Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?

This study examines whether geopolitical risk (GPR) exhibits an ability to forecast crude oil volatility from a time-varying transitional dynamics perspective. Unlike previous studies that assume an oversimplification of the fixed transition probabilities for crude oil volatility, we develop an asymmetric time-varying transition probability Markov regime switching (AS-TVTP-MS) GARCH model. In-sample estimated results show that GPR yields strong evidence of regime switching behavior on crude oil volatility and that the negative shocks of GPR result in greater effects on switching probabilities than positive shocks. Out-of-sample results indicate that the AS-TVTP-MS GARCH model containing the GPR index outperforms other models, suggesting that the consideration of GPR information and time-varying regime switching together results in superior predictive performance. Moreover, the predictability of oil volatility is further verified to be economically significant in the framework of portfolio allocation. In addition, our results are robust to various settings.     

Option pricing under regime switching

Abstract

This paper develops a family of option pricing models when the underlying stock price dynamic is modelled by a regime switching process in which prices remain in one volatility regime for a random amount of time before switching over into a new regime. Our family includes the regime switching models of Hamilton (Hamilton J 1989 Econometrica 57 357–84), in which volatility influences returns. In addition, our models allow for feedback effects from returns to volatilities. Our family also includes GARCH option models as a special limiting case. Our models are more general than GARCH models in that our variance updating schemes do not only depend on levels of volatility and asset innovations, but also allow for a second factor that is orthogonal to asset innovations. The underlying processes in our family capture the asymmetric response of volatility to good and bad news and thus permit negative (or positive) correlation between returns and volatility. We provide the theory for pricing options under such processes, present an analytical solution for the special case where returns provide no feedback to volatility levels, and develop an efficient algorithm for the computation of American option prices for the general case.     

Level-ARCH short rate models with regime switching: Bivariate modeling of US and European short rates

This paper introduces regime switching into level-ARCH models for the short rates of the US, the UK, and Germany. Once regime switching and level effects are included there are no gains from including ARCH effects. It is of secondary importance how the regime switching is specified. The estimated level parameters differ across countries. The corresponding new bivariate models show that the states of the US and UK short rate volatilities are not independent nor identical. There is Granger causality from the US to the UK short rate volatility state but not vice versa. There is no contagion between the US and UK volatility states. Equivalent results apply to the relation between the US and German volatility states.     

基于贝叶斯MSSV-ST金融波动模型的股市特征及机制转移性研究

针对有偏厚尾金融随机波动模型难以刻画参数的动态时变性及结构突变的问题,设置偏态参数服从 Markov 转换过程,采用贝叶斯方法,构建带机制转移的有偏厚尾金融随机波动模型,考量股市不同波动状态间的机制转移性,捕捉股市间多重波动特性。通过设置先验分布,实现模型的贝叶斯推断,设计相应的马尔科夫链蒙特卡洛算法进行估计,并利用上证指数进行实证。结果表明：模型不仅刻画了股市的尖峰厚尾、杠杆效应等特性,发现收益率条件分布的偏度参数具有动态时变性,股市波动呈现出显著的机制转移特性,而且证实了若模型考虑波动的不同阶段性状态后,将降低持续性参数向上偏倚幅度的结论。     

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.     

On real interest rate dynamics and regime switching

We find evidence of regime switching dynamics in the USA and the UK real interest rates over the period 1881–2003. For the UK, there is a regime in which the real interest rate displays a relatively stronger mean-reversion and a regime in which it displays a relatively weaker mean-reversion. The former regime is characterized by a relatively larger error in the estimation of the reversion parameter, and higher volatility. For the USA, the two regimes differ in volatility. The probability of transition from one regime to another is found to be significantly related to the inflation rate regime, and to the political regime. The results highlight the importance of regime switching in the dynamics of the real interest rate, as well as the role of inflation and political regimes in explaining this switching.     

The frequency of regime switching in financial market volatility

The mechanism of risk responses to market shocks is considered as stagnant in recent financial literature, whether during normal or stress periods. Since the returns are heteroskedastic, a little consideration was given to volatility structural breaks and diverse states. In this study, we conduct extensive simulations to prove that the switching regime GARCH model, under the highly flexible skewed generalized t (SGT) distribution, is remarkably efficient in detecting different volatility states. Next, we examine the switching regime in the S&P 500 volatility for weekly, daily, 10-minute and 1-minute returns. Results show that the volatility switches regimes frequently, and differences between the distributions of the high and low volatility states become more accentuated as the frequency increases. Moreover, the SGT is highly preferable to the usually employed skewed t distribution.     

