Volatility Correlation Research Articles

Volatility interdependence on foreign exchange markets: The contribution of cross-rates

• The volatility interdependence of 21 bilateral exchange rates among 7 major currencies is examined. • We focus on the volatility interdependence between the USD (EUR) rate and its cross-rate. • The spillover index is estimated to identify the direction and magnitude of volatility spillovers. • Wavelet analysis is used to identify localized correlations of volatilities in time-frequency space. • We find significant contributions of cross-rates to the volatility interdependence in FX markets.

Realized higher-order comoments

We propose a new realized third-order comoment and new realized fourth-order joint cumulants, which are standardized comoments. They are obtained from sub-period returns and lower-order comoments and satisfy A. Neuberger’s (Realized skewness. Rev. Financ. Stud., 2012, 25(11), 3423–3455) aggregation property. Different from other realized higher-order comoments obtained from sub-period returns only, those in this study reflect characteristics of the volatility of volatility as well as jump contributions. As a result, our realized kurtosis and coskewness can reflect well-known phenomena such as the positive autocorrelation of volatility or negative correlation between returns and covariances.

Oil-stock Nexus in an Oil-rich Country: Does Geopolitical Risk Matter in Terms of Investment Horizons?

ABSTRACT In this paper, we attempt to delineate the relevance of geopolitical risk in the oil-stock nexus in a time-frequency domain. We resort to various wavelet coherence methods to capture the influence of geopolitical risk on the dynamic association between oil and stock prices in Saudi Arabia as a rich oil-exporting country in a region with high geopolitical risk. We primarily show that the role of geopolitical risk in the oil-stock interplay varies through timescales and investment horizons. News regarding geopolitical tensions affects the stock market in high frequency bands, while oil impacts are manifested more on longer time-horizons. Geopolitical risk weakens oil-stock connectedness in the short term. Interestingly, geopolitical incidents significantly lower the oil-stock magnitude and volatility correlation. These results offer prominent insights for investors and policy makers, which may be beneficial when responding to future geopolitical tensions in terms of risk management and the identification of investment opportunities.

Determinants of international price volatility transmissions: the role of self-sufficiency rates in wheat-importing countries

From the 1980s until the early 2000s, many developing governments adopted market liberalization policies due to relatively low agricultural prices and the implementation of structural programs by the IMF. Yet, a paradigm shift occurred with the emergence of the 2008 food crisis, which directed food-deficit governmental bodies to protectionist regimes supporting higher food self-sufficiency. Despite the importance of food autarky to shield domestic food markets, its effects have never been fully discussed in a formalized econometric framework. This article analyses wheat price volatility transmissions from global to local markets in 10 wheat importing countries, identifying the causality with conditional correlation functions (CCF), the degree of volatility transmissions using generalized autoregressive conditional heteroskedasticity (GARCH) models with the dynamic conditional correlation (DCC) specification and potential determinants with a panel analysis. The main findings reveal that a significant unidirectional Granger causality runs from international wheat price to local retail flour prices for wheat importing countries with an approximate five-month lag, the volatility correlations from international to local markets were strengthened around the period of the 2007–08 food crisis and a higher self-sufficiency rate plays a role in alleviating volatility passthroughs from international markets. This evidence that increasing the SSR of an agricultural commodity is effective in isolating the domestic market is valuable for policymakers in food importing countries. The primary beneficiaries of implementing the policy measure are risk-averse producers and consumers because their utility or welfare improves as the price volatility of foodstuffs declines.

Pricing Vulnerable Options with Stochastic Volatility and Stochastic Interest Rate

This paper considers the pricing issue of vulnerable European options when the price process of the underlying asset follows the GARCH diffusion model with stochastic interest rate. Based on the proposed model, we obtain an approximate solution for the vulnerable European option price via means of Fourier transform. In addition, the Greeks of vulnerable option price are derived explicitly. Besides, the approximate solution of vulnerable option price can be quickly computed by using the fast Fourier transform (FFT) algorithm. The results of Monte Carlo simulations indicate that FFT is accurate, fast and easy to implement. More important, the pricing model also reveals that: (i) a negative correlation of volatility with the spot return creates a fat left tail and thin right tail in the distribution of continuously compounded spot returns. Thus, for in-the-money options, the vulnerable option prices of the proposed model are higher than those of Klein (J Bank Finance 20(7):1211–1229, 1996). While for deep-out-of-the-money options, the vulnerable option prices of the proposed model are smaller; (ii) the higher long-run mean of the underlying asset price’s instantaneous variance, the higher vulnerable option price; (iii) the long-run mean of the stochastic interest rate exerts a positive effect on the value of vulnerable European option.

