Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure.

This paper investigates the relationship between investors' attention, as measured by Google search queries, and equity implied volatility during the COVID-19 outbreak. Recent studies show that search investors' behavior data is an extremely abundant repository of predictive data, and investor-limited attention increases when the uncertainty level is high. Our study using data from thirteen countries across the globe during the first wave of the COVID-19 pandemic (January-April 2020) examines whether the search "topic and terms" for the pandemic affect market participants' expectations about future realized volatility. With the panic and uncertainty about COVID-19, our empirical findings show that increased internet searches during the pandemic caused the information to flow into the financial markets at a faster rate and thus resulting in higher implied volatility directly and via the stock return-risk relation. More specifically for the latter, the leverage effect in the VIX becomes stronger as Google search queries intensify. Both the direct and indirect effects on implied volatility, highlight a risk-aversion channel that operates during the pandemic. We also find that these effects are stronger in Europe than in the rest of the world. Moreover, in a panel vector autoregression framework, we show that a positive shock on stock returns may soothe COVID-related Google searches in Europe. Our findings suggest that Google-based attention to COVID-19 leads to elevated risk aversion in stock markets.

Dynamic co-movement in major commodity markets during crisis periods: A wavelet local multiple correlation analysis

We study time-scale co-movement of returns and implied volatilities of oil, gold, wheat, and copper in a multivariate setting using the wavelet local multiple correlation (WLMC) approach. Daily data cover January 03, 2007 – August 08, 2022, including the global financial crisis, COVID-19 pandemic, and Russia-Ukraine war. The results show that the correlations across the commodities are heterogeneous, less stable in the short-term, and more pronounced in the long-term, and vary in sign and magnitude. Despite market instability, contagion is not clearly seen in either return or volatility, reflecting noise trading and the importance of the individual characteristics of commodities.

The time-varying impact of uncertainty on oil market fear: Does climate policy uncertainty matter?

Implied volatility index is a popular proxy for market fear. This paper uses the oil implied volatility index (OVX) to investigate the impact of different uncertainty measures on oil market fear. Our uncertainty measures consider multiple perspectives, specifically including climate policy uncertainty (CPU), geopolitical risk (GPR), economic policy uncertainty (EPU), and equity market volatility (EMV). Based on the time-varying parameter vector autoregression (TVP-VAR) model, our empirical results show that the impact of CPU, GPR, EPU, and EMV on OVX is time-varying and heterogeneous due to these uncertainty measures containing different information content. In particular, the CPU has become increasingly important for triggering oil market fear since the recent Paris Agreement. During the COVID-19 pandemic, CPU, EPU, and EMV, rather than GPR, play a prominent role in increasing oil market fear. © 2023 Elsevier Ltd

Knowledge Discovery to Support WTI Crude Oil Price Risk Management

The high volatility of commodity prices and various problems that the energy sector has to deal with in the era of COVID-19 have significantly increased the risk of oil price changes. These changes are of the main concern of companies for which oil is the main input in the production process, and therefore oil price determines the production costs. The main goal of this paper is to discover decision rules for a buyer of American WTI (West Texas Intermediate) crude oil call options. The presented research uses factors characterizing the option price, such as implied volatility and option sensitivity factors (delta, gamma, vega, and theta, known as "Greeks”). The performed analysis covers the years 2008–2022 and options with an exercise period up to three months. The decision rules are discovered using association analysis and are evaluated in terms of the three investment efficiency indicators: total payoff, average payoff, and return on investment. The results show the existence of certain ranges of the analyzed parameters for which the mentioned efficiency indicators reached particularly high values. The relationships discovered and recorded in the form of decision rules can be effectively used or adapted by practitioners to support their decisions in oil price risk management.

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.

