Markov regime-switching

Abstract: This paper examines the nonlinear effects of investor sentiment on asset pricing in Bursa Malaysia. The Fama and French three-factor model is re-augmented within a time-varying Markov regime-switching framework to investigate the three risk premiums, conditioned by four different proxies for investor sentiment (i.e. market- wide indicators). The study finds evidence that the stock returns movement of Bursa Malaysia exhibits a nonlinear two regimes pattern. Besides, changes in the investor sentiment to some extent function as a mediator in the regime switching dynamics between bear and bull market cycles in Malaysian stock returns. It is also found that an increase in positive sentiment of investors leads to a higher transition probability of regime switching during bear markets. In addition, the three risk premiums are time- variant, contingent upon the fluctuation of the proxies for investor sentiment within discrete regimes. The study finds that in general, the market premium falls when the stock market switches from bull to bear markets. On the contrary, both the size and value premiums increase when the stock market moves from bull to bear markets. Keywords: Asset pricing, Bursa Malaysia, investor sentiment, time-varying Markov regime-switching model

This paper introduces a Bayesian Markov regime-switching model that allows the cointegration relationship between two time series to be switched on and off over time. Unlike classical approaches for testing and modeling cointegration, the Bayesian Markov switching method allows for estimation of the regime-specific model parameters via Markov Chain Monte Carlo and generates more reliable estimation. Inference of regime switching also provides important in- formation for further analysis and decision making.

Abstract: The housing market is one of the most important subsectors of capital markets that have the most backward and forward linkages with other sectors. Because of the high dependence of Iranian economy to oil revenues, oil price shocks can be affect the housing market. The aim of this study is to analyze the business cycle of the housing market, with emphasis on the impact of oil shocks on the return of housing. According to APM model, regardless of portfolio selected, several factors affect the return of housing assets that in this study, the risk and shocks of macroeconomic factors including money supply, private sector investment, housing facilities allocated by special Maskan Bank and oil export revenues, have been analyzed on the housing return by a Markov regime-switching GARCH model during the period 1982 -2016. The results have shown that the return of housing in the Iranian economy has three high, moderate and low return regimes. So that the volatility of the housing return is different in each of the three regimes. Housing returns volatility at the low return regime is more than volatility of returns at the moderate and high return regimes. Therewith in the 35 years of the research period, housing market has been 13 years in the moderate return regime, 20 years in the low return regime and only 2 years in the high return regime. The results also showed that based on Dutch disease hypothesis oil shocks, liquidity and private investment have a significant positive impact on the return of housing but the housing facilities of Maskan Bank have a significant negative impact on the return of housing.

Abstract: In this paper we discuss the calibration issues of models built on mean-reverting processes combined with Markov switching. Due to the unob- servable switching mechanism, estimation of Markov regime-switching (MRS) models requires inferring not only the model parameters but also the state pro- cess values at the same time. The situation becomes more complicated when the individual regimes are independent from each other and at least one of them exhibits temporal dependence (like mean reversion in electricity spot prices). Then the temporal latency of the dynamics in the regimes has to be taken into account. In this paper we propose a method that greatly reduces the compu- tational burden induced by the introduction of independent regimes in MRS models. We perform a simulation study to test the efficiency of the proposed method and apply it to a sample series of wholesale electricity spot prices from the German EEX market. The proposed 3-regime MRS model fits this data well and also contains unique features that allow for useful interpretations of the price dynamics.

Hamilton and Susmel [6] stated that the spurious high persistence problem in GARCH type models can be solved by combining the Markov Regime Switching (MRS) model with ARCH models (SWARCH). The idea behind regime switching models is that as market conditions change, the factors that influence volatility also change. Nowaday some researchers have development of GARCH model, as well as the benefit of using GARCH model [1, 7-9].

