Implied Volatility Index Research Articles

Does happiness forecast implied volatility? Evidence from nonparametric wave-based Granger causality testing

Past studies on the causal relationship between online sentiment and implied volatility indices report mixed results. In this paper, we employ a daily happiness index extracted from Twitter as a proxy for online sentiment to investigate the relationship between sentiment and the Chicago Board of Option Exchange volatility indices. Applying a nonparametric wave-based Granger causality test, allows us close examination of frequencies and periods of causality. Investigating four different subsample periods, we evidence that daily happiness sentiment consistently manifests short-term causality of implied volatility indices. This is evidenced over our sample period as a whole, and for three out of four investigated subperiods—the exception being a subperiod of three years following the Global Financial Crisis. Additional Diebold and Yilmaz (2012) spillover testing evidences that the causal relationship between happiness and implied volatility is strongest for stable low happiness subperiods. Importantly, we also evidence intervals where spillovers from happiness to implied volatility spike sharply. This suggests that the relationship between happiness and implied volatility is heavily conditioned by exogenous factors. Consequently, while we identify consistent short-term causality of happiness to implied volatility measures, economically meaningful forecasting of implied volatility from happiness remains elusive. Our findings should be of considerable interest to investors and practitioners, as well as to behavioral finance researchers interested in how sentiment shapes the ‘uncertainty of uncertainty.

Financial market volatility based on complex network and fuzzy logic theory

Volatility is an inherent attribute of the financial market, which is usually expressed as the degree of volatility of financial asset prices. The volatility of the financial market means that there is uncertainty or risk in the market. This paper mainly studies financial market fluctuations based on complex networks and fuzzy logic theory. This article first systematically organizes and summarizes the theoretical construction of complex networks and fuzzy logic. In terms of complex networks, the definition of complex networks, the theory of commonly used functions (classical models of complex networks) and the solving methods are sorted out. In the construction of fuzzy logic theory, starting with quantifiable financial market volatility indicators, the construction models of realized volatility and implied volatility are discussed, and complex network models of implied volatility and model-free models are discussed. The theoretical construction methods were compared and analyzed. Finally, it summarizes the theoretical construction methods of implied volatility index and points out the advantages of model-free implied volatility as a market volatility and risk measurement index, which contains more effective future risk information and is based on implied volatility. The empirical research on indexes and complex network models has laid a theoretical foundation. Experimental data shows that the bond market and the foreign exchange market have the largest fluctuations in the correlation coefficient, reaching 0.35; followed by the stock market and the bond market, which is about 0.17; the stock market and foreign exchange market with the smallest fluctuations are about 0.08. The experimental results show that the financial market volatility research data based on complex networks and fuzzy logic theory is more accurate.

The predictive power of stock market's expectations volatility: A financial synchronization phenomenon.

We explore the use of implied volatility indices as a tool for estimate changes in the synchronization of stock markets. Specifically, we assess the implied stock market’s volatility indices’ predictive power on synchronizing global equity indices returns. We built the correlation network of 26 stock indices and implemented in-sample and out-of-sample tests to evaluate the predictive power of VIX, VSTOXX, and VXJ implied volatility indices. To measure markets’ synchronization, we use the Minimum Spanning Tree length and the length of the Planar Maximally Filtered Graph. Our results indicate a high predictive power of all the volatility indices, both individually and together, though the VIX predominates over the evaluated options. We find that an increase in the markets’ volatility expectations, captured by the implied volatility indices, is a good Granger predictor of an increase in the synchronization of returns in the following month. Estimating, monitoring, and predicting returns’ synchronization is essential for investment decision-making, especially for diversification strategies and regulating financial systems.

Are Cryptos Safe-Haven Assets during Covid-19? Evidence from Wavelet Coherence Analysis

ABSTRACT This study adds to the inconclusive debate on safe-haven properties of cryptocurrencies during Covid-19 by analyzing the use of wavelet coherence framework on the global Covid-19 fear index, cryptocurrency implied volatility index (VCRIX), and cryptocurrency returns. Our findings show that a non-financial market-based proxy of market stress that represents fear of households and retail investors reveals cryptocurrencies as safe-haven assets; however, a financial market-based proxy of market turbulence exposes that cryptocurrencies behave like traditional assets during the times of Covid-19 pandemic. Our findings support that long-term investors can invest in the cryptocurrency market to hedge the risks during Covid-19 pandemic.