WTI Crude Oil Options Market Prior to and During the COVID-19 Pandemic

The COVID-19 pandemic has caused turbulence in many areas of the global economy. It also contributed to an increase in volatility on the energy commodities market. This spilled over into the derivatives market, particularly the crude oil futures market. The aim of the article is to compare the costs and effectiveness of using options on WTI oil from before and after the pandemic. The analyzes took into account the value of option premiums and final results obtained by buyers of call options from March 1, 2018 to April 14, 2022. The results showed that buyers of call options during the pandemic, despite paying much higher option premiums, experienced significantly higher payouts and rates of return. They were the highest for options with the longest expiry periods of 21-30 days. Research also showed that during the pandemic, options with strike prices set at a level higher than the price of oil on the contract date had particularly high rates of return, while the highest payout values were achieved by buyers of call options with low strike prices.

The effect of oil implied volatility and geopolitical risk on GCC stock sectors under various market conditions

The importance of crude oil volatility and geopolitical risk for stock pricing is well known in both developed and emerging economies, but is relatively understudied in major oil-exporting countries at the sectoral level of stock indices and under various market conditions. Using daily data on eight Gulf Cooperation Council (GCC) stock sector indices over the period February 2010–30 June 2022, we capture the effect of two global risk factors, namely oil implied volatility and geopolitical risk, on stock returns and volatility while accounting for bull/bear markets and low/high volatility regimes. The analysis indicates the following results. Firstly, the effect of oil implied volatility is stronger than that of geopolitical risk, notably for Consumer Discretionary and Staples. Secondly, the effect on both returns and volatility is generally positive during bull markets, but it is stronger for volatility;the response of the returns of Energy, Materials, Industrials, and Financials is negative in bear markets and positive during bull markets. Thirdly, the effect of oil implied volatility on stock sector volatility is slightly higher during the COVID-19 outbreak for some cases and is prominent during bull markets. Our findings matter for the predictability of GCC stock sector returns and volatility and for the design of hedging strategies under various market states. © 2023 Elsevier B.V.

Volatility spillovers during normal and high volatility states and their driving factors: A cross-country and cross-asset analysis

Understanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile-based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011–10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward-looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision-making process of risk managers and policymakers. © 2022 John Wiley & Sons Ltd.

Do stock market fear and economic policy uncertainty co-move with COVID-19 fear? Evidence from the US and UK

Purpose: The purpose of the paper is to investigate co-movement of major implied volatility indices and economic policy uncertainty (EPU) indices with both the health-based fear index and market-based fear index of COVID-19 for the USA and the UK to help investors and portfolio managers in their informed investment decisions during times of infectious disease spread. Design/methodology/approach: This study uses wavelet coherence approach because it allows to observe lead–lag nonlinear relationship between two time-series variables and captures the heterogeneous perceptions of investors across time and frequency. The daily data used in this study about the USA and the UK covers major implied volatility indices, EPU, health-based fear index and market-based fear index of COVID-19 for both the first and second waves of COVID-19 pandemic over the period from March 3, 2020 to February 12, 2021. Findings: The results document a strong positive co-movement 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, is more representative of the stock market and exhibits a stronger positive co-movement with volatility indices than the COVID-19 fear index (C19FI). This study also finds that the UK's implied volatility index weakly co-moves with the C19FI compared to the USA. The results show that EPU indices of both the USA and the UK exhibit a weak or no correlation with the C19FI. However, this study finds a significant and positive co-movement of EPU indices with IDEMVI over the short horizon and most of the sampling period with the leading effect of IDEMVI. This study's robustness analysis using partial wavelet coherence provides further strengths to the findings. Research limitations/implications: The investment decisions and risk management of investors and portfolio managers in financial markets are affected by the new information on volatility and EPU. The findings provide insights to equity investors and portfolio managers to improve their risk management practices by incorporating how health-related risks such as COVID-19 pandemic can contribute to the market volatility and economic risks. The results are beneficial for long-term equity investors, as their investments are affected by contributing factors to the volatility in US and UK's stock markets. Originality/value: This study adds following promising values to the existing literature. First, the results complement the existing literature (Rubbaniy et al., 2021c) in documenting that type of COVID-19 proxy matters in explaining the volatility (EPU) relationships in financial markets, where market perceived fear of COVID-19 is appeared to be more pronounced than health-based fear of COVID-19. Second, the use of wavelet coherence approach allows us to observe lead–lag relationship between the selected variables, which captures the heterogeneous perceptions of investors across time and frequency and have important insights for the investors and portfolio managers. Finally, this study uses the improved data of COVID-19, stock market volatility and EPU compared to the existing studies (Sharif et al., 2020), which are too early to capture the effects of exponential spread of COVID-19 in the USA and the UK after March 2020. © 2022, Emerald Publishing Limited.