This paper uses a regime-switching model that is built on mean-reverting and local volatility processes combined with two Markov regime-switching processes to understand the market structure of the French fuel retail market over the period 1990-2013. The volatility structure of these models depends on a first exogenous Markov chain, whereas the drift structure depends on a conditional Markov chain with respect to the first one. Our model allows us to identify mean reverting and switches in the volatility regimes of the margins. In the standard model of cartel coordination, volatility can increase competition. We find that cartelization is even stronger in phases of high volatility. Our best explanation is that consumers consider volatility in prices to be a change in market structure and are therefore less likely to search for lower-priced retailers, thus increasing the market power of the oligopoly. Our findings provide a better understanding of the behavior of oligopolies.

The CCA serves to predict a relationship between credit spreads, lever- age, asset volatility, and interest rates. The merits of the CCA, when ana- lyzing sovereign risk and contributing to policy design and risk manage- ment, lie in the ability to provide a structural interpretation of a sovereign’s balance sheet, and translate changing economic conditions directly into quantitative credit risk indicators [Gapen et al. (2008)]. There is, however, strong evidence that the Merton-style structural model underestimates the actual probability of default [Tarashev (2005), Leland (2004), Boral et al. (2000)]. The underestimation is attributed to the fact that a firm or en- tity’s value is described by a diffusion process, which does not allow for sudden jumps in the entity’s value. Hence default can never occur by sur- prise [Erlwein et al. (2008)]. Leland (2004) suggests a jump component as a possible way of improving the underestimation. This could be achieved by a jump-diffusion model or a Markov regime-switching model wherein the jumps or discontinuities in a sovereign’s asset value occur at the instant where the transition of macroeconomic conditions takes place. Another possibility is to consider an uncertain threshold level. Such an approach generates default probabilities that can be expressed as mix- tures and is a simplified case of what is obtainable with regime-switching models. In addition, it allows one to capture early default by introducing an ad hoc specification of the threshold default level. Numerical experi- ments confirm that this approach performed very well in predicting the Lehman default [Brigo et al. (2009)].

Ramadan is deemed to be the holiest month which is observed by 1.6 billion Muslims across the world. We investigate the stock returns during Ramadan for 5 biggest stock markets in Muslim majority countries (Saudi Arabia, Malaysia, Turkey, Indonesia and Kuwait) by taking weekly data over the period of 5 years. By applying the Markov Regime Switching technique we found out that there is not enough evidence to conclude that Ramadan effect plays a significant part in providing investors with higher return during the one-month period. However, we found out that all the stock exchanges move in the same direction during the Ramadan 2012 and Ramadan 2015 which perhaps may be attributed to the Eurozone Crisis and oil price drops. These show that external factors may play a far bigger role in determining the returns from the stock market than a seasonality effect.

In financial markets, the state transition processes in the form bull– bear market swings, have significant practical relevance (Wang, 2008). One model that can be used to determine the probability of a regime switch is the MSM. In econometrics, the MSM of Hamilton (1989) is one of the most important models mainly because it can allow for changes both in variance and mean, it can allow for multiple breaks and can detect outliers in time series. If applied properly, the MSM, is able to explain and illustrate economic fluctuations around boom–recession and even other complex multi-phase cycles (Wang, 2008). The MSM has been used to analyse bull and bear markets in various financial markets (Bejaoui and Karaa, 2016; Chi et al., 2016; Yu et al., 2017; Frøystad and Johansen, 2017). Bejaoui and Karaa (2016) for example sought to provide a better understanding of the bull and bear markets with an extension of the multi-state MSM of Maheu and McCurdy (2000). The study applied a four-state-regime model defined as boom, bull, crash and bear states to define the bear and bull markets on trend-based schemes and established an indicator of market state which can detect inflexion points in a market cycle.

