Oil Volatility Index Research Articles

Diversifying crude oil price risk with crude oil volatility index: The role of volatility-of-volatility

To understand the diversification benefit of crude oil volatility, we examine the return-volatility relation in the crude oil market, given the interaction of the volatility (VOL) and the volatility-of-volatility (VOV). We develop a novel empirical model of the crude oil price and crude oil volatility index (OVX) returns incorporating both time-varying and state-dependent variances and correlations, thus allowing us to identify distinct market regimes of VOL and VOV. We find that the behavior of the return-volatility relation is contingent on the prevailing VOV regimes. Specifically, in a low (high) VOV regime, the relation becomes less (more) negative as VOL increases. These empirical results therefore imply that the diversification benefit of crude oil volatility is far from uniform across the different market states. Finally, using our proposed empirical model, we demonstrate the economic significance of recognizing both the time-varying and state-dependent variances/correlations in portfolio risk forecasting and construction.

Volatility dynamics of agricultural futures markets under uncertainties

The objective of this study is to examine the effect of various uncertainty measures on the realized volatility of agricultural futures markets. In doing so, we use a range of uncertainty indicators in our analysis to investigate whether news-based uncertainty measures (e.g., geopolitical risk and economic policy uncertainty) have better predictive contents than the market-based uncertainty measures (e.g., crude oil volatility index, the US equity market VIX and exchange rate VIX). This comparison is important given that employing both measures has some specific benefits. Methodologically, we consider the application of the LASSO (least absolute shrinkage and selection operator) method as well as the heterogenous autoregressive (HAR) process. The in-sample estimates indicate that among the various news-based and market-based risk measures the latter provide better forecasts for the realized volatility of agricultural futures markets. The out-of-sample forecasts also confirm the same with the LASSO method outperforming the HAR process.

WTI, Brent or implied volatility index: Perspective of volatility spillover from oil market to Chinese stock market.

This study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.

The role of investors’ fear in crude oil volatility forecasting

We study whether investors’ fear can predict oil price volatility. The proxies used for investors’ fear are the CBOE Crude Oil Volatility Index (OVX), Google searches for “oil price”, and United States Oil Fund (USO) trading volume. In the in-sample analysis, we find that increased OVX, increased Google searches, and increased trading volume predict increased oil price volatility. Additionally, we find bidirectional Granger-causalities between volatility and OVX, volatility and Google searches, and volatility and trading volume. However, results are very different for the out-of-sample forecasts. We incorporate OVX, Google searches for “oil price”, trading volume, and their combinations into commonly used volatility models but find that these variables or their combinations improve none of the models in terms of out-of-sample forecasting.

Hybrid forecasting of crude oil volatility index: The cross‐market effects of stock market jumps

AbstractFrom the cross‐market perspective, this paper investigates crude oil volatility index (OVX) forecasts by proposing a hybrid method, which combines the data‐driven SVR technique and parametric models. In terms of parametric models, we utilize GARCH‐type models with jumps, and the forecasting effects of five non‐parametric jumps (including interday and intraday jump tests) of stock market are also explored. Empirical results show that our approach can substantially increase forecasting accuracy. In addition, the model confidence set test and robust test reaffirm the superiority of the novel hybrid method. From the assessment of economic significance, the advantages of the hybrid method for volatility index forecasting are further confirmed. All these findings imply that jumps of stock market can be helpful in forecasting OVX, especially after the introduction of the hybrid method. Our work can certainly provide a new insight for volatility forecasting and cross‐market research.

Quantile frequency connectedness between crude oil volatility, geopolitical risk and major agriculture and livestock markets

ABSTRACT This article investigates the quantile frequency risk connectedness between the global geopolitical risk (GPR) index, the CBOE Crude Oil Volatility Index (OVX), and the major agricultural and livestock indexes using a quantile vector autoregression (QVAR)-based frequency connectedness approach. The empirical results suggest that spillover effects are higher in extreme market conditions than in normal market conditions. The GPR has a more significant impact on other markets in the long-term domain of extreme market conditions. Moreover, grain markets are the primary transmitters of spillovers, while OVX predominantly assumes the role of a net receiver of risk spillover effects in most instances. Meanwhile, although the spillover structure between frequencies is homogeneous at the same quantile level, the connection between major agricultural and livestock index markets and two uncertainty indexes is time-varying, and spillovers are heterogeneous at different quantile levels. Our research provides investors and policymakers with new understandings of the development of differentiated crude oil volatility and geopolitical risk aversion strategies under different market conditions, given the heterogeneity of volatility spillover connectedness under different market conditions and frequency domains.