Pricing extreme mortality risk in the wake of the COVID-19 pandemic

In pricing extreme mortality risk, it is commonly assumed that interest rate and mortality rate are independent. However, the COVID-19 pandemic calls this assumption into question. In this paper, we employ a bivariate affine jump-diffusion model to describe the joint dynamics of interest rate and excess mortality, allowing for both correlated diffusions and joint jumps. Utilizing the latest U.S. mortality and interest rate data, we find a significant negative correlation between interest rate and excess mortality, and a much higher jump intensity when the pandemic experience is considered. Moreover, we construct a risk-neutral pricing measure that accounts for both diffusion and jump risk premia, and we solve for the market prices of risk based on mortality bond prices. Our results show that the pandemic experience can drastically change investors' perception of the mortality risk market in the post-pandemic era. © 2022 Elsevier B.V.

Dynamic connectedness among the implied volatilities of oil prices and financial assets: New evidence of the COVID-19 pandemic

This paper examines the dynamic connectedness among the implied volatilities of oil prices (OVX) and fourteen other assets, which can be grouped into five different assets classes (i.e., energy commodities, stock markets, precious metals, exchange rates and bond markets). To do so we estimate a recently developed time-varying parameter vector autoregressive (TVP-VAR) connectedness approach using daily data spanning from March 16th, 2011 to March 3rd, 2021 — covering the first year of the COVID-19 pandemic. The empirical results suggest that connectedness across the different asset classes and oil price implied volatilities are varying over time and fluctuate at very high levels. The dynamic total connectedness ranges between 65% and 85% indicating a high degree of cross-market risk linkages. Furthermore, we find that the oil market is becoming more integrated with the financial markets, since it tends to be materially impacted by abrupt fluctuations of the global financial markets' volatilities. More specifically, the analysis shows that, throughout the period, OVX is a net receiver of shocks to the remaining implied volatilities. Finally, the net pairwise connectedness measures suggest that OVX is constantly at the net receiving end vis-a-vis the majority of the asset classes' implied volatilities. Those findings are of major importance for portfolio and risk management in terms of asset allocation and diversification. © 2022 The Author(s)

Stock valuation during the COVID-19 pandemic: An explanation using option-based discount rates.

Changes in short-term expected market returns (discount rates) were a significant driver behind the unprecedented fluctuations in equity markets during the first 4 months of the COVID-19 pandemic. Using option-based estimates of the expected market risk premium for 13 international markets, we find that approximately 40% of the change in market values during the COVID-19 pandemic can be attributed to changes in short-term discount rates. We also document sharply downward sloping term structures of equity risk premia at the start of the pandemic, consistent with Hasler and Marfè (2016). Finally, we document a significant increase in the correlation between index returns and changes in the short-term discount rate during the pandemic compared to the period before the pandemic.

The COVID-19 risk in the cross-section of equity options.

We use the implied volatility slope measures derived from US stock options to examine the impact of COVID-19 risk on the options market. The severity of COVID-19 is measured by the number of new confirmed cases. We find that equity options that are most sensitive to COVID-19 generate a more positive IV slope than less COVID-19-sensitive equity options. Moreover, this measure is more positive and significant during the lockdown period. Our findings suggest that the hedging cost of downside tail risk is more expensive during the high-uncertainty period, the time when COVID-19 is more intensive.

