Two-state Markov Switching Model Research Articles

Risk-based capital for credit insurers with business cycles and dynamic leverage

This paper develops a risk-based capital pricing model for credit insurance portfolios held by a vulnerable insurer. The model accounts for business cycles using a two-state Markov switching model, and allows for dynamic leverage adjustment by the insured firms. The new proposed model, which incorporates risk-based capital practice, is better for both the insurer and the insured firms. Based on the risk-adjusted performance metric, we found that the insurer is better off insuring short- and medium-term loans in expansion and steady states, while it is better off backing both short- and long-term loans in recessions. Our results also emphasize that macroeconomic uncertainty significantly impairs the creditworthiness of the insurer and insured firms.

A Three-State Markov-Modulated Switching Model for Exchange Rates

Several authors have examined the long swings hypothesis in exchange rates using a two-state Markov switching model. This study developed a model to investigate long swings hypothesis in currencies which may exhibit ak-state(k≥2)pattern. The proposed model was then applied to euros, British pounds, Japanese yen, and Nigerian naira. Specification measures such as AIC, BIC, and HIC favoured a three-state pattern in Nigerian naira but a two-state one in the other three currencies. For the period January 2004 to May 2016, empirical results suggested the presence of asymmetric swings in naira and yen and long swings in euros and pounds. In addition, taking0.5as the benchmark for smoothing probabilities, choice models provided a clear reading of the cycle in a manner that is consistent with the realities of the movements in corresponding exchange rate series.

Dynamic relation of Chinese stock price-volume pre- and post- the Split Share Structure Reform

Purpose – The purpose of this paper is to identify the bull and bear regimes in Chinese stock market and empirically analyze the dynamic relation of Chinese stock price-volume pre- and post- the Split Share Structure Reform. Design/methodology/approach – The authors investigate the price-volume relationship in the Chinese stock market before and after the Split Share Structure Reform using Shanghai Composite Index daily data from July 1994 to April 2013. Using a two-state Markov-switching autoregressive model and a modified two-state Markov-switching vector autoregression model, this study identifies bull or bear market and also examine the existence of regime-dependent Granger causality. Findings – Using a two-state Markov-switching autoregressive model, the authors detect structural changes in the market volatility due to the reform, and find evidence of a positive rather than an asymmetric price-volume contemporaneous correlation. There is a strong dynamic Granger causal relation from stock returns to trading volume before and after the reform regardless of the market conditions, but the causal effects of volume on returns are only seen in the bear markets before the reform. The model is robust when using different stock indices and time periods. Originality/value – The work is different from previous studies in the following aspects: most of the existing empirical literature focus on the well-developed economies, but our interest lies in the emerging Chinese market that has witnessed rapid growth in the past decade; in contrast to many works in the literature that examine the price-volume relationship during one market condition, the authors compare the relationship in a bull market with that in a bear market, using a two-state MS-AR model; the authors also employ a modified two-state Markov-switching vector autoregression model to examine the existence of regime-dependent Granger causality; as the most massive systematic reform for the Chinese stock market since its inception in 2005, the Split Share Structure Reform has a profound impact on the Chinese stock market, thus it is of vital importance to explore its effects on both the price-volume relationship and the market structure.

Hedging or Speculation: What Can We Learn from the Volume-Return Relationship?

ABSTRACTWe investigate the volume-return relationship using data from the Chinese stock market. Drawing on a recent theoretical model on the volume-return relationship, we test empirically whether investors in China are hedging oriented or motivated by speculation. A two-state Markov-switching model is used to augment the basic model. Allowing the underlying model to switch between two regimes reveals further information that investors’ motivation in the Chinese stock market is sensitive to the general market conditions.

Are Asian Stock Market Returns Predictable?