Impact of Crypto Assets as Risk Diversifiers: A VAR-based Analysis of Portfolio Risk Reduction

This research aims to empirically investigate the portfolio risk associated with crypto assets. In other words, we want to investigate whether the inclusion of crypto assets in a portfolio can minimize the portfolio risk or not, because it is argued that there is a lower degree of correlation between crypto assets and traditional assets. In order to achieve our research objectives, we employ the Vector Autoregressive Model (VAR) by using five different asset classes. The first two variables are taken from the crypto assets, Bitcoin and Ethereum, and the remaining three variables for Gold, Crude Oil and VIX (Chicago Board Options Exchange's (CBOE) volatility index). Our research strategy will be based on an analysis for unit root, optimal lag selection, coefficient matrix, checking VAR stability, the Granger causality test, and impulse response function (IRF). Our findings suggest that none of the indicators of traditional assets drive and explain Bitcoin. We also found that only Bitcoin is significantly related to Ethereum. while none of the other variables are statistically useful to explain the variation in the Ethereum. Based on these findings it can be recommended that the inclusion of crypto assets into a portfolio reduces risk because none of the indicators of crypto assets are significantly related to the indicators of traditional assets.

Crude oil volatility index forecasting: New evidence based on positive and negative jumps from Chinese stock market

This article investigates the crude oil volatility index (OVX) forecasting from the perspective of cross-market asymmetric effects of Chinese stock market jumps. We calculate six kinds of positive and negative jumps based on the high-frequency data of stock returns which are used to represent the asymmetric shocks of stock markets. Principal component analysis (PCA) and momentum of predictability (MoP) strategy are employed separately to synthesize the information of asymmetric jumps. Our empirical results find that considering the positive and negative jumps in Chinese stock market helps to improve the forecasting ability of OVX, especially under the MoP strategy. The out-of-sample model confidence set (MCS) tests and Diebold-Mariano (DM) tests, the evaluation of economic significance and the robustness tests further verify our results.

Does oil price volatility matter for the US transportation industry?

Although the US transport sector is one of the major users of fossil fuel (e.g., crude oil), the impact of energy price volatility on transport stock sector indexes remains under-researched. The present study addresses this research void by investigating the impact of energy implied volatility on transportation stock returns in the US. Using the crude oil volatility index (OVX), as a proxy of energy price volatility, and three Dow Jones indexes tracking the performance of the airlines, marine and trucking stock subsectors, we employ a GARCH-jump model. The main results show that the oil market sends volatility to the US transport subsector stock indexes, suggesting that oil implied volatility plays a role in pricing US transport stocks. The impact of OVX shocks is asymmetric, indicating that increases and decreases in oil implied volatility have a heterogeneous impact on the transport subsector stock markets. Jumps are significant in the three transport subsector stock indexes, and are time-dependent. Notably, the three transportation subsector stock indexes are more sensitive to OVX shocks than the S&P 500 index. These results have important implications for investors, policymakers, academics, and managers of the US transportation industry.

Oil in crisis: What can we learn

This study focuses on the Chicago Board Options Exchange Oil Volatility Index (CBOEOVX)'s volatility transmission to the European stock markets. In the first section, to determine the contingent connection between market returns and CBOEOVX and to examine if CBOEOVX return Granger causes economic capital rates of return, the Dynamic Conditional Correlation (DCC) GARCH model has been used. Then, the study examined the asymmetric effects of fluctuations in estimated unpredictability on markets' profitability as the last step of the methoology. In this regard, the study applies quantile regression to examine the asymmetrical effect of the CBOEOVX on the market's daily returns. We have discovered a statistically significant inverse link that changes over time across our various sample markets. This hints at and supports CBOEOVX ‘s influence on the European stock market as a source of primary energy risk. In addition, we find substantial evidence of an asymmetric effect of fluctuations in the CBOEOVX on very negative market profits in the lowest quartile. Our findings not only build on and support previous research and add to the existing body of knowledge but also have evident consequences for future research, regulatory authorities, and practitioners. Practitioners and regulators may use these findings to better comprehend the connection between all crises and pave the path for the future.

Volatility predictability in crude oil futures: Evidence based on OVX, GARCH and stochastic volatility models

The paper examines the volatility predictive ability of the CBOE crude oil volatility index (OVX), GARCH and Stochastic Volatility Models in the crude oil market. Specifically, the dynamics of two major crude oil pricing benchmarks — Brent in Europe and WTI in America are compared. OVX index is able to provide the optimal forecast for the volatility of Brent’s future. The GJR-GARCH model outperforms its competition volatility models in both oil markets. It also reveals that WTI exhibits a faster and stronger response to market shocks compared to Brent oil. Most importantly, the predictability exhibit consistent and convincing results during remarkable events: the 2014 oil price decline, the 2020 coronavirus pandemic, and the 2022 Russian–Ukraine war. In practice, the paper contributes to portfolio strategy construction and investment risk management.

