Volatility State Research Articles

Unraveling S&P500 stock volatility and networks – an encoding-and-decoding approach

Volatility is a measure of uncertainty or risk embedded within a stock's dynamics. Such risk has been received huge amounts of attention from diverse financial researchers. By following the concept of regime-switching model, we proposed a non-parametric approach, named encoding-and-decoding, to discover multiple volatility states embedded within a discrete time series of stock returns. The encoding is performed across the entire span of temporal time points for relatively extreme events with respect to a chosen quantile-based threshold. As such the return time series is transformed into Bernoulli-variable processes. In the decoding phase, we computationally seek for locations of change points via estimations based on a new searching algorithm in conjunction with the information criterion applied on the observed collection of recurrence times upon the binary process. Besides the independence required for building the Geometric distributional likelihood function, the proposed approach can functionally partition the entire return time series into a collection of homogeneous segments without any assumptions of dynamic structure and underlying distributions. In the numerical experiments, our approach is found favorably compared with parametric models like Hidden Markov Model. In the real data applications, we introduce the application of our approach in forecasting stock returns. Finally, volatility dynamic of every single stock of S&P500 is revealed, and a stock network is consequently established to represent dependency relations derived through concurrent volatility states among S&P500.

The euro to dollar exchange rate in the Covid‐19 era: Evidence from spectral causality and Markov‐switching estimation

In this paper, we analyse how the Covid‐19 pandemic changed the dynamics of the euro to dollar exchange rate. To do so, we make use of spectral non‐causality tests to uncover the determinants of the euro to dollar exchange rate, using data that cover the pre‐Covid‐19 and the actual Covid‐19 era, by considering the exchange rate movements of other currencies, the stock market index of S&P 500, and the price of oil and gold, as well as their realized volatilities. Based on our findings, the Covid‐19 pandemic has indeed significantly changed the determinants of the euro to dollar exchange rate. Also, to investigate the potential shifts in the regimes of the euro to dollar exchange rate, we formulate a Markov‐switching model with two regimes, based on the determinants that have been found in the previous step. Based on our findings, the duration of the high volatility state in the Covid‐19 era has doubled, from almost 3 to approximately 6 days, compared to the pre‐Covid‐19 era, whereas the high volatility state in the Covid‐19 era is characterized by a statistically significant higher range of volatility compared to the pre‐Covid‐19 era.

Chinese Influences on Inflation Determination in Australia and the ASEAN group: A Markov-Switching Analysis

Notwithstanding China’s rapidly growing role in the world economy, there is surprisingly little analysis of the extent to which inflation dynamics in China are passed through to other regional economies. We find evidence of significant inflation transmission from China to Australia and the five larger ASEAN economies under our Markov-switching analysis that allows for variation across high and low volatility states. Chinese inflation effects are further confirmed when we incorporate money supply and commodity price effects with a Markov-Switching Vector Autoregressive (VAR) framework.

Volatility Modeling for Currency Pairs and Stock Indices by Means of Complex Networks

Financial markets are complex systems. Network analysis is an innovative method for improving data sharing and knowledge discovery in financial data. Oriented weighted networks were created for the Shanghai Composite, S&P500, DAX30, CAC40, Nikkei225, FTSE100, IBEX35 indexes, for CNY-JPY, EUR-USD, GBP-EUR, RUB-CNY and for cryptocurrency BTC-USD. We considered data since January 6, 2006 to September 6, 2019. The complex networks had a similar structure for both types of markets, which was divided into the central part (core) and the outer one (loops). The emergence of such a structure reflects the fact that, for the most part, the stock and currency markets develop around some significant state of volatility, but occasionally anomalies occur when the states of volatility deviate from the core. Comparing the topology of evolutionary networks and the differences found for the stock and currency markets networks, we can conclude that stock markets are characterized by a greater variety of volatility patterns than currency ones. At the same time, the cryptocurrency market network showed a special mechanism of volatility evolution compared to the currency and stock market networks.

