Fear Gauge Research Articles

How does fear spread across asset classes? Evidence from quantile connectedness

PurposeThis study aims to investigate the connectivity among four principal implied volatility (“fear”) markets in the USA.Design/methodology/approachThe empirical analysis relies on daily data (“fear gauge indices”) for the period 2017–2023 and the quantile vector autoregressive (QVAR) approach that allows connectivity (that is, the network topology of interrelated markets) to be quantile-dependent and time-varying.FindingsExtreme increases in fear are transmitted with higher intensity relative to extreme decreases in it. The implied volatility markets for gold and for stocks are the main risk connectors in the network and also net transmitters of shocks to the implied volatility markets for crude oil and for the euro-dollar exchange rate. Major events such as the COVID-19 pandemic and the war in Ukraine increase connectivity; this increase, however, is likely to be more pronounced at the median than the extremes of the joint distribution of the four fear indices.Originality/valueThis is the first work that uses the QVAR approach to implied volatility markets. The empirical results provide useful insights into how fear spreads across stock and commodities markets, something that is important for risk management, option pricing and forecasting.

Flight to Lottery Ahead of FOMC Announcements: Institutional Investors or Retail Investors?

AbstractThis paper studies the pre‐Federal Open Market Committee (FOMC) announcement drift at the stock level. We hypothesize that investors have a higher propensity to speculate before the monetary policy announcements by the FOMC, due to the resolution of uncertainty and associated reduction in investors' fear. Indeed, we find evidence that there exists a drift of lottery‐like stocks in the pre‐FOMC window, when investors' fear gauge is lower, together with higher demand for lottery‐like stocks and higher realized skewness. Moreover, we show that the demand for lottery‐like stocks ahead of FOMC announcements is more prominent among institutional investors than retail investors. Our findings also identify the key role of transient and quasi‐index institutional investors in our documented flight‐to‐lottery effect. Our findings advance the ongoing debates about the role of firms' investor heterogeneity in determining how monetary policy affects corporate managers' decisions. Our paper has important implications for central banks and managers by showing that investors' preference for lottery‐like stocks increases before FOMC announcements.

The Impact of Government Response on the US Stock Market during the COVID-19 Pandemic

Although pandemics have occurred with some degree of frequency over the past century, the degree to which social and economic pandemic-related government responses have been used across the globe during the COVID-19 pandemic period has been unprecedented. Using the S&P 500 stock market index return over the period January 2020-June 2021as our dependent variable in a regression-based model, we measure, via a Government Response Index, the extent to which government response impacted stock market returns. We also include the VIX (“fear gauge”), Market Value, Trading Volume, and Price-to-Earnings Ratios to control for other factors that may have impacted the market during the pandemic. Although we found that the VIX was highly negatively correlated with stock market returns during this period, we found very limited evidence of a significant relationship between stock market returns and government response over this time period. Thus, we believe it would be prudent for governments to be more strategic in their approach regarding initiatives if or when a future pandemic should arise, as not all government-related policy initiatives have the intended effect.

Value for Equity Index Options: Expected—Not Realized—Volatility and the Distribution of Forecasts

Models of option returns neglect the distribution of expected asset volatility, unfortunately for not only derivatives traders but also investors who monitor options as <i>fear gauges</i>. Six common GARCH (generalized autoregressive conditional heteroskedasticity) models afford estimates of the physical, rather than the risk-neutral, distribution of anticipated—instead of historical—volatility, as well as of volatility disagreement. This value specification covering nine global equity indexes and five expiries from 1 to 12 months fits implied volatilities closely, with sizeable and robust error-correction speeds out of sample, all else equal. Exploratory backtests of delta-neutral trading rules produce high Sharpe ratios and alphas, with modest drawdowns and skew.