Did real economic uncertainty drive risk connectedness in the oil–stock nexus during the COVID-19 outbreak? A partial wavelet coherence analysis

This study explores the impact of real economic policy (business condition risk) on the oil–stock nexus risk connectedness during the COVID-19 pandemic. It uses multivariate wavelet coherency and partial wavelet coherency methods to isolate the effects of global risk indices, such as the US economic uncertainty index, the crude oil volatility index, and the geopolitical risk index, on risk connectedness. The study is based on daily data from January 2018 to December 2020 and finds a strong impact of real economic uncertainty indices on risk connectedness, with time-varying and frequency-sensitive patterns. The results also show that during the COVID-19 crisis, higher coherencies between oil and equity volatilities exist at lower frequencies. This research provides useful insights for regulators and portfolio diversifiers.

What Should be Taken into Consideration when Forecasting Oil Implied Volatility Index?

This study forecasts the oil volatility index (OVX) incorporating information from other implied volatility (IV) indices. We provide evidence for the existence of long memory in the OVX in order to justify the use of the Heterogeneous AutoRegressive (HAR) model. We extend the HAR model by implementing a dynamic model averaging (DMA) method in order to allow for IV indices from other asset classes to be applicable at different time periods. Apart from the statistical evaluation, a straddle options trading strategy validates our results from an economic point of view. The IV of Dow Jones is highly significant for short- and mid-run forecasting horizons, whereas, at longer horizons, the IV of Energy Sector provides accurate forecasts but only from an economic point of view.

How prone are emerging markets' sectoral indices to global uncertainties? Evidence from the quantile connectedness approach with portfolio implications

PurposeThere has been a burgeoning interest in exploring the impact of uncertainty factors on share returns. However, studies on the influence of global financial uncertainties on emerging market sectoral indices are scarce. Thus, there is a need to have a thorough investigation of the connection between global financial uncertainties and emerging market sectoral indices. To fill this gap, using the theoretical framework of international portfolio diversification (IPD) and utilizing data from 2008 to 2021, this study examines the spillover connection between global uncertainty indices (GUIs) and leading sectoral indices of 28 emerging markets.Design/methodology/approachThe authors employ the quantile spillover-based connectedness approach and minimum connectedness portfolio approach to explore the dynamic connectedness among sectoral indices and global uncertainty indices (GUIs) as well as portfolio implication.FindingsThe study found high connectedness among all indices, especially at higher and lower quantiles. Among GUIs, the authors find that stock market volatility (VIX) and oil volatility index (OVX) are strongly interconnected with all leading emerging markets' sectoral indices. Among sectoral indices, the linkage between the financial (F-Index), information technology (IT-Index), and consumer discretionary (CD-Index) sectors shows moderate interconnectedness. In contrast, the communication services (CS-Index) sector has low interconnectedness with the system. In terms of spillover effects, the authors find EVZ, OVX, and the IT sectors to be net recipients for the entire period. The authors also explored portfolio diversification benefits by employing a minimum connectedness portfolio approach. The cumulative returns' findings show a slight decline in the portfolio's value after 2010; during 2012, the pattern remained stable; from 2014 to 2020, the portfolio performed negatively, that is, underperformance due to different events in that period, including COVID-19. The Consumer Discretionary sector is found to be significant because of having the largest weight, 51%, in the portfolio during the study period.Practical implicationsThe study suggests that investors should invest in the communication services sector as it is the least connected. However, the connectedness increases during COVID-19, which implies that it may be difficult for investors to benefit from IPD in a crisis period. Hence, to obtain the benefits from IPD, the evidence suggests that investors need to consider Consumer Discretionary sector while considering assets for investment.Originality/valueThe study's uniqueness is that the authors have investigated spillover between GUIs and 28 emerging markets sectoral indices by employing a quantile spillover-based connectedness approach and minimum connectedness portfolio approach with a special focus on portfolio implication.

The Impact of Gold, Oil Prices, and their Associated Implied Volatilities on Performance of Pakistan’s Stock Market

This study examined the asymmetrical implications of gold and oil prices, as well as the implied volatility that goes along with them, on the performance of Pakistan's stock market. It was used Chicago Board of Options Exchange’s GVZ and OVX index for measuring volatility in gold market and oil market respectively due to more uncertainty in commodity markets and also took historical price movements, investor’s expectations regarding global prices into account. The monthly data used from October 2009 to January 2023. An ARDL approach was employed for checking short term and long term asymmetrical. The results of ARDL revealed that oil volatility index have significant impact of Pakistan’s stock market in short-run and long-run. The Gold Volatility Index shows significant impact after one month on both stock market capitalization and KSE 100 indexes in the short run. Fluctuations in worldwide commodity prices have a significant influence on underdeveloped economies like Pakistan and have a negative effect on the entire economy. The volatility and fluctuations in oil prices lead to higher production costs, which create uncertainty in companies' cash flows.