Risk of investing in volatility products: A regime-switching approach

ABSTRACT Volatility indexes provide a tool for investors to speculate and trade on market sentiment regarding future volatility. The risk of trading on volatility indexes can be measured by their second moments, namely, variance and correlation. This study considers the four representative volatility indexes published by the CBOE: stock market volatility index (VIX), crude oil volatility index (OVX), foreign exchange rate volatility index (EVZ), and gold price volatility index (GVZ). To examine their risk, we develop an extended multivariate Markov switching ARCH (MSARCH) model in which regime-switching variances, correlations, and variance-correlation relations are designed. Our empirical sample consists of the four volatility indexes from June 2008 to April 2020 for 612 weekly observations (Wednesday to Wednesday). For the conditional variances, we find evidence of regime-switching processes (switching between low and high volatility regimes) for the individual volatility index returns, with the exception of the GVZ. The estimated probability of the high volatility regime may be used to track economic distress and uncertainty shocks. These results provide evidence for volatility-of-volatility risk. For the conditional correlations, we find a regime-switching relation between variances and correlations. That is, the highest correlation appears when the paired volatility markets are simultaneously experiencing a state of high volatility. By contrast, when the paired volatility markets are encountering different volatility states, the correlation is weaker. These results indicate that the volatility-of-volatility risk is a factor affecting the dynamics of correlations between volatility indexes.

Bayesian Inference of Multivariate Regression Models with Endogenous Markov Regime-Switching Parameters

AbstractThis study introduces a multivariate regression model with endogenous Markov regime-switching parameters, in which the regression disturbances and regime switches are allowed to be instantaneously correlated. For the estimation and model comparison, we develop a posterior sampling algorithm for the parameters, regimes, and marginal likelihood calculation. We demonstrate the reliability of the proposed method using simulation and empirical studies. The simulation study shows that neglecting the endogeneity leads to inaccurate parameter estimates, and that our marginal likelihood comparison chooses a correctly specified model. In the business cycle application, we find that the joint dynamics of the U.S. industrial production index (IPI) growth and unemployment rates are subject to three-state endogenous regime shifts. Another application to stock and bond return data suggests that negative shocks to the stock return seem to cause regime shifts from a low volatility state to a high volatility state of the financial markets. (JEL: C11, C53, E43, G12)

Conditional Volatility Targeting

In analyzing the performance of volatility-targeting strategies, we found that conventional volatility targeting fails to consistently improve performance in global equity markets and can lead to markedly greater drawdowns. Motivated by return patterns in various volatility states, we propose a strategy of conditional volatility targeting that adjusts risk exposures only in the extremes during high- and low-volatility states. This strategy consistently enhances Sharpe ratios and reduces drawdowns and tail risks, with low turnover and leverage, when used in the major equity markets and for momentum factors across regions. Conditional volatility management can also be applied to tactical allocations among multiple assets or risk factors.

Oil price uncertainty, global industry returns and active investment strategies

This paper shows that time-varying oil return volatility predicts regime transitions across a majority of global stock sectors, particularly for durables, financials, industrials, oil & gas, telecommunications and utilities. Global stock sectors yield significantly higher returns during periods of low oil market uncertainty and an active, forward-looking investment strategy conditional on the state of oil market volatility yields significantly positive excess returns even after adjusting for systematic risk exposures. The findings show that the predictive information captured by oil market fundamentals can be utilized in active sector rotation strategies.

The Effects of Oil and Gas Risk Factors on Malaysian Oil and Gas Stock Returns: Do They Vary?

This study explores Malaysian oil and gas stocks’ exposure to oil and gas risk factors, paying special attention to subindustry classification, stock size, book-to-market value, and volatility state. The study employs firm-level weekly frequency data of oil and gas firms and several multi-asset pricing models within a GARCH (1,1)-X and Markov-switching framework. The empirical findings reveal that oil price, gas price, and exchange rate exhibit positive effects on the stock returns of all oil and gas sub-industries, but they exhibit negative effects on gas utilities sub-industry stock returns. The empirical findings also reveal that the extent of this effect varies across sub-industry, stock size, book-to-market value, and volatility states. Thus, the findings suggest the existence of asymmetric, heterogeneous, and non-linear exposures.

Decoding the Battlelust: A Note on the Provocations to War in the Purananuru

Unfettered by the tinais and turais by which the Purananuru is organized, this paper looks at the issues and subjectivities of the ancient Tamil society which determined its attitudes towards war. It includes in its narration not just the members of the royalty alone but every other stakeholder whose life was altered by war: the mother, the wife and the man himself. What the paper hopes to present is, by eschewing a theoretical framework, a world closer to the realities embodied and embedded in the poems of the Purananuru. Besides mapping the discourse of the society concerning war and heroism, it analyses the causes which frequently produced wars keeping the society in a state of perpetual volatility. Interestingly, it finds the glamorised world of the warrior co-existing with the gritty and often gruesome realities of the battlefield, with all its blood and gore.