ABSTRACTWe conduct predictability tests for selected Asian stock markets using monthly data from the period March 2001–April 2012. Asian market bears and returns are predicted using the U.S. stock market bears and returns. A two-state Markov-switching model is employed to distinguish between the bull and bear regimes in the U.S. and Asian stock markets. The in-sample predictability analysis suggests that the U.S. market returns and bears are important predictors of Asian market returns and some Asian bears. The out-of-sample predictability exercise is not able to reinforce the in-sample results, which is in large part due to the small forecasting sample size.

Time-varying expected momentum profits

This paper examines the time variations of expected momentum profits using a two-state Markov switching model with time-varying transition probabilities to evaluate the empirical relevance of recent rational theories of momentum profits. We find that in the expansion state the expected returns of winner stocks are more affected by aggregate economic conditions than those of loser stocks, while in the recession state the expected returns of loser stocks are more affected than those of winner stocks. Consequently, expected momentum profits display strong procyclical variations. We argue that the observed momentum profits are the realization of such expected returns and can be interpreted as the procyclicality premium. We provide a plausible explanation for time-varying momentum profits through the differential effect of leverage and growth options across business cycles.

Stock Market Volatility and Equity Returns: Evidence from a Two-State Markov-Switching Model with Regressors

Stock Market Volatility and Equity Returns: Evidence from a Two-State Markov-Switching Model with Regressors

Correlation Function and Business Cycle Turning Points: A Comparison with Markov Switching Approach

We present a new technical approach based on the autocorrelation function, widely used in physics, to determine and to analyze the business cycle turning points of an economic activity. This method is adapted to stochastic processes and does not require a smoothing technique. The application of this method to the industrial production seasonally adjusted of Tunisia, for the period 1994:4 -2006:8 gives similar results to these obtained by two-state Markov switching model.

Hedging or Speculation, What Can We Learn from the Volume-Return Relationship?

This study investigates the volume-return relationship using data from the Chinese stock market. Based on the model set up by Llorente et al. (2002), we test empirically whether investors in China are hedging oriented or motivated by speculation. A two-state Markov switching model was used to augment the basic model. Allowing the underlying model to switch between two regimes reveals further information that investors' motivation in the Chinese stock market is sensitive to the general market conditions.

Lumpy Investment and Filtering Techniques

In this paper, we propose the use of the Markov-switching filter to identify investment spikes. In using Markov-switching filter, we apply a first-order two-state Markov-switching mean model to the investment rates de-trended using Hodrick and Prescott's (1997) filter. A Gibbs-sampling procedure is used to produce the marginal posterior distributions of unobserved state variables and model parameters. Among other advantages, this filter allows us to identify multi-year investment spikes. Some investment projects are so large that they last more than one year. Thus, a single annual accounting period would not necessarily reflect the total expenditure necessary to complete the project. Furthermore, even a year-long project need not start at the beginning of an accounting year nor reach completion by the end of an accounting year. We estimate the filter using Compustat data over the period 1988 to 2007 for 504 firms without any missing values in the period. We find that some 86% of firms have lumpy investment using the filter with the 5% level of significance. We also categorize about 12% of firm-years in the sample as having investment spikes.

Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

Abstract This paper proposes a two-state Markov-switching model for stock market returns in which the state-dependent expected returns, their variance and associated regime-switching dynamics are allowed to respond to market information. More specifically, we apply this model to examine the explanatory and predictive power of price range and trading volume for return volatility. Our findings indicate that a negative relation between equity market returns and volatility prevails even after having controlled for the time-varying determinants of conditional volatility within each regime. We also find an asymmetry in the effect of price range on intra- and inter-regime return volatility. While price range has a stronger effect in the high volatility state, it appears to significantly affect only the transition probabilities when the stock market is in the low volatility state but not in the high volatility state. Finally, we provide evidence consistent with the ‘rebound’ model of asset returns proposed by Samuelson (1991), suggesting that long-horizon investors are expected to invest more in risky assets than short-horizon investors.

Markov-Switching Models with Evolving Regime-Specific Parameters: Are Post-War Booms or Recessions All Alike?

