Search

Motivated by the extremely low level of the CBOE VIX accompanied by the high level of U.S. economic policy uncertainty in the period of late 2016 to the end of 2017, we examine the factors affecting the relationship between market volatility and economic policy uncertainty in the United States and the United Kingdom. Our analysis shows that low-quality political signals, higher opinion divergence among investors, and exceptional equity market performance consistently weaken the positive relationship between implied market volatility and policy uncertainty. Our findings help to explain the divergence between the market volatility index and economic policy uncertainty post the 2016 U.S. presidential election and the UK Brexit referendum.

This article aims to uncover the effects of the COVID-19 pandemic on the energy markets in terms of energy stock indexes, energy futures, ETFs, and implied volatility indexes. We model the volatility of energy markets and demonstrate the effects of various phases of the pandemic outbreak (COVID-19) on the energy market. COVID-19-induced uncertainty indicators like the growth of the infection, economic policy uncertainty (EPU), and infectious diseases market volatility (IDsMV) have shown pronounced effects on energy markets’ historical volatility. The volatility of energy ETFs–stocks appears to be more resilient in line with S&P 500 energy stocks. WTI crude oil market has shown an unprecedented overreaction amid pandemic outbreaks and traded with an extreme volatility level. The investors’ sentiment in the energy market was factually higher on the tail events, indicating that fearful investors rushed toward put options and paid an excess premium to protect from unparalleled risk in the energy market.

This paper examines the nonlinear effect of risk aversion, monetary shocks and sentiment changes, on index returns for a set of developed countries. We employ a smooth transition model, in which, these leading stock return drivers are separately considered as a threshold variable. Results show that index returns fall as investors become more risk averse following a positive shock to the volatility index for most of the markets. A restrictive monetary policy negatively affects index returns, in the low regime for some countries, and such an effect turns to be larger in the high regime for more liquid markets. When investors exhibit extreme pessimism or optimism, the market smoothly switches from bear to bull states according to the heterogeneous reactions of market participants and the degree of compensation for taking additional trading risks. Assessment of the predictive performance provides compelling evidence for the supremacy of the model with monetary shocks as a transition variable over competing models.

This article investigates whether the usefulness of fundamentalist signals to predict returns are altered in context of high volatility and also considering the sensitivity of assets to the IVol-BR volatility index. In times of high volatility, investors could make their decisions based on risk aversion and not only on the fundamentals signals of companies. In addition, it is possible to see how different delays in fundamentalist signals are related to future returns. The methodological choice is for estimators in panel data for the analysis of non-financial companies that have shares traded on B3 - Brasil, Bolsa, Balcão - in the period from 2011.3Q to 2018.2Q. The results show evidence of changes in the explanatory capacity of fundamentalist signals in different volatility scenarios, and for different sensitivities to IVol-BR. This finding may impact the decision-making of managers and investors as it enables the design of investment strategies based on fundamentalist signals adhering to different risk scenarios. RESUMO Este artigo investiga se a utilidade dos sinais fundamentalistas para prever o retorno é alterada em contextos de alta volatilidade e considerando ainda a sensibilidade ou não dos ativos ao índice de volatilidade IVol-BR. Em momentos de alta volatilidade, os investidores poderiam tomar suas decisões voltados a uma aversão ao risco e não com base apenas nos fundamentos das empresas. Além disso, verifica-se como diferentes defasagens dos sinais fundamentalistas se relacionam com retornos futuros. A escolha metodológica é de estimadores para dados em painel para análise das empresas não financeiras que têm ações negociadas na B3 - Brasil, Bolsa Balcão - no período de 2011.3T a 2018.2T. Os resultados encontrados mostram indícios de alteração da capacidade explicativa dos sinais fundamentalistas em cenários distintos de volatilidade e para diferentes sensibilidades ao IVol-BR. Esse achado pode impactar a tomada de decisão de gestores e investidores uma vez que viabiliza o delineamento de estratégias de investimento baseadas em sinais fundamentalistas aderentes a cenários distintos de risco.

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 mom...

To forecast the volatility of Chinese stock market, we use information from 28 international markets rather than relying on the Chinese market alone. A common factor, which we call the international volatility index, is constructed by the first principal component of all the 28 cross‐national stock market volatilities. We then add the international volatility index into the prevailing heterogeneous autoregressive model of realized volatility (HAR‐RV). The in‐sample estimation results show that the impact of this common index on future Chinese market volatility is statistically significant and positive. More importantly, the out‐of‐sample forecasting results suggest that our proposed model outperforms competing models including the HAR‐RV, kitchen sink model, and combination approaches. The results are similar when we use a wide range of robustness checks. Furthermore, the international volatility index also yields the highest economic value from an asset allocation perspective.