The asymmetric spillover effect of the Markov switching mechanism from the futures market to the spot market

This article develops a multichain Markov switching dynamic conditional correlation ARCH model with idiosyncratic jump dynamics to investigate whether the state of the crude oil futures market can asymmetrically affect the state of the crude oil spot market. The asymmetric spillover effects are investigated after controlling the dependence structure on idiosyncratic jumps. The empirical findings show an asymmetric spillover effect from the futures market to the spot market. Moreover, the transition probabilities depend highly on the volatility of the futures market, showing the leading role of the futures market. The jump components play a relatively more important role in explaining the conditional variances than do the ARCH and regime-switching effects. Finally, both the contribution of idiosyncratic jumps on total variance and the correlation coefficient between the futures and spot returns rely on the volatility state of the futures and spot returns. The second and fourth moments of the conditional correlation coefficients will be underestimated when the common jumps and/or independent transition mechanisms are ignored. The findings of this paper have important implications for investors in accurately evaluating riskiness, hedgers in improving hedging performance, as well as market participants and government authorities in understanding the lead-lag relationship between crude oil spot and futures markets.

Bitcoin Sentiment Index, Performance and US Dollar Exchange Rate

This study introduces a comprehensive Google search volume based Bitcoin sentiment index (BSI) by following the methodology of Da, Engelberg, and Gao (2014). BSI is investigated for its association with Bitcoin returns, trade volume, volatility, and United States dollar exchange rates (USD). Results of the simple OLS regression clarify that BSI has a positive impact on Bitcoin returns and trade volume while it has a negative impact on the volatility of Bitcoin returns. Furthermore, the Auto-regressive distributed lag (ARDL) model has been used to capture the relationship between BSI and Bitcoin price with USD. Empirical results of the ARDL model demonstrate that Bitcoin sentiments affect USD in the short-run but not long-run. However, Bitcoin's price can negatively affect USD in short as well as in the long-run. Error correction term represents a 38.50% adjustment towards the long-run equilibrium in one month if there is a deviation in the short-run. The results imply that investors in FOREX or public in general interested in Bitcoin trading should foresee sentiments of the public towards Bitcoin.

Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states

Numerous advances in the modeling techniques of value-at-risk (VaR) have provided financial institutions with a wide range of market risk approaches. However, which model to use depends on the state of volatility. We present backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several of the best-known VaR models, including generalized autoregressive conditional heteroscedasticity (GARCH), extreme value theory (EVT), conditional autoregressive VaR (CAViaR) and filtered historical simulation (FHS) with multiple sets of parameters. The backtesting procedure is based on the excess ratio, Kupiec and Christoffersen tests for multiple thresholds and cost functions. The main contribution of this paper is that we compared the models in four different scenarios, with different states of volatility in the training and testing samples. The results indicate that the best of the models, ie, the least affected by changes in the volatility, is GARCH(1,1) with a standardized Student t distribution. Nonparametric techniques (eg, CAViaR with GARCH setup or FHS with a skewed normal distribution) have very prominent results in testing periods with low volatility, but they are worse in turbulent periods. We also discuss an automatic method to set an extreme distribution threshold for EVT models as well as several ensembling methods for VaR, of which the minimum VaR estimate from the best models – in particular, a minimum of GARCH(1,1) with a standardized Student t distribution and either the EVT or the CAViaR model – has been proven to give very good results.

Does herding behavior exist in the Mongolian stock market?

This investigation is among the first to examine the presence of herd behavior in the Mongolian stock market. Herding behavior was analyzed for the full sample data (1999 to 2019) including during bull and bear market periods, as well as high and low volatility states of markets. Additionally, the impact of four important events which occurred during these period were also tested: the establishment of the Finance Regulatory Committee of Mongolia (FRCM), the Global Financial Crises, Mongolia's inclusion in the FTSE Russell Watch list and economic boom in 2011. Using a cross-sectional absolute deviation (CSAD) model evidence of herding behavior was found in all the situations.