Do stock market fear and economic policy uncertainty co-move with COVID-19 fear? Evidence from the US and UK

Purpose>The purpose of the paper is to investigate co-movement of major implied volatility indices and economic policy uncertainty (EPU) indices with both the health-based fear index and market-based fear index of COVID-19 for the USA and the UK to help investors and portfolio managers in their informed investment decisions during times of infectious disease spread.Design/methodology/approach>This study uses wavelet coherence approach because it allows to observe lead–lag nonlinear relationship between two time-series variables and captures the heterogeneous perceptions of investors across time and frequency. The daily data used in this study about the USA and the UK covers major implied volatility indices, EPU, health-based fear index and market-based fear index of COVID-19 for both the first and second waves of COVID-19 pandemic over the period from March 3, 2020 to February 12, 2021.Findings>The results document a strong positive co-movement 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, is more representative of the stock market and exhibits a stronger positive co-movement with volatility indices than the COVID-19 fear index (C19FI). This study also finds that the UK's implied volatility index weakly co-moves with the C19FI compared to the USA. The results show that EPU indices of both the USA and the UK exhibit a weak or no correlation with the C19FI. However, this study finds a significant and positive co-movement of EPU indices with IDEMVI over the short horizon and most of the sampling period with the leading effect of IDEMVI. This study's robustness analysis using partial wavelet coherence provides further strengths to the findings.Research limitations/implications>The investment decisions and risk management of investors and portfolio managers in financial markets are affected by the new information on volatility and EPU. The findings provide insights to equity investors and portfolio managers to improve their risk management practices by incorporating how health-related risks such as COVID-19 pandemic can contribute to the market volatility and economic risks. The results are beneficial for long-term equity investors, as their investments are affected by contributing factors to the volatility in US and UK's stock markets.Originality/value>This study adds following promising values to the existing literature. First, the results complement the existing literature (Rubbaniy et al., 2021c) in documenting that type of COVID-19 proxy matters in explaining the volatility (EPU) relationships in financial markets, where market perceived fear of COVID-19 is appeared to be more pronounced than health-based fear of COVID-19. Second, the use of wavelet coherence approach allows us to observe lead–lag relationship between the selected variables, which captures the heterogeneous perceptions of investors across time and frequency and have important insights for the investors and portfolio managers. Finally, this study uses the improved data of COVID-19, stock market volatility and EPU compared to the existing studies (Sharif et al., 2020), which are too early to capture the effects of exponential spread of COVID-19 in the USA and the UK after March 2020.

COVID-19 related stringencies and financial market volatility: sectoral evidence from India

PurposeThis study aims to examine the impact of the stringency of COVID-19 protocols on the volatility of sectoral indices during the period 03:2020-05:2021. Specifically, this study investigates the role of economic disturbances on sectoral volatility by applying a range of conditional volatility techniques. Design/methodology/approachFor this analysis, two approaches were adopted. The first approach considers COVID stringency as a factor in the conditional variance equation of sectoral indices. In contrast, the second approach considers the stringency indicator as a possible determinant of their estimated conditional volatility. FindingsResults show that the stringency of the protocols throughout the pandemic phase led to an instantaneous spike followed by a gradual decrease in estimated volatility of all the sectoral indices except pharma and health care. Specific sectors such as bank, FMCG, consumer durables, financial services, IT, media and private banks respond to protocols expeditiously compared to other sectors. Originality/valueThe key contribution of this study to the existing literature is the innovative approach. The inclusion of the COVID stringency index as a regressor in the variance equation of the conditional volatility techniques was a distinctive approach for assessing the volatility dynamics with the stringency of COVID protocols. Furthermore, this study also adopts an alternative approach that estimates the conditional volatility of the indices and then tests the effect of the stringencies on estimated volatility in a regression framework.

Volatility spillovers during normal and high volatility states and their driving factors: A cross‐country and cross‐asset analysis

Understanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile-based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011-10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward-looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision-making process of risk managers and policymakers.