We contribute to the scarce literature on the oil market volatility index (OVX) by examining the presence of time‐varying jumps in OVX and by assessing the ability of OVX to predict the conditional variance of crude oil returns. Using a GARCH‐jump model, we find evidence that OVX is characterized by jump behaviour that tends to vary over time. Further analysis indicates that accounting for the jump behaviour of OVX helps improve the conditional variance forecasts of crude oil returns. Since the studied features of OVX play a crucial role in asset pricing and risk analyses, our findings have policy implications related to refining volatility prediction models and risk measures.

In this paper, we investigate whether the model-free implied volatility index iVX officially launched by the Shanghai Stock Exchange has incremental explanatory power for future volatility in the S...

This research investigates whether the market sentiment influences firm stock returns with bank loan announcements. We find that CBOE Volatility Index (VIX) is positively related to cumulative abno...

We investigate the valuation of volatility index (VIX) options by developing a model with a self‐exciting Hawkes process that allows for clustering in the VIX. In the proposed framework, we find semianalytical expressions for the characteristic function and forward characteristic function, and then we solve the pricing problem of standard‐start and forward‐start options via the fast Fourier transform. The empirical results provide evidence to support the significance of accounting for volatility clustering when pricing VIX options.

The main goal of this research is to analyse the investment benefits from an incorporation of the volatility exposure to the diversified portfolio from the perspective of a Polish investor. Volatility, treated as a new asset class, may improve the performance of the portfolio due to its negative correlation with most types of assets. This topic has been widely investigated for the United States and Europe whereas the Polish market appears to be not heavily researched and this study may fill this gap. The research covers the period from October 2010 to July 2018 and is performed on daily close prices. To construct the portfolios the analysis uses the mean-variance framework and the naïve diversification approach. The comparison of risk-adjusted returns between investments with and without volatility exposure enables an answer to the research question about an improvement of the results by the addition of a non-standard asset to the diversified portfolios. The VXX is considered as the proxy for volatility as it is the most popular ETN which follows the volatility index derivatives with the given maturity. To test the robustness of the results the portfolios are constructed with a broad range of different parameters and assumptions imposed on the optimization procedure.

This paper focuses on the 2008-2020 period during which two major crises, affecting the economy and the financial markets, occurred. Between 2008 and 2020, there were less extreme tail events, including the lingering Eurozone and Greece crises. In particular, after extremely high stock market volatility and volatility of volatility (VoV) during 2008, the long-run average volatility declined to about 20% and the VoV to around 100%. This paper analyzes this period through the lens of risk and ambiguity (uncertainty). It aims to address the question: what are the financial markets that trade risk - the volatility derivatives markets - telling us? To this end, this paper uses several measures of uncertainty. It reviews the history of volatility and uncertainty measures and discusses their informativeness. It then discusses the information derived from volatility derivatives. © 2022 World Scientific Publishing Company. Midwest Finance Association.

In the recent time, there has been increasing importance of tourism development to the global economic dynamics inspite of the global uncertainties. In this regard, the current study is aimed to find out if the volatility index (VIX) affects the returns of the firms operating in the tourism sector in 11 countries. The relationship between the variables in the study was tested through causality and cointegration tests. As a result, the change in the VIX was found to have causality towards the change in the tourism indices of the countries except for the USA and Sri Lanka. In addition, it was found that there was a long-term relationship between the variables and that the increase in VIX caused a decrease in the return of tourism indices. Hence, the current study offers significant policy direction for the tourism industry operations and the government of the examined destination countries.

This study examines whether the volatility index, a proxy for aggregate volatility risk, can be used as an additional factor in the standard asset pricing model for the Indian stock market after controlling for well-documented risk factors. This study first examines the empirical performance of the capital asset pricing model, the Fama-French three-factor model and the Carhart four-factor model. On the basis of a GRS test, the Fama-French three-factor model is used as the baseline model to explore the role of the volatility index as an additional factor considered in the asset pricing model. The factor mimicking portfolio returns for the volatility index innovations is constructed and is included as an additional factor in the Fama-French three-factor model. The study provides strong evidence of size effects but relatively weak value-growth and momentum effect. Further, stock’s sensitivity to volatility index innovation is a priced risk factor during high volatility period, but not during low volatility period. Nevertheless, the addition of volatility risk factor leads to marginal improvements in the capacity for the model to explain the variation in stock returns. The results of this study will help financial analysts and corporate financial managers to evaluate the performance of professionally managed portfolios.