The data for South Africa includes CDS market quotes averaged on a given quarter for a total of 10 observations from March 2010 to June 2012. The estimated parameters calibrated across the entire term struc- ture on the last day of each quarter of the estimation period are presented in Table 1. The first column is the date when the calibration is performed. The second and third column present the volatility in state 1 and 2 re- spectively, and provide an indication of how the states are defined e.g., “good” or “bad.” Columns four and five give indication of the probability of the Markov chain remaining in state 1 and probability of transitioning to state 1 from state 2 respectively. Column six presents the inverse of the leverage parameter i.e., the ratio of the sovereign asset value and the observed distress barrier. An obligor defaults when the value of its assets falls below the value of its liabilities, or equivalent when its inverse lever- age ratio (the ratio of liabilities to assets) falls below one.

Finansal ve parasal gelişmelere bağlı olarak dalgalanma gösteren altın fiyatlarındaki belirsizlik hem altın yatırımcısının hem de birçok sektörün geleceğe yönelik öngörüler de bulunmasını zorlaştırmaktadır. Durağan piyasaların aksisine oynak piyasalarda, fiyatlarda aşağı ve yukarı yönlü hızlı değişim gözlemlenmekte; bu durum piyasalarda altın fiyatlarının tahmin edilebilirliğini zayıflatmaktadır. Bu nedenle, altın fiyatlarındaki rejim değişikliğinin yani farklı özellik gösteren dönemlerin tespiti, riskin ve olası portföy kayıplarının kontrolünün sağlanması için önem taşımaktadır (Tuna, Türk ve Ozun, 2014). Bu bağlamda Hamilton (1989) tarafından geliştirilen Markov rejim değişim (MS-AR(p)) modeli mevcut konjonktür içerisinde serilerin ne kadar süreyle düşük getirili daralma sürecinde, ne kadar süreyle yüksek getirili genişleme sürecinde kalacağının belirlenmesi amacıyla kullanılmaktadır.

In this paper, we applied regime switching model on market data to identify any poten- tial disequilibrium in the long-run. Long-run disequilibrium in energy markets indicates the need to consider the demand and supply management to improve energy market efficiency and stability. The results on the disequilibrium study imply that the long-run gasoline price dynamics may not always correct the system. Furthermore, the Markov regime switching model with two different regimes identifies there is a significant effect of regular gasoline costs, gas retail real price, residual fuel oil price, and distillate fuel oil price on retail gasoline prices in the USA and consequently on the stability of correction to these regimes.

In light of Moln´ ar and Thies (2018) demonstrating that the price data of Bitcoin contained seven distinct volatility regimes, and in response to the recent paper by Ardia et al (2018), who only fitted the 2- and 3-state MRS-GRACH models to the price data of Bitcoin; we fit- ted a sample of Bitcoin returns with six m-state MRS estimations, with m ∈ {2, ..., 7}. Our aim was to identify the optimal number of states for modelling the regime heteroskedas- ticity in the price data of Bitcoin. In doing so, we found that the restricted 5-state Markov regime-switching model attained the highest goodness-of-fit scores in our comparative study. However, for each additional state over the simple 2-state model that was estimated, there was an increased complexity in the form of transition restric- tion matrices and a disproportionate marginal cost in the form of computational runtime. Whilst we did attempt to fit both the 6- and 7-state models to our sample, in reference to Moln´ ar and Thies’ assertion; we found that the estimation results indicated overfitting of the sample, in the form of absorbing states and a redundant regime (State 7).

Markov state switching models are a type of specification which allows for the transition of states as an intrinsic property of the econometric model. Such types of statistical representations are well known and utilized in different problems in the field of economics and finance. Also, “Markov regime switching models are a type of specifications of which the selling point is the flexibility in handling processes driven by heterogeneous states of the world. In this section, we give a brief exposition on the subject. Technical details regarding Markov regime switching models can be found in Hamilton (1994), Hamilton (1996), Kim and Nelson (1999). For introductory material on the subject, see Hamilton (2005), Brooks (2002), Alexander (2008) and Tsay (2002) among others” (Perlin (2009)). Consider the following process given by:

This paper empirically examines the relation between energy consumption volatility and unpredictable variations in real gross domestic product (GDP) in the UK. Estimating the Markov switching ARCH model we find a significant regime switching in the behavior of both energy consumption and GDP volatility. The results from the Markov regime-switching model show that the variability of energy consumption has a significant role to play in determining the behavior of GDP volatilities. Moreover, the results suggest that the impacts of unpredictable variations in energy consumption on GDP volatility are asymmetric, depending on the intensity of volatility. In particular, we find that while there is no significant contemporaneous relationship between energy consumption volatility and GDP volatility in the first (low-volatility) regime, GDP volatility is significantly positively related to the volatility of energy utilization in the second (high- volatility) regime.

Within the framework of this paper, we focused on clustering IPO phenomenon. Four set of variables measuring IPO activity are employed to detect a nd pr e di ct hot and cold cycles and their turning points for the Tunisian and Egyptian share IPO markets using Markov regime switching models. These variables are the number of IPOs issued the levels of underpricing measured by the Initial Return and the Adjusted Stock Market Initial return, market conditions measured by the trading volume and three stock market returns across different periods and finally the listing speed. For both studied countries, results show that markets are in the majority of cases in cold state and the hot periods do not last for many months, this reinforces our need for a model that predicts the state of the market.

Our results show the importance of estimating a stochastic discount rate process that is allowed to vary with asset returns. If interest rates remain constant through time, then both the standard one-state model and the multi-state Markov regime switching model significantly underestimate the likelihood of future pension plan deficits compared to an in- dependently stochastic interest rate process. For both the one-state and multi-state models, when interest rates changes vary with asset returns, the estimated proportion of underfunded pension plans is higher than in the case with fixed discount rates (except in our final cal- ibration) but lower than when interest rates move independently from asset returns. In our main calibration with correlated interest rates and asset returns, incorporating multiple states predicts between approximately 2.5% and 4.5% more underfunded schemes than the one-state model and this number is persistent through time.

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 study tests the validity of the Expectations Hypothesis (EH) in an emerging market context and the utility of the information content of the term structure to predict the interest rate changes. This study extends the model used by Bulkley et al. (2011) to test the Expectation Hypothesis with a feature to allow for an unobservable Markov regime switching. The results reveals that the long and short term rates were found co-integrated before the onset of 2008 Global Financial Crisis, whereas, after the crisis, these were only partially co-integrated. These results marginally support the validity of expectation hypothesis in the Indian market. Further, it was found that the yield spread and the forward spot spread contained useful information to predict the interest rate changes. The results of the study provide valuable insights to the policy makers for efficient monetary policy management.

icantly improved fit when applied to real interest rate data. However, most of the existing models in time series that take into account the data memory property have stationary param- eters, which seem inadequate in real-world applications. This serves as another motivation to introduce the idea of higher-order HMMs. The underlying Markov process for these models o ff ers a simple way yet rich enough to describe the evolution of market variables with dynamic parameters and capture as well the memory property through the dependence on the back- ward time recurrence. To the best of our knowledge, the embedding of WHMM into available interest rate modeling approaches in the context of dynamic parameter estimation is so far non-existent. It is our intent to show the usefulness and merits of a WHMM-based interest rate model. Attempting to accomplish a similar goal, Hunt and Devolder [23] constructed an extension of the Ho and Lee model under a semi-Markov regime-switching framework aimed to capture the data’s long-memory property. An application of their proposed extension to the pricing of European bond options was given. A few applications of higher-order Markov chain in finance were considered in recent years and include modeling applications for returns of risky assets (Xi and Mamon [40]), risk management (Siu, et al. [33]), exotic option pricing (Ching, et al. [7]), and spot rates and credit ratings (Siu et al. [32]). Except for [40], none of these deal with the problem of e ffi cient and systematic parameter estimation.