Pricing Extreme Mortality Risk in the Wake of the COVID-19 Pandemic

In pricing extreme mortality risk, it is commonly assumed that interest rate and mortality rate are independent. However, the COVID-19 pandemic calls this assumption into question. In this paper, we employ a bivariate affine jump-diffusion model to describe the joint dynamics of interest rate and excess mortality, allowing for both correlated diffusions and joint jumps. Utilizing the latest U.S. mortality and interest rate data, we find a significant negative correlation between interest rate and excess mortality, and a much higher jump intensity when the pandemic experience is considered. Moreover, we construct a risk-neutral pricing measure that accounts for both diffusion and jump risk premia, and we solve for the market prices of risk based on mortality bond prices. Our results show that the pandemic experience can drastically change investors' perception of the mortality risk market in the post-pandemic era.

COVID-19 vaccine and post-pandemic recovery: Evidence from Bitcoin cross-asset implied volatility spillover.

This paper examines implied volatility spillovers and connectedness between Bitcoin and a broad range of traditional financial assets (U.S. equity market, gold, crude oil, emerging markets and developing markets) from January 8, 2019 to January 20, 2022. Vector Auto-Regression and Generalized Forecast Error Variance Decomposition are used to compare results before COVID-19, during COVID-19 and after the vaccine becomes available. Results indicate higher connectedness during COVID-19 but very low connectedness after the vaccine is available, signaling recovery in financial markets. We also find that Bitcoin is a strong transmitter of volatility during COVID-19.

Dynamic connectedness among the implied volatilities of oil prices and financial assets: New evidence of the COVID-19 pandemic

This paper examines the dynamic connectedness among the implied volatilities of oil prices (OVX) and fourteen other assets, which can be grouped into five different assets classes (i.e., energy commodities, stock markets, precious metals, exchange rates and bond markets). To do so we estimate a recently developed time-varying parameter vector autoregressive (TVP-VAR) connectedness approach using daily data spanning from March 16th, 2011 to March 3rd, 2021 – covering the first year of the COVID-19 pandemic. The empirical results suggest that connectedness across the different asset classes and oil price implied volatilities are varying over time and fluctuate at very high levels. The dynamic total connectedness ranges between 65% and 85% indicating a high degree of cross-market risk linkages. Furthermore, we find that the oil market is becoming more integrated with the financial markets, since it tends to be materially impacted by abrupt fluctuations of the global financial markets’ volatilities. More specifically, the analysis shows that, throughout the period, OVX is a net receiver of shocks to the remaining implied volatilities. Finally, the net pairwise connectedness measures suggest that OVX is constantly at the net receiving end vis-a-vis the majority of the asset classes’ implied volatilities. Those findings are of major importance for portfolio and risk management in terms of asset allocation and diversification.

The wisdom of the Twitter crowd in the stock market: Evidence from a fragile state

The classical finance theory postulates that markets are informationally efficient and that the actions of arbitrageurs always bring stock prices to their correct values. Behavioural finance, on the other hand, emphasises the role of investor sentiment in the formulation of asset prices. In this study, we provide insights into the relationship between textual sentiment extracted from Twitter and stock returns in the fragile market of Zimbabwe between 24 February 2019 and 22 June 2020. Wavelet analysis is used to find the linkages between sentiment and returns in a frequency-time domain. The results from this study show that coherence is persistent and significant in highly volatile periods characterised by increasing inflation as well as during the time COVID-19 was declared a global pandemic. The findings also show that macroeconomic instability, especially hyperinflation, induces fear in investors while the onslaught of black swan events like the COVID-19 pandemic leads to greed in the financial markets as investors become uncertain about the future. The government could, therefore, prioritise macroeconomic stability as the high coherence between sentiment and returns during a crisis like the COVID-19 pandemic may lead to a crashing of the stock market. Classical finance theory, therefore, falls short in explaining the stock market returns as the evidence in the study shows that investors are susceptible to investor sentiment.