This paper analyzes the impact of intraday trading activity on option prices in the Volatility Index (VIX) options market. Our results show that there is a temporal relationship between net buying pressure (NBP) and changes in implied volatility of VIX options. Moreover, an increase in NBPs lowers the next‐day delta‐hedged option returns. Using several measures proxying for limits to arbitrage, the average levels of the implied volatility curve rise when limits to arbitrage are severe. A trading strategy in the VIX futures market constructed by using the NBP generates an average annualized return of 10.09%.

In this article, we examine the information linkages of the forward-looking measure of volatility, the volatility index (VIX), for underlying equity market indices of BRICS countries—Brazil, Russia, India, China and South Africa. A study of the information transmission process confirmed a long-run equilibrium relationship between pairs of BRICS countries. The multivariate generalised autoregressive conditional heteroscedasticity (MGARCH) model revealed strong intertemporal linkages between sample VIX. Return and volatility spill-over matrix show the varying degree of connectedness of BRICS VIX across the study period. This study contributes to the international finance literature and has important implications for investors, portfolio managers, policymakers and academia. JEL Classification: C58, F36, G11, G14, G15

We use unique intraday data to investigate the validity of the Shanghai Stock Exchange's the revised Chinese implied volatility index (iVX). We find that iVX is an effective barometer for the underlying exchange‐traded fund (ETF) market and can be used as a valid “fear index” when there is anxiety over large drops. Furthermore, we use robust quantile regressions and document the asymmetric relation between returns and iVX changes. We also show that behavioral theories offer better explanations for this asymmetric relation than do fundamental theories. More important, we examine the role of iVX in selecting trading strategies.

We develop a new generalized autoregressive conditional heteroskedasticity (GARCH) model that accounts for the information spillover between two markets. This model is used to detect the usefulness of the CBOE volatility index (VIX) for improving the performance of volatility forecasting and option pricing. We find the significant ability of VIX to predict stock volatility both in‐sample and out‐of‐sample. VIX information also helps to greatly reduce the option pricing error. The proposed volatility spillover GARCH model performs better than the related approaches proposed by Kanniainen et al. (2014, J Bank Finance, 43, pp. 200‐211) and P. Christoffersen et al. (2014, J Financ Quant Anal, 49, pp. 663–697).

In this study, we investigate how the volatility index (VIX) and oil price influence the foreign exchange rate based on a conditional autoregressive value at risk model. We find that the oil price affects the value at risk (VaR) of exchange rates of oil-importing and oil-exporting countries differently. Further, the VIX as a volatility measure can only influence the tail risk of these currencies when the US financial market fluctuates significantly. In addition, we find that there is a significant increase in the volatility of the VaR of these currencies after the financial crisis. Our empirical results would provide useful information for investors.

This study explores the relationships between changes in the fear index (VIX) and changes in emerging market volatilities i.e., Chinese, Brazilian and the overall emerging volatility index, across their conditional distributions by employing a mixed Quantile regression - Copula methodological approach. Moreover, we analyze whether emerging market volatility indices would respond asymmetrically to positive and negative volatility shocks in the fear index i.e., whether the relationships are asymmetric between the VIX and the emerging market volatilities. Our results confirm that there are strong positive relationships between changes in the VIX and emerging market volatilities, and the linkages tend to be stronger for the upper-parts of the conditional distributions, namely above the median-quantiles up to the extreme-quantiles. In all cases, the nature of the relationship appears to be contemporaneous and on average is three times stronger than their lagged relationship. Further test results reveal that the relationship is highly asymmetric i.e., the effect of a positive shock in the VIX is on average about twice more pronounced than the effect of a negative shock at the extreme-tails of their conditional distributions, a stylized fact that cannot be revealed via conventional estimation methods as OLS. If we compare the effects of positive and negative VIX shocks on emerging market volatilities utilizing QRM, Copulas and OLS, our findings reveal that the effect of a positive shock by the QRM at the 95% quantile is about eight times higher than the one revealed by OLS. An exhaustive robustness analysis is also performed with respect to other volatility measures.

The SKEW index of the Chicago Board Options Exchange (CBOE), launched in February 2011, measures the tail risk not fully captured by the VIX index. In this paper we introduce, for the first time, a...