Time Connectedness of Fear

This paper examines the interconnection between four implied volatility indices representative of the investors' consensus view of expected stock market volatility at different maturities during the period January 3, 2011-May 4, 2018. To this end, we first perform a static analysis to measure the total volatility connectedness in the entire period using a framework proposed by Diebold and Yilmaz (2014). Second, we apply a dynamic analysis to evaluate both the net directional connectedness for each market using the TVP-VAR connectedness approach developed by Antonakakis and Gabauer (2017). Our results suggest that a 72.27%, of the total variance of the forecast errors is explained by shocks across the examined investor time horizons, indicating that the remainder 27.73% of the variation is due to idiosyncratic shocks. Furthermore, we find that volatility connectedness varies over time, with a surge during periods of increasing economic and financial instability. Finally, we also document a superior performance of the TVP-VAR approach to connectedness respect to the original one proposed by Diebold and Yilmaz (2014).

Jump activity analysis of the equity index and the corresponding volatility: Evidence from the Chinese market

AbstractThis paper performs a nonparametric analysis of jump activity for the Chinese equities market. More precisely, we perform formal tests to decide whether the jumps in the 50 exchange‐traded fund (50ETF) and its volatility occur together by using the implied volatility index (iVIX) as a proxy for volatility. Our empirical findings are as follows: (i) joint jumps in the 50ETF and iVIX hardly occur, especially during noncrisis periods; (ii) there is a strong degree of dependence between the jump sizes of the 50ETF and iVIX when disaggregating jumps into their positive and negative parts; (iii) the jump component seems to contribute more to the leverage effect than the diffusive component.

Designing volatility indices for Austria, Finland and Spain

The volatility index of the Chicago Board Options Exchange (VIX) was the first to be established, and it has given rise to international imitations worldwide as it is considered to be a barometer of investor fear. Starting from this volatility index, the aim of this paper is threefold. By adopting the VIX methodology, we construct a volatility index for three European countries (Austria, Finland and Spain) which currently do not provide this kind of market information for investors. Second, we investigate the properties of various volatility indices. In particular, we test their ability to act as fear indicators and as predictors of future returns. Moreover, we seek to cast light on the term structure of the proposed volatility indices, by computing spot and forward implied volatility indices for different times to maturity (30, 60 and 90 days). Our results indicate that volatility indices are useful not only for investors to improve their trading decisions, but also for policy-makers to choose the appropriate economic measures to promote stability in the market.

The role of US implied volatility index in forecasting Chinese stock market volatility: Evidence from HAR models

The US implied volatility index (VIX) is a popular proxy for global financial market uncertainty. This paper aims to assess the role of this proxy in forecasting the Chinese stock market volatility, as China’s globalization trend is strengthening. For this purpose, we develop six heterogeneous autoregressive (HAR) models allowing for VIX changes to forecast the Chinese stock market volatility. The in-sample and out-of-sample results show the VIX changes can improve the volatility forecasting in the Chinese stock market but mainly show improvement for the bad volatility forecasting rather than for good volatility forecasting. Additionally, such forecasting improvement is sizable at the short-run prediction horizon but weakens as the prediction horizon increases. Our results also remain robust by using alternative evaluation methods and alternative HAR models.

Fat Tails, Serial Dependence, and Implied Volatility Connections

This paper accounts for fat tails and serial dependence for implied volatility index network connections among equity and commodity markets using Bayesian vector heterogeneous autoregressions. I analyse the information content of such connections over short-, medium- and long-horizons for predicting underlying asset returns and whether conventional asset pricing risk factors explain the variation of portfolios that sort on directional connections. Results highlight the importance of accounting for network connections within the information set when forecasting underlying asset returns, and that investors can trade on directional connections at horizons of less than month to hedge against temporary adverse changes to investment opportunities.