Determining the Interaction of the International Portfolio Flows with Exchange Rate Volatility in Developing Countries

The aim of this study is to examine the effect of the bond portfolio and equity inflows on the exchange rate dynamics for a set of developing countries, including Turkey, Hungary, New Zealand, India, Russia, Poland, Brazil and Argentina over the period 1997:01-2017:12 by using a Markov-switching model. According to the analysis results, the net bond inflows lead to an increase in the likelihood of a high volatility regime in Turkey and Russia and increases the probability of transition from the high volatility regime to the low volatility regime in Hungary. Additionally, the net bond inflows from New Zealand and Poland to the United States (US) rise the possibility of remaining in the low volatility regime. The net equity inflows from Turkey and Poland to the US lead to a rise in the possibility of remaining in the high volatility regime. Besides, the net equity inflows from Brazil and Argentina to the US lead to a decline in the possibility of remaining in the low volatility state. In the light of the empirical results supporting the return chasing hypothesis, this paper argues that credit controls on short-term financial inflows could be an effective means in stabilizing the foreign exchange market.

Do green investments react to oil price shocks? Implications for sustainable development

This study investigates whether green investments are connected to oil price shocks. While earlier papers mainly investigate the linkage between clean energy stock and crude oil prices, we consider recently introduced environmental indexes consisting of companies that use eco-friendly production processes and develop green infrastructures. Using the Markov regime switching regression approach, we observe that the effect of crude oil prices on environmental investments appears to be statistically insignificant, albeit positive in most of the cases. One possible reason behind such a finding is that the oil-dependence is limited to the case of eco-friendly firms. The results further indicate switching between low and high volatility regimes implying that there exist high and low volatility states for green assets. Given that growing concerns about climate change and energy security would inspire market participants shift towards ethical investments, our results could be of interest to investors aiming at decarbonizing their portfolios by including more environmental assets. One important finding of our empirical analysis is that green assets are found to be more susceptible to oil market volatility rather than to oil price fluctuations. This is a novel finding given that prior literature focusing on the association of oil sectors and green assets mainly consider the information on oil prices rather than oil volatility. Hence, our study aims to conceal an important void in the exiting literature.

Testing identification via heteroskedasticity in structural vector autoregressive models

Summary Tests for identification through heteroskedasticity in structural vector autoregressive analysis are developed for models with two volatility states where the time point of volatility change is known. The tests are Wald-type tests for which only the unrestricted model, including the covariance matrices of the two volatility states, has to be estimated. The residuals of the model are assumed to be from the class of elliptical distributions, which includes Gaussian models. The asymptotic null distributions of the test statistics are derived, and simulations are used to explore their small-sample properties. Two empirical examples illustrate the usefulness of the tests in applied work.

A detailed look at crude oil price volatility prediction using macroeconomic variables

AbstractWe investigate whether crude oil price volatility is predictable by conditioning on macroeconomic variables. We consider a large number of predictors, take into account the possibility that relative predictive performance varies over the out‐of‐sample period, and shed light on the economic drivers of crude oil price volatility. Results using monthly data from 1983:M1 to 2018:M12 document that variables related to crude oil production, economic uncertainty and variables that either describe the current stance or provide information about the future state of the economy forecast crude oil price volatility at the population level 1 month ahead. On the other hand, evidence of finite‐sample predictability is very weak. A detailed examination of our out‐of‐sample results using the fluctuation test suggests that this is because relative predictive performance changes drastically over the out‐of‐sample period. The predictive power associated with the more successful macroeconomic variables concentrates around the Great Recession until 2015. They also generate the strongest signal of a decrease in the price of crude oil towards the end of 2008.

Forecasting crude oil price volatility via a HM-EGARCH model

This paper compares uni-regime GARCH-type models, GARCH-type models with Markov and hidden Markov (HM) switching regimes on their forecasting abilities in WTI and Daqing crude oil markets, respectively. Empirical results indicate a HM-EGARCH model outperforms the competitive models, namely the regular GARCH-type models and Markov regime-switching models as well as the other models with hidden Markov regimes through results of six loss functions and the superior predictive ability (SPA) test. More significantly, we find the HM-EGARCH not only performs well in developed crude oil markets, but also in emerging crude oil markets. Therefore, the HM-EGARCH model can be regard as an effective measure of volatility when accounting for different volatility states in the time-changing process.

News sentiment and states of stock return volatility: Evidence from long memory and discrete choice models

This paper examines the impact of public news sentiment on volatility states of firm-level returns. We firstly propose a Markov regime switching fractionally integrated exponential GARCH (MRS-FIEGARCH) model, which is employed to estimate the latent volatility states of intraday stock return. By using the new RavenPack Dow Jones News Analytics database, we fit discrete choice models to investigate the impact of news sentiment on changes of volatility states of the constituent stocks in the Dow Jones Composite Average. Our results demonstrate that sentiments of macroeconomic and firm-specific news can significantly influence the likelihoods of volatility states of intraday stock returns.