The volatility index is the implied volatility calculated inversely from the option prices. This study investigates whether the official Chinese volatility index, iVX, can represent investor sentiment. In order to describe investor sentiment comprehensively, we build a three-dimensional investor sentiment measurement system composed of macro, meso and micro level, and decompose iVX into three components to obtain short-term, medium-term fluctuations and long-term trend by EEMD method. The relationships between iVX, its components and sentiment indexes at each level have been analyzed separately, and the empirical results reveal all components of iVX can reflect the investor sentiment at the corresponding level but to which extent they can reflect are not the same. Further we introduce the mixed-frequency dynamic factor analysis to extract the common sentiment factor, which shows stronger correlation with contemporaneous iVX, compared with the sentiment indexes at each level. The ADL model in robustness check also demonstrates the results. Our findings confirm iVX can represent the common sentiment and expectations of Chinese investors in different time scales., Highlights • A three-dimensional investor sentiment measurement system composed of macro, meso and micro level is built. • iVX is decomposed into three components to obtain short-term, medium-term fluctuations and long-term trend. • Mixed-frequency dynamic factor analysis is introduced to extract the common sentiment factor. • The relationships between iVX, its components and sentiment indexes at each level have been analyzed separately.

Economic policy drives investment, production, employment, and other macroeconomic indicators of the economy. The study examines the equity, commodity, interest rates, and currency markets, taking into consideration the US economic policy uncertainty (EPU) index. The present work determines the association among policy uncertainty and volatility index, expressed in terms of generalized autoregressive conditional heteroscedasticity and period of empirical work spanning from 2000 to 2018. The results suggest that equity markets’ volatility tends to be very high based on a high degree of policy uncertainty. The findings on the commodity market indicate that crude oil and gold prices remain more volatile during the presidential election and financial crisis. One of the essential results shows that the 2000s boom, early credit crunch, Lehman’s collapse and recession, and fiscal policy battles have significantly affected the equity, currency, and commodity markets. The interest rates and currency markets have responded considerably to Feds’ and EPU index. The empirical outcome provides evidence that implied volatility index is a forward looking expectation of future stock market volatility, and it uncovers that policy uncertainty affects investor sentiment. The present work holds some practical implications for the government to formulate policies to regulate the US market.

Figuerola-Ferretti thanks the Spanish Ministry of Education and Science for support under grants MICINN ECO2010-19357, ECO2012-36559 and ECO2013-46395, and McCrorie, The Carnegie Trust for the Universities of Scotland under grant no. 31935. This paper provides an analysis of oil prices during and in the aftermath of the Global Financial Crisis, concentrating on the 2007-08 price spike and the 2014-16 price decline. The mildly explosive/multiple bubbles testing strategy by Phillips, Shi and Yu (2015, International Economic Review 56(4), 1043-1133) is used to test for price departures from an underlying stochastic trend and to assess whether any such departures can be explained by fundamentals or other proxy variables. The test dates two significant time periods in both Brent and WTI nominal and real front-month futures prices: a mildly explosive episode during the 2007-08 spike, prior to the peak of the Global Financial Crisis; and a significantly shorter, negative such episode during the 2014-16 price decline, whose commencement is dated around a key OPEC meeting in November 2014. Evidence using other commodity prices points to explanatory factors beyond commodity markets. A global economic activity proxy is found to be decisive in the episode in mid-2008; excess speculation is not. U.S. shale oil production, though contributing to the post-June 2014 price decline, is not seen to have been decisive. Against some recent work tying the CBOE Volatility Index (VIX) to oil futures prices, we find no evidence that the VIX decisively affected oil price levels during the sample period. The results are compared and contrasted with those obtained by Baumeister and Kilian (2016, Journal of the Association of Environmental and Resource Economists 3, 131-158) via a forecasting approach based on a structural vector autoregressive model without financial variables. Taken altogether, the results herein provide new evidence based on formal statistical testing that helps resolve a number of recent controversies in the oil price literature. Postprint

This study uses multiple maturity‐independent variables to examine whether the volatility information implied in the term structure of volatility index can improve the prediction of realized volatility. The empirical results for the S&P 500 index show that, in terms of both the in‐sample estimation and out‐of‐sample forecasting, the term structure variables provide substantial incremental contribution to the models with only level variables. Our empirical results are robust to various forms of volatility, alternative ways to develop the term structure variable, the impact of macroeconomic variables, and alternative underlying assets.

We show that market volatility of volatility is a significant risk factor that affects index and volatility index option returns, beyond volatility itself. The volatility and volatility of volatility indices, identified model-free as the VIX and VVIX, respectively, are only weakly related to each other. Delta-hedged index and VIX option returns are negative on average and are more negative for strategies that are more exposed to volatility and volatility-of-volatility risks. Further, volatility and volatility of volatility significantly negatively predict future delta-hedged option payoffs. The evidence suggests that volatility and volatility-of-volatility risks are jointly priced and have negative market prices of risk.