Regime switching affine processes with applications to finance

We introduce the notion of a regime switching affine process. Informally this is a Markov process that behaves conditionally on each regime as an affine process with specific parameters. To facilitate our analysis, specific restrictions are imposed on these parameters. The regime switches are driven by a Markov chain. We prove that the joint process of the Markov chain and the conditionally affine part is a process with an affine structure on an enlarged state space, conditionally on the starting state of the Markov chain. Like for affine processes, the characteristic function can be expressed in a set of ordinary differential equations that can sometimes be solved analytically. This result unifies several semi-analytical solutions found in the literature for pricing derivatives of specific regime switching processes on smaller state spaces. It also provides a unifying theory that allows us to introduce regime switching to the pricing of many derivatives within the broad class of affine processes. Examples include European options and term structure derivatives with stochastic volatility and default. Essentially, whenever there is a pricing solution based on an affine process, we can extend this to a regime switching affine process without sacrificing the analytical tractability of the affine process.     

The effectiveness of competing regulatory regimes and the switching effects: Evidence from an emerging market

I investigate the effectiveness of two competing regulatory regimes and the effect of switching from strict price limits to circuit breakers on volatility spillover, and also on trading interference hypotheses. I find that switching to the circuit breakers' regime increases volatility and disrupts the price discovery mechanism. Stock prices are prevented from reaching their equilibrium levels and traders are unable to obtain their desired positions on limits hit day. Moreover, I find that volatility is spread out over the following 2 days post-limit hits within the strict price limits regime. Finally, the results show that price limits interfere with trading activity and affect investors' beliefs and liquidity positions.     

Volatility switching and regime interdependence between information technology stocks 1995–2005

In this paper we use SWARCH models to analyze volatility regime switching and regime interdependence for information technology (IT) stocks in Canada, France, Hong Kong, Japan, Taiwan, the United States and a composite Emerging Markets (EM) index. We find that prior to the IT bubble country effects were more important for IT stocks, but the effect of the IT bubble has been to make industry effects more important than country effects in explaining the volatility switching behavior of IT stocks.     

高利率能否捍卫汇率稳定：基于马尔科夫变换的世代交叠模型的分析

按照传统观点,在本国货币遭受投机攻击时,中央银行的典型做法是提高短期利率来捍卫货币和汇率制度。但批评者认为,提高利率会增加经济发展的成本,容易引发信用恐慌和产出减少。通过建立一个基于马尔科夫变换的世代交叠模型可以发现,利率被提的越高,汇率波动率也会随之相应增加。当高利率的货币政策使经济增长放缓甚至衰退,维持汇率稳定的可信度下降时,投机者就会发动对本币的投机攻击。最后在外汇储备耗尽的情况下,中央银行权衡得失后不得不实行浮动汇率制。     

The Political Economy of Volatility Dynamics in the Hong Kong Stock Market

Despite its obvious importance, little empirical research has examined the impact of political risk on stock market volatility. This paper uses data on the Hong Kong stock market over a long sample period to investigate whether political risk has induced regime shifts in stock market volatility. Regime shifts are modelled via a Markov switching EGARCH model that allows for regime-dependent volatility asymmetry. We find strong evidence of regime shifts in conditional volatility as well as significant volatility asymmetry in high volatility periods. Major political uncertainties were reflected in a switch to the high-volatility regime. However, contrary to popular perceptions, we find no evidence that the Hong Kong stock market has become persistently more volatile since the start of Sino-British political negotiations in 1982.     

The January effect across volatility regimes

Using a Markov regime switching model, this article presents evidence of the well-known January effect on stock returns. The specification allows a distinction to be drawn between two regimes: one with high volatility and another with low volatility. We obtain a time-varying January effect that is, in general, positive and significant in both volatility regimes. However, this effect is larger in the high-volatility regime. In sharp contrast with most of the previous literature, we find two major results: (1) the January effect exists for all sizes of portfolio; (2) the negative correlation between the magnitude of the January effect and portfolio size fails across volatility regimes. Moreover, our evidence supports a slight decline in the January effect for all sizes of portfolio except the smallest, for which it is even larger.     