In this paper, we relax the assumption of constant regime-specific mean growth rates in Hamilton’s (1989) two-state Markov-switching model of the business cycle. We introduce a random walk hierarchical prior for each regime-specific mean growth rate and impose a cointegrating relationship between the mean growth rates in recessionary and expansionary periods. By applying the proposed model to postwar U.S. real GDP growth (1947:Q4-2011:Q3), we uncover the evolving nature of the regime-specific mean growth rates of real output in the U.S. business cycle. Additional features of the postwar U.S. business cycle that we uncover include: i) a steady decline in the long-run mean growth rate of real output over the postwar sample and ii) an asymmetric error-correction mechanism when the economy deviates from its long-run equilibrium.

Markov Switching and State-Space Approaches for Investigating the Link between Egyptian Inflation Level and Uncertainty

In this paper, we use the Markov switching model, the state-space model with Markov switching heteroskedasticity and the local level model with GARCH(1,1) disturbances to investigate the link between the level of Egyptian inflation and its uncertainty. We use different ways to measure inflation uncertainty. First, we consider it as the variance of unanticipated inflation. Second, we measure it as the unconditional variance of unanticipated changes in inflation. Finally, we measure it by the conditional variance modeled as GARCH effect. We find evidence of a positive effect of inflation level on inflation uncertainty for the three models. By making the distinction between the long run and the short run, we conclude that inflation has a significant positive effect on uncertainty in the short run but no effect in the long run. We state that the cost of inflation is mainly due to the association between higher inflation and higher short-run uncertainty. Reversing the causality link between inflation and its uncertainty, we find that inflation uncertainty has a positive effect on inflation level in the short run but this effect dies out in the long run, which is indicative of a stabilization monetary policy in Egypt. According to the likelihood-based information criteria (Akaike and Schwarz Bayesian criteria), the state-space model with Markov switching heteroskedasticity and the two-state Markov switching model outperform all others.

Regime-Switching and Long Memory in Systematic Risk: Evidence from Realized Betas of Industry Portfolios

We employ a two-state Markov-switching model, which allows both regime-switching and long memory (fractional integration) in each regime, to explore the dynamic characteristics of the realized beta. We find that the model is superior to the one regime model. The identified regime switches are closely related to the level and volatility of the monthly realized betas, and there are clear similarities between the regimes and well known financial events. The macroeconomic influences have differences across industries in a pattern consistent with conventional expectations. Industry portfolio realized betas are predominantly identified with the low mean, low volatility regime. Finally, our results show that the two-state Markov-switching model is better than a single regime model in fitting the dynamics of the ex post monthly realized betas for all twelve industry portfolios. Our empirical findings on realized beta have important implications for tests of the conditional CAPM, for use of betas in performance benchmarking and for asset allocation decisions of portfolio managers.

What explains default risk premium during the financial crisis? Evidence from Japan

As is well documented, subprime mortgage markets carried significant default risk. This paper investigates the relationship between default risk premium, stock market conditions and macroeconomic variables during the financial crisis. Using iTraxx Japan Credit Default Swap (CDS) index spreads covering the period from March 2006 to November 2009, we employ a time-varying dynamic factor model with Markov regime switching to generate regime probabilities for default risk. We analyze the sensitivity of default risk premium changes to stock market conditions and macroeconomic variables by using two-state Markov switching models: a crisis regime sparked by rising loan defaults in the sub-prime mortgage market, and a non-crisis regime. We found strong evidence that the relationship between default risk premium changes, stock market and macroeconomic variables is regime-dependent. Our results suggest that during periods of crisis, CDS indices behave as a higher-risk indicator and become more sensitive to stock market conditions and macroeconomic variables. This paper examines the effects of the financial crisis in explaining the default risk premium. Understanding the determinants of default risk premium is important for financial analysts, economic policy makers and credit risk management.