This paper proposes a general but tractable approach to price volatility derivatives which includes most existing stochastic volatility models as special cases. Specifically, we use the generalized beta prime distribution to model the dynamics of the volatility underlying, which is flexible enough to capture the empirical marginal density function and yield desired implied volatility slope consistent with empirical data. Based on that, we use a Jacobi process to add a diffusion component, and further extend our model through stochastic time changes to capture jumps as well as to accommodate the term structure of volatility index futures. Our approach is supported by calibrations.

We investigated the cross-market relations of volatility indexes with U.S. and non-U.S. stock market returns. We found that the pervasive VIX influence at both U.S. and non-U.S. stock markets. The ...

International audience; We investigate the conditional cross effects and volatility spillover between equity markets and commodity markets (oil and gold), Fama and French HML and SMB factors, volatility index (VIX) and bonds using different multivariate GARCH specifications considering the potential asymmetry and persistence behaviours. We analyse the dynamic conditional correlation between the US equity market and a set of commodity prices and risk factors to forecast the transmission of shock to the equity market firstly, and to determine and compare the optimal hedge ratios from the different models based on the hedging effectiveness of each model. Our findings suggest that all models confirm the significant returns and volatility spillovers. More importantly, we find that GO-GARCH is the best-fit model for modelling the joint dynamics of different financial variables. The results of the current study have implications for investors: (i) the equity market displays inverted dynamics with the volatility index suggesting strong evidence of diversification benefit; (ii) of the hedging assets gold appears the best hedge for the US equity market as it has a higher hedge effectiveness than oil and bonds over time; and (iii) despite these important results, a better hedge may be obtained by using well-selected firm sized and profitability-based portfolios.

This paper aims to investigate whether investor fear gauge (IFG) contains incremental information content for forecasting the volatility of crude oil futures. For this purpose, we use oil volatility index (OVX) to measure the IFG. Adding the IFG to existing heterogeneous autoregressive (HAR) models, we develop many HAR models with IFG. Subsequently, we employ these HAR models to predict the volatility of crude oil futures. The results from the parameter estimation and out-of-sample forecasting show that the in-sample and out-of-sample performances of HAR models with IFG are significantly better than their corresponding HAR models without IFG. The results are robust in different ways. Thus, the HAR models with IFG are more beneficial to the decision making of all participants (including financial traders, manufacturers and policymakers) in the crude oil futures market. More importantly, the results suggest that the investor fear gauge has a significant positive effect on volatility forecasting, and can help improve the performances of almost all the existing HAR models.

The crude oil volatility index (OVX) is a direct and more accurate measure of oil price uncertainty. This paper uses this kind of implied volatility index of oil prices to investigate the impacts of oil price uncertainty on the aggregate and sectoral stock returns in China. This issue is resolved by using a quantile regression, which can provide a more detailed examination under different market conditions. Meanwhile, the asymmetric effects of uncertainty shocks are also examined by using the positive and negative changes of the OVX. Furthermore, we assess whether the reform of March 27, 2013 affected the OVX-stock nexus since this reform was a major step to relax the control of domestic oil prices in China. Our results reveal that OVX changes mainly show significantly negative effects on the aggregate and sectoral stock returns in the bearish market. In particular, these effects depend largely on the positive shocks of the OVX rather than the negative shocks of the OVX. Moreover, the reform of March 27, 2013 decreased the impacts of the positive OVX shocks on Chinese stock returns.

Derivatives based on the VIX index and related indexes in the U.S. and around the world have proliferated enormously in the last few years. This article reviews the behavior of VIX-like indexes in 14 markets in 8 countries. Eleven are stock indexes, 2 are commodities, and the last is the USD–EUR exchange rate. A simple GARCH-family model for the change and volatility of the index is fitted to index returns, implied volatility (i.e., lagged IV), and the U.S. VIX (as a proxy for global volatility conditions). Separate coefficients are estimated for positive and negative variable values, which reveals that negative market returns cause sharp and immediate increases in the volatility index, but positive returns reduce implied volatility by a lesser amount and the effect is spread out over time. Including lagged factors from the previous day was important, especially for the smaller markets. The U.S. VIX was found to influence all of the other markets, thus suggesting the existence of a global volatility factor that can be proxied by the VIX, and the evidence indicates that volatility appears to spill over from the first-tier markets to the smaller ones.