Volatility spillover impact of world oil prices on leading Asian energy exporting and importing economies’ stock returns

Today Asia accounts for about 40% of the world oil trade and most of the trade is transpired within the region. This study presents an investigation of the volatility spillovers among leading Asian oil exporting countries and oil importing countries’ stock exchanges and crude oil returns. The model selection from multivariant GARCH models include BEKK-GARCH, DCC-GARCH, ABEKK-GARCH and ADCC-GARCH estimation techniques which are applied using daily data from 1st September 2009 to August 31st, 2018 for the three oil exporting countries (Saudi Arabia, United Arab Emirates, Iraq) and four oil importing countries (China, Japan, India, South Korea) stock exchanges with spot crude oil. Further, these estimation results are used to analyze the optimal portfolio weights for oil-stock portfolios. The bidirectional spillover is accepted for the correlation of two oil exporting countries (Saudi Arabia, Iraq) and South Korean stock return with oil returns. the Overall the results of all the oil exporting and oil importing countries show a different level of significant correlation with oil but the influence of oil shock are more pronounced on oil exporting countries. We also present a graphical picture of the stock correlation with oil by employing DCC-GARCH model that shows great shock and volatility correlation in the period of the oil crisis of 2014–2016. According to optimal portfolio weights and hedge ratios, the oil assets are a useful instrument to minimize the portfolio risk in studied markets. The investors, however, should choose more stocks than oil assets to form an optimal portfolio in case of oil importing countries and equal ratio for the oil exporting countries.

Modeling financial time-series with generative adversarial networks

Financial time-series modeling is a challenging problem as it retains various complex statistical properties and the mechanism behind the process is unrevealed to a large extent. In this paper, a deep neural networks based approach, generative adversarial networks (GANs) for financial time-series modeling is presented. GANs learn the properties of data and generate realistic data in a data-driven manner. The GAN model produces a time-series that recovers the statistical properties of financial time-series such as the linear unpredictability, the heavy-tailed price return distribution, volatility clustering, leverage effects, the coarse-fine volatility correlation, and the gain/loss asymmetry.

A Comparative Analysis of Currency Volatility among BRICS Countries

ABSTRACTIn recent years, exchange rates of the BRICS countries have all experienced periods of high volatility. Thus far, no study has simultaneously compared the volatility of the BRICS currencies and analyzed the dependence and causal structure of relative volatility of these peer currencies. We addressed this issue by using monthly data from January 1995 to January 2017. We find that:(i) Brazil, India and China are more competitive than South Africa, on average, while South Africa, in turn, is only more competitive than Russia;(ii) the rand has been more volatile than the Brazilian real and the Russian ruble, but less volatile than the Chinese renminbi and the Indian rupee; (iii) there are inter-currency volatility correlations among the real, renminbi, ruble, and rand;(iv) the renminbi return volatility causes return volatility in the real, ruble, and rand.

Energy and Agricultural Commodity Markets Interaction: An Analysis of Crude Oil, Natural Gas, Corn, Soybean, and Ethanol Prices

This paper broadens the analysis of the interactions between energy and agricultural commodity markets by focusing on five major commodities: oil, natural gas, soybean, corn, and ethanol, and intends to provide more updated information regarding the degree of the connection among the markets. We estimate a DCC-MGARCH model to accommodate the dynamic and changing degree of interconnections among the five markets with respect to price levels and price volatilities. In doing so, we control for additional economic variables including oil and gas inventories, interest rate spread, exchange rate and economic activities. Our empirical evidence suggests that there are varying degrees of interconnections among the energy and agricultural commodities in the long term as well as the short term, but the interactions among the agricultural commodities and ethanol are generally higher than the interactions between oil and gas and agricultural markets. In addition, we reveal some weak evidence of commodity market speculation. The estimated conditional volatility correlations suggest that volatility spillovers among the markets were time dependent and dynamic.