On the relation between Pandemic Disease Outbreak News and Crude oil, Gold, Gold mining, Silver and Energy Markets

Fear of the disease outbreak news (DONs) has shocked commodity markets and raised the likelihood of economic uncertainty and recession globally. This article examines the unprecedented overreaction of investors sentiments in the commodities such as Crude oil, Gold, Gold Mining, Silver, and the Energy sector. The deadly effects of DONs-COVID-19 in the commodities market have been the worst in history; it appeared the first time higher than the common stock's volatility. Covid-19 induced economic uncertainty has impacted severely through all commodities except the safe-haven Gold (GVZ). Importantly, ETF Options based Implied Volatility Index of Crude (OVX), Silver (VXSLV), and Energy (VXXLE) stocks have crossed the peak level what it was prevailing during the global financial crisis 2008. The unparalleled upsurge of the implied volatility index across all commodities indicates higher demand for the hedge funds to protects the commodity portfolio. ETF options on the commodity act as the best hedge against market uncertainty. Overburden on the put option results in an increased risk premium, henceforth higher expected volatility. ETF options truly measure the investor's fear of predominant in the commodity market.

Impact of COVID-19 pandemic on the energy markets

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.

Do Dow Jones Islamic equity indices undergo speculative pressure? New insights from a nonlinear and asymmetric analysis

AbstractThis paper study the exposure of Dow Jones Islamic equity indices to global risk factors while considering both effects of speculative influence and fundamental changes. The study involves future markets according to Shari'a guidelines that ban speculative trading. Nonlinear ARDL modelling approach along with dynamic asymmetric causality tests is conducted to scrutinize long‐run and short‐run asymmetries and asymmetric causalities. The used data are crude refined oil future prices to stand for fundamental chocks in speculative trading and implied volatility index to stand for speculative pressure. Obtained results approve the presence of asymmetric cointegration between Islamic equity indices and oil‐VIX future prices. The Islamic indices are sensitive to oil‐VIX future chocks both in the long run and the short run. Asymmetric causality tests indicate the existence of asymmetric causality running from crude refined oil future prices which confirm the fundamental contagion effects but the causality from VIX to Islamic indices is not pronounced at all. Results support the absence of speculative influence on Islamic indices and prove that DJIM indices corroborate with Shari'a compliant rules. In sum, the paper confirms the asymmetric cointegration and then assumes that the asymmetry is present in Islamic equity indices inherent to changes in international futures prices. The paper shows the structure of exposure to global markets' expectations and provides faith‐based investors with insights about the attitude of Islamic indices towards the future. Our findings suggest useful highlights to policymakers, portfolio managers, faith‐based investors and scholars. We then could state that if long‐run relationship occurs it is naturally asymmetrical.

News-based equity market uncertainty and crude oil volatility

Previous studies indicate that the US equity market implied volatility index (VIX) impacts the crude oil market volatility. However, the VIX typically reflects macroeconomic fluctuations, little affected by social media or investor perception changes. In this paper, we use recently introduced news-based equity market volatility (EMV) trackers to examine their impacts on crude oil volatility in its various states and their ability to predict oil volatility relative to that of the VIX. Applying quantile regressions, the results indicate a significant impact of EMV trackers on the oil market volatility during periods of high oil volatility, whereas the impact is mostly insignificant when the oil market is less volatile, which points to an asymmetry. Further analysis shows that various EMV trackers (EMV-overall, EMV-commodity, EMV-crises) have better forecasting power than VIX, economic policy uncertainty (EPU) and geopolitical risk (GPR) indexes. Our findings are relevant to asset pricing, oil portfolio formation, and risk management.

Stock exchange volatility forecasting under market stress with MIDAS regression

AbstractThis paper presents two different approaches of volatility forecasting. One is based on option‐implied volatility (IV), the other involves conducting time series methods using historical volatility. With that purpose, we study eight developed stock markets, offering implied volatility indexes for the 2008 financial crisis. We evaluated the 1 month out‐of‐sample volatility forecast performance of two statistical‐based models, Mixed Data Sampling (MIDAS) and GARCH, and compared the results with option‐implied volatility indexes. Our results suggest that MIDAS produce superior forecast performance compared to GARCH model and IV method. While options are not available for all assets, we believe that MIDAS model can be a sophisticated tool for researchers and analysts to forecast future volatility with its ability to process high‐frequency data.

GARCH option pricing and implied FX volatility indices

The focus of this paper is the foreign exchange (FX) variance risk premium and the modelling of FX volatility indices of both a developed (United States Dollar) and an emerging market (South African Rand). Regarding the methodology, the variance risk premium is estimated as the difference between realised variance and the variance obtained from the volatility index, and different univariate Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models are considered for the modelling of the volatility index. Empirical results indicate that the variance risk premium is negative on average, this is consistent with previous findings in the literature. Furthermore, asymmetric GARCH models outperform the symmetric GARCH model when modelling FX implied volatility indices.