This paper investigates the effect of scheduled news announcements on two energy-related volatility indices calculated by the Chicago Board of Options Exchange. These volatility indices are the crude oil volatility index (OVX) and the energy sector volatility index (VXXLE), which measure the market's expectation of 30-day volatility of crude oil prices and energy sector returns, respectively. We find that energy-related volatility indices tend to fall following several US macroeconomic news releases and OPEC meetings. Put differently, these news releases resolve investors' uncertainty about the future development of energy-related markets. Our results also suggest that macroeconomic releases and oil-related events do not seem to exert significantly different impacts on energy market uncertainty. We further document that the observed announcement day patterns cannot be exploited to generate significant returns using variance swaps once we incorporate transaction costs. Our findings are relevant because there are currently no other volatility derivatives traded in exchange markets, such as options or futures, that offer investors pure exposure to energy-related volatility alone as variance swaps do.

In this paper we show why a Real Estate VIX is needed. We develop a 30-day forward-looking real estate volatility index (REVIX), based on a state preference approach using US equity Real Estate Investment Trusts (REITs), for the new Real Estate Sector under the Global Industry Classification Standard (GICS). We show that REVIX is a very useful predictor of future REIT realized volatility. We further explore an economic application of REVIX and demonstrate that REVIX, similar to VIX, serves as an investor fear gauge for the real estate market.

To the best of our knowledge, this is the initial study to investigate the predictive power of crude oil volatility index (OVX), a measure of oil market uncertainty, in explaining the return structure of industrial and precious metal markets. Applying different forms of the GARCH-jump model, we document the following major findings. First, we report a significant price spillover running from oil market to industrial metal sector. In addition, jumps do exist in the underlying metal market returns and such jumps are time-varying. Second, we do not find any evidence that oil volatility shocks impact the aggregate precious metal market. However, such effect is present at least in the silver market, if we disaggregate the data. Third, while examining the impact of global financial crisis on the association between oil and metal markets, we report that the effects of OVX hold for both crisis and post crisis periods. Finally, we document the existence of asymmetry in the linkages between oil and the industrial metal markets. To be specific, spillovers for the positive oil volatility shocks appear to be larger than that for the negative oil volatility shocks confirming the existence of uneven spillover effects.

This paper uses the nonlinear autoregressive distributed lag (NARDL) model to analyze the different performances of Bitcoin and gold under the impacts of three different uncertainties, namely global economic policy uncertainty (GEPU), US stock market volatility index (VIX) and the CBOE crude oil ETF volatility index (OVX). The results indicate that faced with shocks of different uncertainties, Bitcoin is unable to serve as a safe-haven, while gold can hedge against uncertainties to varying degrees. Moreover, the three types of uncertainties have asymmetric impacts on the prices of Bitcoin and gold respectively. The decrease of uncertainties has a greater impact on Bitcoin price than the increase, while the increase of uncertainties has a greater impact on gold price than the decrease. It suggests that investors are cautious and optimistic about Bitcoin, and gold remains unanimously recognized as the traditional safe-haven.

This paper provides new insights into the relationship between the crude oil volatility index (OVX) and Chinese stock returns by refining and extending the latest research of Xiao et al. (2018). Firstly, we refined the evaluation of the impact of OVX changes on stock returns by introducing nonparametric function setting, a technique robust to parametric form assumptions of Xiao et al. (2018). Secondly, we extend the original data sets from May 10, 2007, to April 9, 2020, to assess the consistency of the estimated results. Thirdly, we apply the most recent econometric model, the semiparametric additive quantile regression proposed by Fasiolo et al. (2020), instead of the linear quantile regression used in Xiao et al. (2018) to explore the potential nonlinear relationships between OVX shocks and stock returns from the asymmetric and heterogeneous perspectives. Fourthly, we investigate whether the COVID-19 pandemic mediated the OVX–stock connection, because this pandemic has had a tremendous impact on global financial markets. Our results reveal that the implications of OVX shocks on stock returns are nonlinear and dynamic and exhibit high asymmetry and heterogeneity. In particular, the positive OVX changes have a “U-shaped” effect on Chinese stock rewards in a bearish market, rather than the linear negative effect reported by Xiao et al. (2018). Moreover, the COVID-19 pandemic amplified the consequences of the positive and negative OVX variations on Chinese stock returns.