Technical Appendix To: From SA-CCR to RSA-CCR: Making SA-CCR Self-Consistent and Appropriately Risk-Sensitive by Cashflow Decomposition in a 3-Factor Gaussian Market Model

The full-text version of this paper can be found at: https://ssrn.com/abstract=3329538. SA-CCR has major issues including: lack of self-consistency for linear trades; lack of appropriate risk sensitivity (zero positions can have material add-ons; moneyness is ignored); dependence on economically-equivalent confirmations. We show that SA-CCR is, by re-construction, based on a 3-factor Gaussian Market Model with particular calibration to volatility and inter-bucket correlation structure. Hence we proposed RSA-CCR (Berrahoui, Islah and Kenyon, 2019) based on cashflow decomposition and this 3-factor Gaussian Market Model. RSA-CCR solves the issues with SA-CCR for linear trades and is calibrated to SA-CCR apart from avoiding SA-CCR's issues. This Technical appendix provides background material, proofs and technical details behind RSA-CCR not present in the source paper (Berrahoui, Islah and Kenyon 2019). There is some duplication to make this appendix more readable.

Dynamical Volatility and Correlation among US Stock and Treasury Bond Cash and Futures Markets in Presence of Financial Crisis: A Copula Approach

The main purpose of this study is to investigate the dynamic interrelationships among US stock, treasure bond cash and futures markets. To achieve this goal, the VEC copula GJR-GARCH-skewed-t model is formulated to detect these dynamic interactions and their corresponding tail dependence structure. Meanwhile, we also discuss the interactions and contagion effects among these three markets with consideration of two special events such as the subprime mortgage and global financial crisis. By using the VEC copula GJR-GARCH-skewed-t model, this study analyzes the dependence structure and calculates the correlation coefficients between stock-bond cash, stock-bond futures and bond cash and futures during the crisis, and also compared to before and after the crisis event. The empirical results verify that during the crisis events, the correlation coefficients between stock-bond cash and stock-bond futures markets have increased, meanwhile the correlation coefficients between bond cash-futures markets have decreased. The results further point out that the return and volatility correlations among the three markets occur from not just fundamental co-movements, but are also affected by the excess propagation of shocks as contagion effect from the occurrence of negative shocks from financial crisis events.

Market Efficiency and News Dynamics: Evidence from International Equity Markets

This paper examines the efficient market hypothesis by applying monthly data for 15 international equity markets. With the exceptions of Canada and the U.S., the null for the absence of autocorrelations of stock returns is rejected for 13 out of 15 markets. The evidence also rejects the independence of market volatility correlations. The null for testing the absence of correlations between stock returns and lagged news measured by lagged economic policy uncertainty (EPU) is rejected for all markets under investigation. The evidence indicates that a change of lagged EPUs positively predicts conditional variance.

Trends and contagion in WTI and Brent crude oil spot and futures markets - The role of OPEC in the last decade

This article examines the interconnectedness of WTI and Brent prices on different resolutions of price movements. Firstly, within a multivariate BEKK framework we identify high but volatile correlations with recurring highs around 0.8 and multiple periods of decoupling. OPEC meetings increase the correlation in the short run. Secondly, linear ℓ1-trends reveal that long-term movements of WTI and Brent are driven by the same dynamics, confirming the ‘one great pool’ hypothesis. OPEC meetings have only little impact on long-term price trends. Thirdly, we find leading effects of WTI over Brent by short-term trends of several days, especially in a negative direction. These trends have an asymmetrical effect on volatility; negative trends cause a stronger increase than positive trends. These findings are of interest to policy makers as well as hedging strategies of crude oil portfolios and provide insight into long-term movements of crude prices.

Asymmetric Volatility Risk: Evidence from Option Markets*

Abstract Asymmetric volatility concerns the relation of returns to future expected volatility. Much is known from option prices about the marginal risk-neutral distributions (RNDs) of S&P 500 returns and of relative changes in future expected volatility (VIX). While the bivariate RND cannot be inferred from the marginals, we propose a novel identification based on long-dated index options. We estimate the risk-neutral asymmetric volatility implied correlation (AVIC) and find it to be significantly lower than its realized counterpart. We interpret the economics of the asymmetric volatility correlation risk premium and use AVIC to predict returns, volatility, and risk-neutral quantities.