Markov Chains In Predictive Models of Currency Crises - With Applications to Southeast Asia

This paper utilizes Markov regime switching models of exchange rate movements to construct predictive models of currency crises in Southeast Asia. The empirical results are based on estimated univariate two-state Markov switching models of monthly percentage changes in nominal exchange rates. Transition probabilities in the Markov chains are affected by real effective exchange rates, money supply relative to international reserves, and real domestic credit. The estimated models for Indonesia, Malaysia, the Philippines, and Thailand prove moderately successful at sending warning signals but the results are not uniform across countries. For some depreciation episodes, like those in 1981, 1984, and 1997 for Thailand and in 1997 for Malaysia, the model provides strong signals, in some cases, several months in advance. But for other episodes, such as in1997 for Indonesia and the Philippines, the model provides at best only weak warning signals.

Testing the hypothesis of market efficiency in the Taiwan-US forward exchange market since 1990

This article examines the applicability of the hypothesis of market efficiency in Taiwan's foreign exchange market using daily data. Instead of linear regression-based models, we consider the possibility that the true data generating process may come from two different distributions, and we employ the Markov Switching approach to analyse this. From the results of the two-state Markov Switching model, we define State 1 as the efficient state and State 2 as the inefficient one. Only the 30-day forward rate is able to differentiate between the two states. Based on the unconditional probabilities from the Markov switching model, we also find that the 30-day forward rate has a 70% probability in the efficient state, which indicates that 70% of all speculators fully extract information when predicting future spot rates, while 30% of all investors do not.

A self-tuning model for inflation rate dynamics

A regime-switching model is proposed to capture the structural changes in inflation dynamics. We apply a special version of the EM algorithm to find optimal parameter estimates of the model within the framework of a discrete-time finite state Markov chain that governs the switching of regimes from one state to another. The model is implemented to Canada’s consumer price index (CPI) data series and its performance is assessed by comparing its one-step ahead predictions with the actual data. We found that, within the dataset studied, a two-state Markov-switching model is sufficient to capture the dynamics of Canadian CPI series. The model being proposed is adaptive as parameters are updated upon the arrival of a new set of information. A description of how to calculate the standard errors for parameter estimates using the Fisher information matrix is provided. We also determine the optimal number of states for the Markov chain within the dataset considered via the AIC analysis.

Hierarchical Bayesian Markov Switching Models with Application to Predicting Spawning Success of Shovelnose Sturgeon

Summary The timing of spawning in fish is tightly linked to environmental factors; however, these factors are not very well understood for many species. Specifically, little information is available to guide recruitment efforts for endangered species such as the sturgeon. Therefore, we propose a Bayesian hierarchical model for predicting the success of spawning of the shovelnose sturgeon which uses both biological and behavioural (longitudinal) data. In particular, we use data that were produced from a tracking study that was conducted in the Lower Missouri River. The data that were produced from this study consist of biological variables associated with readiness to spawn along with longitudinal behavioural data collected by using telemetry and archival data storage tags. These high frequency data are complex both biologically and in the underlying behavioural process. To accommodate such complexity we developed a hierarchical linear regression model that uses an eigenvalue predictor, derived from the transition probability matrix of a two-state Markov switching model with generalized auto-regressive conditional heteroscedastic dynamics. Finally, to minimize the computational burden that is associated with estimation of this model, a parallel computing approach is proposed.

Markov switching negative binomial models: An application to vehicle accident frequencies

In this paper, two-state Markov switching models are proposed to study accident frequencies. These models assume that there are two unobserved states of roadway safety, and that roadway entities (roadway segments) can switch between these states over time. The states are distinct, in the sense that in the different states accident frequencies are generated by separate counting processes (by separate Poisson or negative binomial processes). To demonstrate the applicability of the approach presented herein, two-state Markov switching negative binomial models are estimated using five-year accident frequencies on Indiana interstate highway segments. Bayesian inference methods and Markov Chain Monte Carlo (MCMC) simulations are used for model estimation. The estimated Markov switching models result in a superior statistical fit relative to the standard (single-state) negative binomial model. It is found that the more frequent state is safer and it is correlated with better weather conditions. The less frequent state is found to be less safe and to be correlated with adverse weather conditions.