The Effects of Financial Ratios on the Perceived Risk Count for Single Equity VIX

Abstract The determinants of fear gauge from March 2005 to September 2019 are empirically examined with attention to the single equity volatility index (VIX). This study utilized Poisson and Negative Binomial Regressions to investigate the link between perceived risk count and its variables at certain levels of quantiles. The Negative Binomial model was chosen based on the highest log-likelihood value and the lowest the Akaike information criterion (AIC) value to analyze the market psychology condition of investors. The result of the return on equity (ROE), cash conversion cycle (CCC), and dividend payout ratio (DPR) are negatively significant in both medium and higher quantile of perceived risk count. The debt ratio and free cash flow (FCF) positively affect the perceived risk count. The impacts of variables on higher quantile have a greater influence on perceived risk count, followed by medium quantile. JEL classification numbers: G32. Keywords: Perceived Risk Count, Equity VIX, Poisson and Negative Binomial Regressions.

The policy response to COVID-19 pandemic and its impact on the equity market sentiment: The Indian experience.

We examine the dynamics of the impact of the evolving policy response during the COVID‐19 pandemic on the equity market sentiment in India. We operationalise our study by examining the India VIX, the fear gauge of the Indian equity market as an indicator for the market sentiment, and the country level Government Response Index of the Blavatnik School of Government, Oxford University as an indicator for the policy response. The relation is examined through the Markov‐switching model using high‐frequency daily data from January 30, 2020, to May 31, 2021. The evidence suggests that the policy response has a positive impact on the market sentiment when the market is fearful. Further, the evidence suggests that both the high‐fear state and the low‐fear state of the market sentiment given by the model are short‐lived indicating heightened volatility and possible speculation during the ongoing pandemic in the Indian equity market.

The dynamic interrelations of oil-equity implied volatility indexes under low and high volatility-of-volatility risk

This study employs the Markov-switching vector correction model (MRS-VECM), in which the magnitude of cointegration and correlation are conditional on the regime of the volatility of volatility indexes, to examine the dynamic interrelations between the oil-equity volatility indexes. The CBOE crude oil volatility index (OVX) and stock market volatility index (VIX) are used as a proxy of the oil and equity market volatility index, respectively. Our empirical results and their implications are summarized as follows. First, there is a long-run cointegration relationship between OVX and VIX. Adjustments maintaining the relationship take place on OVX, which is evidence that the revelation of investor fears, proxied by the volatility index, is focused on the equity market. Second, the magnitude of cointegration and correlation between the OVX and VIX under the regime of high volatility-of-volatility risk is greater than that at the low risk. These results imply that the fear gauge of oil and equity markets moves in tandem, particularly during times of economic distress and uncertainty.

Volatility timing, sentiment, and the short-term profitability of VIX-based cross-sectional trading strategies

This paper explores the profitability of simple short-term cross-sectional trading strategies based on the implied volatility index (VIX), often referred to as an “investor fear gauge” in the stock market. These strategies involve holding sentiment-prone stocks when VIX is low and sentiment-immune stocks when VIX is high and generate significantly higher excess returns than the benchmark long–short portfolios that do not condition on VIX. We show that the profitability of our trading strategies is not subsumed by the well-known risk factors or transaction cost adjustments. Our findings are consistent with the theory of delayed arbitrage and the synchronization problem of Abreu and Brunnermeier (2002).

Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic

This study investigates the dependence between extreme returns of West Texas Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well as the predictive power of OVX to generate accurate Value at Risk (VaR) forecasts for crude oil. We focus on the COVID-19 pandemic period as the most violate in the history of the oil market. The static and dynamic conditional copula methodology is used to measure the tail dependence coefficient (TDC) between the variables. We found a strong relationship in the tail dependence between negative returns on crude oil and OVX changes and the tail independence for positive returns. The time-varying copula discloses the strongest tail dependence of negative oil price shocks and the index changes during the COVID-19 health crisis. The findings indicate the ability of the OVX index to be a fear gauge with respect to the oil market. However, we cannot confirm the ability of OVX to improve one day-ahead forecasts of the Value at Risk. The impact of investors’ expectations embedded in OVX on VaR forecasts seems to be negligible.