Real estate's information and volatility links with stock, bond and money markets

AbstractWe examine real estate's information and volatility linkages with stock, bond and money markets. Based on the theory that the volatility of prices directly reflects of the rate at which information flows to the market (Kyle, ; Ross, ), we propose that information linkages across markets are revealed in the correlations of their volatilities, rather than correlations of returns. Applying an implied volatility correlation approach and the Generalized Method of Moments (GMM) estimation of Fleming et al. () stochastic volatility model, we find strong information and volatility linkages across the four markets.

From Currency Volatilities to Global Equity Correlations

We derive and empirically test a theoretical link between exchange rate volatility and global equity correlations. Starting with option-implied currency volatilities, we use variants of existing currency models, global capital flows, international parity, the Taylor rule, and some simplifying assumptions to theoretically link foreign exchange options-implied volatilities and future global equity correlations. Using data from January 1999 to June 2015, we test our hypothesis and find that exchange rate implied volatilities — coupled with one-period ex-post correlations — more accurately predict subsequent equity market correlations than other models. Our findings have implications for portfolio diversification, forecast of overall equity portfolio volatility, and portfolio optimization.

Dynamic Volatility Linkages between Developed and Emerging Stock Markets: A MMV-GARCH Approach

This paper investigates the short-run and long-run volatility linkage structures among developed and emerging stock markets using a multivariate multiplicative volatility GARCH (MMV-GARCH) model, which fulfills the assumption of a constant unconditional covariance matrix. The empirical results show that in the long-run volatility linkages, both developed and emerging markets are affected by the subprime mortgage crisis, but the developed stock markets are more sensitive to global financial perturbations. Moreover, the concern that the volatility linkages in stock markets are more significant after the subprime mortgage crisis is unfounded. In the short-run volatility linkages, the developed stock markets present stronger volatility correlation than the emerging markets.

Positive Return–Volatility Correlation and Short Sale Constraints: Evidence from the Chinese Market

AbstractThe market price is a convex function of information when short sales are constrained. Borrowing constraints limit investors to bidding up the price. The two effects imply an asymmetric return–volatility correlation (RVC) when information shifts. We build a model to show that: (i) short selling decreases RVC, while margin trading increases RVC; (ii) RVC increases with disagreement; and (iii) RVC increases with returns. The Chinese stock market is ideal for the empirical test because only certain stocks are eligible for short selling and margin trading in the slow policy adoption process. We obtain evidence to support the theoretical predictions correlation.

Interaction between oil and US dollar exchange rate: nonlinear causality, time-varying influence and structural breaks in volatility

ABSTRACTThis article examines the nonlinear Granger causality and time-varying influence between crude oil prices and the US dollar (USD) exchange rate using the Hiemstra and Jones (HP) test, the Diks and Panchenko (DP) test and the time-varying parameter structural vector autoregression model. By applying the iterated cumulative sums of squares (ICSS) algorithm and the DCC-GARCH model, the effects of structural breaks in volatility of the two markets are also investigated. The empirical analysis indicates that, first, crude oil prices are the nonlinear Granger-cause of the USD exchange rate, but not vice versa. Second, the USD exchange rate exerts a stronger and more stable negative influence on crude oil prices in the short term, and the influence gradually weakens after 2012. Finally, ignoring structural breaks can increase the negative volatility correlation between the oil and USD exchange rate markets, which is particularly remarkable during the financial crisis.

Common and country specific economic uncertainty

We use a factor model with stochastic volatility to decompose the time-varying variance of macroeconomic and financial variables into contributions from country-specific uncertainty and uncertainty common to all countries. We find that the common component plays an important role in driving the time-varying volatility of nominal and financial variables. The cross-country co-movement in volatility of real and financial variables has increased over time with the common component becoming more important over the last decade. Simulations from a two-country DSGE model featuring Epstein-Zin preferences suggest that increased globalisation and trade openness may be the driving force behind the increased cross-country correlation in volatility.