Earnings quality and the heterogeneous relation between earnings and stock returns

We adopt a heterogeneous regime switching method to examine the informativeness of accounting earnings for stock returns. We identify two distinct time-series regimes in terms of the relation between earnings and returns. In the low volatility regime (typical of bull markets), earnings are moderately informative for stock returns. But in high volatility market conditions (typical of financial crisis), earnings are strongly related to returns. Our evidence suggests that earnings are more informative to investors when uncertainty and risk is high which is consistent with the idea that during market downturns investors rely more on fundamental information about the firm. Next, we identify groups of firms that follow similar regime dynamics. We find that the importance of accounting earnings for returns in each of the market regimes varies across firms: certain firms spend more time in a regime where their earnings are highly relevant to returns, and other firms spend more time in a regime where earnings are moderately relevant to returns. We also show that firms with poorer accrual quality have a greater probability of belonging to the high volatility regime.     

Evidence of an asymmetry in the relationship between volatility and autocorrelation

This paper focuses on the general determinants of autocorrelation and the relationship between autocorrelation and volatility in particular. Using UK stock market index and individual stock price data, a multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) model is used to generate estimates of conditional autocorrelation. The covariance equation of this model is modified to include the potential determinants of autocorrelation including volatility, which is proxied using the time series of filtered probabilities of a Markov regime switching model. Consistent with the previous literature, this paper documents a negative relationship between volatility and autocorrelation. The results suggest that an asymmetry exists in this relationship which is attributed to the constraints placed on short selling.     

Growth uncertainty,generalized disappointment aversion and production-based asset pricing

We study a production economy with regime switching in the conditional mean and volatility of productivity growth. The representative agent has generalized disappointment aversion (GDA) preferences. We show that volatility risk in productivity growth carries a positive and sizable risk premium in levered equity. Our model can endogenously generate long-run risks in the volatility of consumption growth observed in the data. We show that introducing leverage with a procyclical dividend process consistent with the data is critical for the GDA preferences to have a large impact on equity returns.     

Regime-switching stochastic volatility and short-term interest rates

In this paper, we introduce regime switching in a two-factor stochastic volatility (SV) model to explain the behavior of short-term interest rates. We model the volatility of short-term interest rates as a stochastic volatility process whose mean is subject to shifts in regime. We estimate the regime-switching stochastic volatility (RSV) model using a Gibbs Sampling-based Markov Chain Monte Carlo algorithm. In-sample results strongly favor the RSV model in comparison to the single-state SV model and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) family of models. Out-of-sample results are mixed and, overall, provide weak support for the RSV model.     

Dynamic optimal capital structure with regime switching

We investigate the optimal capital structure of a corporate when the dynamics of the assets (both growth rate and volatility) change following different states of the economy. Two structural models are examined in the paper. The first considers the case when the firm is not facing tax benefit and bankruptcy costs with a regime switching dynamics. This model extends the Black and Cox (J Financ 31:351–367, 1976) model to allow for regime switching risk. The second model incorporates both tax benefit and bankruptcy costs with a regime switching dynamics. This is is more realistic, and is an extension of the Leland (J Financ 49(4):1213–1252, 1994) model with regime switching risk. We obtain closed-form analytic solutions for the optimal capital structure and default barrier for both models.     

The cost of capital- the practitioners view

The application of a SWARCH model to stock market returns allows one to endogenously determine the regime dependence of the stock market volatility. Comparison of the results from a sample of daily data from five major stock markets shows that the majority of the markets switch regimes simultaneously. This fact is used to investigate the relation between market volatility and the behaviour of the variance—;covariance matrix. It is found that the international variance—;covariance matrix is not stable and that changes in the matrix are dependent on the volatility regime. A high level of variance causes an increase in the average correlation coefficient. The co-movement of the markets is further described by a steady increase in the covariance over the whole sample period. It can be shown that both the time component and the regime dependence of the average correlation have separate and significant explanatory power.     

Log Mean-Variance Portfolio Selection Under Regime Switching

In this paper we develop a portfolio selection theory under regime switching means and volatilities. We use log mean-variance as the portfolio selection criteria and, as a result, the theory is made substantially easier to implement than other existing theories. Moreover, the estimated regimes are easy to interpret as one of the regimes corresponds to the business cycle turning points. Finally, we conduct an asset allocation simulation and obtain reasonable results by introducing an idea of switching volatility targets.