary and permanent stock-price innovations in positive-return and negative-return regimes. The model finds that temporary innovations account for as much as 68% of the innovations after negative returns and less than 4% of the innovations after positive returns. This paper shows that the negative-return regime is also the high-volatility regime, a result that links the excess-volatility and asymmetric-volatility literatures. This link provides evidence that temporary stock-price innovations are due primarily to time-varying expected returns in an efficient market and not to a market inefficiency.

Volatility Regimes and the Provision of Liquidity in Order Book Markets

We analyze whether the liquidity provision in a pure order book market during normal market conditions (low volatility regime) differs from what is observed when the market is under stress (high volatility regime). We show that the static relationship between liquidity and volatility is resilient to regime changes in volatility. Nevertheless, we do find that it is more costly to trade when volatility is large. A VAR analysis shows that the liquidity dynamics is similar in the low and high volatility regimes, although the drop in liquidity subsequent to volatility shocks is larger in the high volatility regime. Finally, the market is more resilient to volatility or liquidity shocks in periods of turmoils.

Characteristics of permanent and transitory returns in oil-sensitive emerging stock markets: The case of GCC countries

The estimates suggest that for both return components there exists a statistically significant high volatility regime for all the Gulf Cooperation Council (GCC) stock markets and the oil market. On the other hand, the results for the low volatility state of both components are mixed. The individual GCC markets vary in terms of sensitivity to volatility and its duration; with Saudi Arabia and Oman having the highest overall return volatility. All the GCC markets are much less volatile than that of the more open, crisis-ridden, oil-exporting Mexico. All GCC returns move in the same direction, whether in terms of total return, fundamentals or fads under both volatility regimes. The correlations between themselves and with Mexico, the oil price and the Morgan Stanley Capital International Index (MSCI) returns are weak compared to the correlations among stock returns of Germany, Japan UK and the US [Bhar, R., Hamori, S., 2004. Empirical characteristics of the permanent and transitory components of stock returns: analysis in a Markov-switching heteroscedasticity framework. Economics Letters 82, 157–165]. Mexico has considerably higher correlation with both MSCI and the oil price than all the GCC countries.

American Put Options with Regime-Switching Volatility

In this paper we study the price of an American option with stochastic volatility. The nature of stochastic volatility in this paper is as follows: volatility can take two values and changes at the jump time of an independent Poisson process. Namely, the volatility of the underlying asset changes between two regimes, say 'high volatility regime' H and 'low volatility regime' L. We derive an analytic formula for the price of a perpetual put and prove that the price of the perpetual put is always higher in the high volatility regime than in the low volatility regime and the exercise boundary is lower in the high volatility regime than in the low volatility regime. We also show by numerical examples that the Richardson interpolation together with the randomization method by Carr (1998) provides a fast algorithm to approximate the price of an American put with finite expiry and compare our numerical results with the previous study by Bollen (1998).

Volatility changes caused by the trading system: a Markov switching application

An expanding literature exists to suggest that the trading mechanism can influence the volatility of security returns. This study adds to this literature by examining the impact that the introduction of SETS, on the London Stock Exchange, had on the volatility of security returns. Using a Markov switching regime change model security volatility is categorized as being in a regime of either high or low volatility. It is shown that prior to the introduction of SETS securities tended to be in a low volatility regime. At the time SETS was introduced securities moved to a high volatility regime. This suggests that volatility increased when SETS was introduced.

Regime (non)stationarity in the US/UK real exchange rate

Using a Markov-switching extension of the ADF regression, we find evidence that the US/UK real exchange rate is stationary in periods when it is in a low volatility regime, and nonstationary when it is in a high volatility regime.

Forecasting Stock Market Volatility with Regime-Switching GARCH Models

In this paper we compare a set of different standard GARCH models with a group of Markov Regime-Switching GARCH (MRS-GARCH) in terms of their ability to forecast the US stock market volatility at horizons that range from one day to one month. To take into account the excessive persistence usually found in GARCH models that implies too smooth and too high volatility forecasts, in the MRS-GARCH models all parameters switch between a low and a high volatility regime. Both gaussian and fat-tailed conditional distributions for the residuals are assumed, and the degrees of freedom can also be state-dependent to capture possible time-varying kurtosis. The forecasting performances of the competing models are evaluated both with statistical and risk-management loss functions. Under statistical losses, we use both tests of equal predictive ability of the Diebold-Mariano-type and test of superior predictive ability. Under risk-management losses, we use a two-step selection procedure where we first check which models pass the tests of correct unconditional or conditional coverage and then we compare the best models under two subjective VaR-based loss functions. The empirical analysis demonstrates that MRS-GARCH models do really outperform all standard GARCH models in forecasting volatility at horizons shorter than one week under both statistical and VaR-based risk-management loss functions. In particular, all tests reject the presence of a better model than the MRS-GARCH with normal innovations. However, at forecast horizons longer than one week, standard asymmetric GARCH models tend to be superior.