Do Stock Market Fear and Economic Policy Uncertainty Co-Move with COVID-19 Fear? Evidence from the US and UK

Using the daily data covering both the first and second wave of COVID-19 pandemic over the period from March 3, 2020, to February 12, 2021, this study documents a strong positive comovement between implied volatility indices and two proxies of the COVID-19 fear. However, in all the cases, the infectious disease equity market volatility index (IDEMVI), the COVID-19 proxy that is more representative of the stock market, exhibits a stronger positive comovement with volatility indices than the COVID-19 health-based fear index. We also find that the UK's implied volatility index weakly co-moves with the COVID-19 health-based fear index compared to the US. Our results show that EPU indices of both the US and UK exhibit a weak or no correlation with the COVID-19 health-based fear index. However, we find a significant positive co-movement between EPU indices and IDEMVI over the short-horizon and most of the sampling period with the leading effect of IDEMVI.

Predicting VIX with Adaptive Machine Learning

Using 278 economic and financial variables we study the power of machine learning (ML) in predicting the daily CBOE implied volatility index (VIX). Designing and applying an automated three-step ML framework with a large number of algorithms we identify Adaptive Boosting as the best classification model chosen at the validation stage. It produces an average rate of 57% during the 11-year out-of-sample period. Potential significant economic gains are demonstrated in various applications with tradable instruments. Besides the modelling techniques, the weekly US jobless report is the most important contributor to the predictability along with some S&P 500 members’ technical indicators.

Are Cryptos Safe-Haven Assets during COVID-19? Evidence from Wavelet Coherence Analysis

We use wavelet coherence analysis on global COVID-19 fear index, cryptocurrency market specific implied volatility index (VCRIX) and cryptocurrency returns to investigate safe-haven properties of cryptocurrencies during COVID-19 pandemic. The findings of our paper show that a non-financial market-based proxy of market stress that represents fear of households and retail investors reveals cryptocurrencies as safe-haven assets; however, a financial market-based proxy of the market turbulence exposes that cryptocurrencies behave like traditional assets during the times of COVID-19 pandemic. Our findings support that long-term investors can invest in the cryptocurrency market to hedge the risks during the COVID-19 pandemic.

Dynamic connectedness between the U.S. financial market and Euro-Asian financial markets: Testing transmission of uncertainty through spatial regressions models

This study investigates fear transmission between the U.S. financial market and the Euro-Asian financial markets. Our spatial regression models show that the U.S. VIX index (Chicago Board Options Exchange implied volatility index CBOE VIX) and six European and Asian VIX indexes collectively have the explanatory ability for each other in static time conditions. Regarding a significant spatial spillover effect, dynamic interaction is also demonstrated between the U.S. market and European and Asian markets, implying a significant transmission of risk over time. Moreover, we highlight that the U.S. domestic risk is dominant in explaining the U.S. fear index relative to international risks (European and Asian fear indexes). On the contrary, we highlight that the U.S. fear index outperforms European domestic risks in explaining fear in European financial markets. However, this is not the case for Asian markets, where the results showed that all Asian fear index reactions are more affected by the Asian realised volatilities than by the U.S. fear index.

Role of Economic Policy Uncertainty in the Connectedness of Cross-Country Stock Market Volatilities

The implied volatility index is a forward-looking indicator of fear among stock market participants. We examine the extent to which the connectedness of fear among global stock markets is driven by the cross-country connectedness of economic policy uncertainty (EPU). We use data on stock market fear and EPU indices for 13 countries, which spans from January 2011 to December 2018. To measure the connectedness among stock market fear and EPU of our sample countries, we employ two connectedness models. A cross-sectional regression model is further employed to ascertain the extent to which EPU connectedness between two countries explains the connectedness of fear between their stock markets, while controlling for bilateral linkage and country-specific factors. We find that EPU connectedness between any two partner countries significantly drives the connectedness of fear between their stock markets. The driving potential not only holds for short- and long-term connectedness, but also after controlling for bilateral linkages (bilateral trade, geographical distance, common language) and country-specific (trade and financial openness of the transmitter country) factors indicating robustness in our results.