We investigate the effect of Japan's Monetary Policy Meeting releases on the intraday dynamics of the Nikkei Stock Average Volatility Index and its futures during pleasant and unpleasant weather. We show that at the time of a monetary policy release when the temperature is pleasant, there is a significant decline in Japanese equities' implied volatility and futures, which lasts for about 10 min and 5 min, respectively. This decline is longer and exhibits a greater variation when releases occur during cold days. Finally, we emphasize the achievable economic profits and losses, given the reaction of Nikkei VI futures to the Japanese monetary policy releases during pleasant and unpleasant weather days, respectively. In particular, taking a short position at the start of the trading day on pleasant days and closing this position at the end of the trading day generates an average annual return of 5.6%.

This study investigates the risk level in the oil market measured by Value-at-Risk (VaR) and conditional VaR (CoVaR), as well as the dynamic and asymmetric dependence between WTI returns and crude oil volatility index (OVX), by constructing six time-varying mixed copula models. Results show that mixed copula between t copula and the 270-degree rotated Clayton copula is the optimal fitting copula to measure dynamic dependence. The estimated time-varying Kendall coefficients indicate that WTI returns and OVX present negative dependence most of the time. There exists a structural change point of dependence between WTI returns and OVX changes on April 17, 2009, while the dependence characteristics within the subsamples are similar to that in the whole sample, indicating the rationality of our time-varying mixed copula models. Finally, the tests show significant risk spillover from OVX to WTI returns and also asymmetric effects for CoVaRs in response to different upside and downside extreme OVX movements.

At the settlement time of the VIX Volatility Index, volume spikes on SP editorial decision June 19, 2017 by Editor Robin Greenwood.

This study provides a consistent and efficient pricing method for both Standard & Poor's 500 Index (SPX) options and the Chicago Board Options Exchange's Volatility Index (VIX) options under a multiscale stochastic volatility model. To capture the multiscale volatility of the financial market, our model adds a fast scale factor to the well-known Heston volatility and we derive approximate analytic pricing formulas for the options under the model. The analytic tractability can greatly improve the efficiency of calibration compared to fitting procedures with the finite difference method or Monte Carlo simulation. Our experiment using options data from 2016 to 2018 shows that the model reduces the errors on the training sets of the SPX and VIX options by 9.9% and 13.2%, respectively, and decreases the errors on the test sets of the SPX and VIX options by 13.0\% and 16.5\%, respectively, compared to the single-scale model of Heston. The error reduction is possible because the additional factor reflects short-term impacts on the market, which is difficult to achieve with only one factor. It highlights the necessity of modeling multiscale volatility., 16 pages, 5 figures, 2 tables

This paper uses bivariate and multivariate wavelet approaches to revisit the co-movements between the Volatility Uncertainty Index (VIX) and Bitcoin (BTC). This is achieved by accounting for the impacts of the three major global factors, namely the U.S. Economic Policy Uncertainty Index (EPU), the Crude Oil Volatility Index (OVX), and the Geopolitical Risk Index (GPR). To do this, we use Wavelet Coherence (WC), Cross Wavelet Transform (CWT), Power Wavelet Coherence (PWC), and Multiple Wavelet Coherence (MWC) approaches. The results show that the BTC-VIX relationship varies across time and at high and low frequencies. Moreover, we find positive (or in-phase) co-movements between both variables while a negative co-movement (out-of-phase) is observed at both high and low frequencies. VIX news has a prediction power on BTC price returns over different frequencies. The results of PWC and MWC show that OVX, EPU, and GPR factors affect the BTC-VIX nexus under different frequencies. Finally, correlations between BTC-uncertainty indices are dependent upon investment horizons. The results of our research are of interest and importance to investors, portfolio managers, and policy-makers, as the results have practical applications to inform their decision-making. - 2019 Elsevier Inc. Scopus

This paper investigates the net directional and total volatility spillovers of A- and B-shares in the Chinese stock market. Using the framework proposed by Diebold and Yilmaz (2012, 2014), our empirical results show a net exporter of volatility associated with uncertainty shocks from A-shares to B-shares. Moreover, the uncertainty indices measured by the total volatility spillover index and the global volatility index exert a nonlinear effect on subsequent Chinese stock index returns. Finally, our findings indicate that for explaining Chinese stock index returns, the total volatility spillover index provides more useful information than the global volatility index.