Indexing, cointegration and equity market regimes

This paper examines, from a market efficiency perspective, the performance of a simple dynamic equity indexing strategy based on cointegration. A consistent ‘abnormal’ return in excess of the benchmark is demonstrated over different time horizons and in different real world and simulated stock markets. A measure of stock price dispersion is shown to be a leading indicator for the abnormal return and their relationship is modelled as a Markov switching process of two market regimes. We find that the entire abnormal return is associated with the high volatility regime as the indexing model implicitly adopts a strategic position that pays off during market crashes, whilst effectively tracking the benchmark in normal market circumstances. Therefore we find no evidence of market inefficiency. Nevertheless our results have implications for equity fund managers: we show how, without any stock selection, solely through a smart optimization that has an implicit element of market timing, the benchmark performance can be significantly enhanced. Copyright # 2005 John Wiley & Sons, Ltd.

Real interest rates linkages between the USA and the UK in the postwar period

This paper addresses the issue of real interest rate linkages between the UK and the USA during the postwar period. We use a bivariate Markov switching vector error correction model, which accounts for both the regime switches in the real interest rates and their long-run cointegration properties over that period. We find strong evidence of two volatility regimes, namely a high-volatility and a low-volatility regime, jointly characterizing the US and the UK real interest rates. Evidence is found of high-volatility regime dependence between the two real interest rates. In addition, there is evidence of regime-dependent Granger causality: the US real interest rate Granger causes the UK only in the regime of high volatility. Copyright © 2005 John Wiley & Sons, Ltd.

Association Between Markov Regime-Switching Market Volatility and Beta Risk: Evidence from Dow Jones Industrial Securities

In this paper, the volatility of the return generating process of the market portfolio and the slope coefficient of the market model is assumed to follow a Markov switching process of order one. The results indicate very strong evidence of volatility switching behaviour in a sample of returns in the S&P500 index. In three of the thirty securities in the Dow Jones index, the estimated slope in the market model show strong switching behaviour. In these three securities the low risk state is more persistent than the high-risk state. For each security we estimate the conditional probabilities that the security is in the high (low) risk state given the market is in the high (low) volatility regime and show that this information can be used to classify securities into three distinct groups. There is no association between these groups and the securities' constant beta estimated in the market model and the Sharpe index. Some directions for further research are discussed.

Markov Switching Models for GDP Growth in a Small Open Economy

This paper fits Markov switching models to quarterly New Zealand aggregate GDP growth rates for the period 1978:1 to 2003:2 in order to analyse changes in mean and volatility over time. The models considered are drawn from a simple class of parsimonious, four state, Markov switching models which encompass a wide range of stationary time series behaviour from linear AR(1) models to non-linear models with persistent cycles and outliers. An overall objective is to use the models to help understand and identify changes in the historical growth performance of New Zealand's small open economy, particularly pre and post wide ranging economic reforms. Conclusions to emerge are that, in contrast to the 1980s, New Zealand GDP growth experienced an unusually long period of time in high growth and low volatility regimes since the early 1990s. In addition, New Zealand does not appear to have ...

How Regimes Affect Asset Allocation

International equity returns are characterized by episodes of high volatility and unusually high correlations coinciding with bear markets. This article provides models of asset returns that match these patterns and illustrates their use in asset allocation. The presence of regimes with different correlations and expected returns is difficult to exploit within a framework focused on global equities. Nevertheless, for global all-equity portfolios, the regime-switching strategy dominated static strategies in an out-of-sample test. In addition, substantial value was added when an investor switched between domestic cash, bonds, and equity investments. In a persistent high-volatility market, the model told the investor to switch primarily to cash. Large market-timing benefits are possible because high-volatility regimes tend to coincide with periods of relatively high interest rates.

Exchange rate volatility and its impact on the transaction costs of covered interest rate parity

This paper provides empirical evidence on the linkage between foreign exchange market volatility and daily 90-day covered interest rate parity (CIP) conditions of the three major exchange rates against the US dollar (US$). Markov regime shifting models were utilized to generate time series of volatility regime probabilities and these were used to explain the first and second moments of the daily deviations from and the transaction cost bands around the covered parity conditions. We find a significant positive relationship between the deviations and the regime probabilities, indicating an increasing probability of higher volatility state being associated with rising deviations (both first and second moments) from the parity condition. Similar positive relationship is found for the transaction bands. Rising (falling) probabilities of high (low) volatility regimes increased the first and second moments of the bands. Furthermore, we find a higher volatility state combined with a US$ depreciation is associated with significantly higher volatility in the daily deviations than an appreciation. Also, US$ depreciation is associated with widening transaction bands. This suggests that the level of market uncertainty was higher when the US$ was depreciating.

Exchange Rate Volatility and its Impact on the Transaction Costs of Covered Interest Rate Parity

This paper provides empirical evidence on the linkage between foreign exchange market volatility and daily 90-day covered interest rate parity conditions of the three major exchange rates against the US$. Markov regime shifting models were utilized to generate time series of volatility regime probabilities and these were used to explain the first and second moments of the daily deviations from and the transaction cost bands around the covered parity conditions. We find a significant positive relationship between the deviations and the regime probabilities, indicating an increasing probability of higher volatility state being associated with rising deviations (both first and second moments) from the parity condition. Similar positive relationship is found for the transaction bands. Rising (Falling) probabilities of high (low) volatility regimes increased the first and second moments of the bands. Furthermore, we find a higher volatility state combined with a US$ depreciation is associated with significantly higher volatility in the daily deviations than an appreciation. Also, US$ depreciation is associated with widening transaction bands. This suggests that the level of market uncertainty was higher when the US$ was depreciating. Keywords: Deviations from CIP, Markov regime shifting probabilities