CBOE Crude Oil Volatility Index &Effects On The American Economy – VAR Based Causality Analysis

CBOE Crude Oil Volatility Index &Effects On The American Economy – VAR Based Causality Analysis

Can COVID-19 deaths and confirmed cases predict the uncertainty indexes? A multiscale analysis

Purpose This study aims to examine the multiscale predictability power of COVID-19 deaths and confirmed cases on the S&P 500 index (USA), CAC30 index (France), BSE index (India), two strategic commodity futures (West Texas intermediate [WTI] crude oil and Gold) and five main uncertainty indices Equity Market Volatility Ticker (EMV), CBOE Volatility Index (VIX), US Economic Policy Uncertainty (EPU), CBOE Crude Oil Volatility Index (OVX) and CBOE ETF Gold Volatility Index (GVZ). Furthermore, the authors analyze the impact of uncertainty indices and COVID-19 deaths and confirmed cases on the price returns of stocks (S&P500, CAC300 and BSE), crude oil and gold. Design/methodology/approach The authors used the wavelet coherency method and quantile regression approach to achieve the objectives. Findings The results show strong multiscale comovements between the variables under investigation. Lead-lag relationships vary across frequencies. Finally, COVID-19 news is a powerful predictor of the uncertainty indices at intermediate (4–16 days) and low (32–64 days) frequencies for EPU and at low frequency for EMV, VIX, OVX and GVZ indices from January to April 2020. The S&P500, CAC30 and BSE indexes and gold prices comove with COVID-19 news at low frequencies during the sample period. By contrast, COVID-19 news and WTI oil moderately correlated at low frequencies. Finally, the returns on equity and commodity assets are influenced by uncertainty indices and are sensitive to market conditions. Originality/value This study contributes to the literature by exploring the time and frequency dependence between COVID-19 news (confirmed and death cases) on the returns of financial and commodity markets and uncertainty indexes. The findings can assist market participants and policymakers in considering the predictability of future prices and uncertainty over time and across frequencies when setting up regulations that aim to enhance market efficiency.

Does solar activity affect the price of crude oil? A causality and volatility analysis

This study examines how solar activity affects the oil volatility index. To test whether solar phenomena affect the oil volatility price index, the Granger and Step-by-Step causality techniques are applied. Furthermore, we employ the autoregressive distributed lag order model (ARDL), which introduces as exogenous variables the Dow Jones Equity REIT index, the 3-month U.S. Government bond, the Oil Volatility Index, and the spread. As part of the model, we also include a solar variable, namely, the solar wind velocity, which is one of the most significant characteristics of the solar wind plasma. According to the results, the solar wind velocity "Granger causes" and "step-by-step causes" the oil volatility price index. All variables analyzed provide useful information for the modeling of the oil volatility index, which shows that solar activity does indeed influence the market for oil prices.

Markov-Regime Switches in Oil Markets: The Fear Factor Dynamics

This paper is an attempt to examine regime switches in the empirical relation between return dynamics and implied volatility in energy markets. The time-varying properties of the return-generating process are defined as a function of several risk factors, including oil market volatility and changes in stock prices and currency rates. The empirical evidence is based on Markov-regime switching models, which have the capacity to capture, in particular, the stochastic behavior of the OVX oil volatility index as a benchmark for investors’ fear. The results suggest that the dynamics of oil market returns are governed by two distinct regimes, a state driven by a negative relationship between returns and implied volatility and another state characterized by a more pronounced negative correlation. It is the latter regime with a stronger correlation that tends to prevail over the sample period from 2008 to 2021, but the frequency of regime shifts also seems to increase under more volatile oil price dynamics in association with significant events such as the COVID-19 pandemic. Thus, the evidence of a negative correlation structure is found to be robust to changes in the estimation period, which suggests that the oil volatility index remains a reliable gauge of market sentiment in the energy markets.

Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests

We examine the daily dependence and directional predictability between the returns of crude oil and the Crude Oil Volatility Index (OVX). Unlike previous studies, we apply a battery of quantile-based techniques, namely the quantile unit root test, the causality-in-quantiles test, and the cross-quantilogram approach. Our main results show evidence of significant bi-directional predictability that is quantile-dependent and asymmetric. A significant positive Granger causality runs from oil (OVX) returns to OVX (oil) returns when both series are in similar lower (upper) quantiles, as well as in opposite quantiles. The Granger causality from OVX returns to oil returns is only significant during periods of high volatility, although it is not always positive. The findings imply that the forward-looking estimate of oil volatility, reflecting the sentiment of oil market participants, should be considered when studying price variations in the oil market, and that crude oil returns can be used to predict oil implied volatility during bearish market conditions. Therefore, the findings have implications regarding predictability under various conditions for oil market participants.