Modern derivatives pricing models develop pricing relationships from the principle of no-arbitrage, assuming that if an arbitrage opportunity were to arise in the market, it would be immediately eliminated by active arbitrageurs. With this assumption, the supply curve for the derivative security is infinitely elastic at the model price, so that effects of fluctuation in supply and demand from non-arbitrageurs are suppressed. In particular, flows of funds into and out of mutual funds and ETFs should not affect market prices for the underlying securities. This article explores whether that assumption holds in the real world for ETFs based on the VIX volatility index, and finds that it doesn’t. Examining 13 volatility funds separated into four groups according to whether the focus is on nearby or longer horizons and whether volatility exposure is long or short, the authors show that fund flows respond to observed market volatility, flowing into long (short) exposure funds when the market is calm (volatile). Moreover, the VIX responds to fund flows, rising (falling) when money is flowing into long (short) exposure funds. But the evidence shows that this effect is no stronger, i.e., not unusually destabilizing, in periods of high volatility.

We propose a closed-form pricing formula for the Chicago Board Options Exchange Volatility Index (CBOE VIX) futures based on the classic discrete-time Heston–Nandi GARCH model. The parameters are estimated using several sets of data, including the S&P 500 returns, the CBOE VIX, VIX futures prices and combinations of these data sources. Based on the resulting empirical pricing performances, we recommend the use of both VIX and VIX futures prices for a joint estimation of model parameters. Such estimation method can effectively capture the variations of the market VIX and the VIX futures prices simultaneously for both in-sample and out-of-sample analysis. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark 37:641–659, 2017

This paper examines the forecasting power of the volatility index of Canada (VIXC) and GARCH-family volatility. Specifically, this paper is motivated by an enquiry into how well volatility estimators predict volatility changes. To this end, we use a daily series of VIXC from 1 October 2009 through 30 April 2015. To estimate out-of-sample parameters for GARCH volatilities, a series of daily returns of the TSX60 since 29 November 2002 is used roll-forwardly. Then we run the forecasting regressions for a full-sample and five subsamples grouped by the daily percentage change in realised volatility, and compare and the loss functions to assess the forecasting power of volatility estimators. Additionally, this paper proposes a new measurement called mean-directional-error (MDE), which can comprehensively evaluate the forecast error in both direction and size of movement. The key findings of this paper can be summarised as follows: even though the information content of VIXC is highest on average and int...

This paper proposes a methodology using VIX futures as an investment asset while controlling downside risk. For this purpose, three portfolio insurance ( PI) strategies are built by using option-based portfolio insurance ( OBPI) and constant proportion ( CPPI) for VIX futures. The effectiveness of the strategy is tested by historical return simulation of eight subsamples and a full sample for the period of Feb. 2007–Jan. 2015. We evaluate the performance of each strategy first as a pure investment tool and then as a diversification tool for S&P500 index. In the subsample simulation, all PI strategies perfectly protect its floor. Protective Put and CPPI appropriately catch up with strong and trendy bull markets of VIX futures. Resetting achieves a considerable return for the periods of return-reversal. In the full-sample simulation, the daily mean returns of the PI strategy are all greater than the benchmark's. The PI strategy is also a good diversification tool for S&P500 index.

VIX (CBOE Volatility Index) is often called the “fear index.” Using monthly returns from January 2000 to December 2011 as their data sample, the authors find that when the change in VIX is positive, large-capitalization stocks (the SP and when the change in VIX is negative, small-cap stocks outperform. Furthermore, the statistical significance of the change in VIX is substantially greater than SIZE (the natural logarithm of the market capitalization). The authors argue that the benefit of owning a small-cap stock depends on the distribution of the change in VIX in any given period. During the period July 2007–March 2009, both levels of the large-cap index (S&P 500) and the small-cap index (S&P 600) had a maximum drawdown over 50%, and the small-cap suffered much more than the large-cap. This huge drawdown demonstrates the importance of the effect of VIX on the cross section of stock returns

This study examines the usefulness of incorporating investor sentiment, weather, and catastrophe effects into the benchmark volatility model for an effective hedging strategy in the Taiwan options market. The empirical results indicate that investor sentiment, as measured by the option volatility index (VIX) and put-call open interest ratio (PCO), and the catastrophic factors of earthquakes (EQ) can help explain realized volatility and that the PCO has the best predictive ability. Incorporating investor sentiment and weather effects improves the hedging performance of options. VIX and changes in cloud cover (ΔCC) have significant improvement level for hedging performance, the highest of which are 0.44% and 5.36%, respectively.

International audience; This paper proposes a new empirical methodology for computing a cross-market volatility index – coined CMIX – based on the Factor DCC-model, implemented on volatility surprises. This approach solves both problems of treating high-dimensional data and estimating time-varying conditional correlations. We provide an application to a multi-asset market data composed of equities, bonds, foreign exchange rates and commodities during 1983–2013. This new methodology may be attractive to asset managers, since it provides a simple way to hedge multi-asset portfolios with derivative contracts written on the CMIX.