Investor sentiment, realized volatility and stock returns

PurposeThis paper examines the relationship between volatility, sentiment and returns in terms of levels and changes for both lower and higher data frequencies using quantile regression (QR) method.Design/methodology/approachIn the first step, the study applies the Granger causality test to understand the causal relationship between realized volatility, returns and sentiment as levels and changes. In the second step, the study employs a QR method to investigate whether investor sentiment and returns can predict realized volatility. This regression method gives robust results irrespective of distributional assumptions and to outliers in the dependent variable.FindingsEmpirical results show that the VIX volatility index is a better fear gauge of market-wide investors' sentiments and has a predictive power for future realized volatility in terms of levels and changes for both higher and lower data frequencies. This study provides evidence that the relationship between realized volatility, investor sentiment and returns, respectively, is not symmetric for all quantiles of QR, as opposed to OLS regression. Furthermore, this work supports the behavioral theory beyond leverage hypothesis in explaining the asymmetric relation between returns and volatility at higher and lower data frequencies.Originality/valueThis paper adds to the limited understanding of investor sentiment’s impact on volatility by proposing a QR model which provides a more complete picture of the relationship at all parts of the volatility distribution for both higher and lower data frequencies and in terms of levels and changes. To the author knowledge, this is the first paper to study the volatility responses to positive and negative sentiment changes for developed market and to use both lower and higher data frequencies as well as data in terms of levels and changes.

Relationship between Bitcoin Exchange Rate and Other Financial Indexes in Time Series

The Bitcoin exchange rate (BER) is influenced by many variables such as human speculation and policies and, thus, is dependent on the financial system. The fluctuation of BER submitted has been extensively investigated. However, the correlation analysis of the short- and long-term effects by indicators of online sentiment is unexplored. Therefore, this study establishes a VAR model for BER which provides a framework to the Google search volume index (SVI), the investor fear gauge (VIX), and the S&amp;P500 Index. The findings of the analysis suggest that BER and Google SVI have a Granger causality feedback relationship in both the short- and long-term co-integration equilibrium, and the VIX is significantly related to BER in the long-term co-integration.

Gold and silver prices, their stocks and market fear gauges: Testing fractional cointegration using a robust approach

The present paper investigates the long-run relationships between daily prices, stocks and fear gauges of gold and silver by employing an updated fractional cointegrating framework, that is, the Fractional Cointegrating Vector Autoregression (FCVAR). The initial unit root tests results indicate that the series are I(d)s with values of d around 1 in all cases, and these are homogenous in the paired cointegrating series. Evidence of cointegration is found in the three pairs (prices, stocks and market gauge indices), while these cointegrations are only time-varying in the case of market gauge indices for the commodities. The fact that cointegration exists in prices and stocks of gold and silver implies the possibility that gold and silver prices and stocks can interchangeably be used to access the performances of the commodity markets, with the recommendation that the two commodities are not to be traded in the same portfolio.

The VIX Index Under Scrutiny of Machine Learning Techniques and Neural Networks

The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the market’s expected volatility on the S&P 500 Index, calculated and published by the Chicago Board Options Exchange (CBOE). It is also often referred to as the fear index or the fear gauge. The current VIX index value quotes the expected annualized change in the S&P 500 index over the following 30 days, based on options-based theory and current options-market data. Despite its theoretical foundation in option price theory, CBOE’s Volatility Index is prone to inadvertent and deliberate errors because it is weighted average of out-of-the-money calls and puts which could be illiquid. Many claims of market manipulation have been brought up against VIX in recent years. This paper discusses several approaches to replicate the VIX index as well as VIX futures by using a subset of relevant options as well as neural networks that are trained to automatically learn the underlying formula. Using subset selection approaches on top of the original CBOE methodology, as well as building machine learning and neural network models including Random Forests, Support Vector Machines, feed-forward neural networks, and long short-term memory (LSTM) models, we will show that a small number of options is sufficient to replicate the VIX index. Once we are able to actually replicate the VIX using a small number of S&P options we will be able to exploit potential arbitrage opportunities between the VIX index and its underlying derivatives. The results are supposed to help investors to better understand the options market, and more importantly, to give guidance to the US regulators and CBOE that have been investigating those manipulation claims for several years.

Examining Asymmetric Relationship between India VIX, Nifty 50 Returns and Trading Volume: A case of Quantile Regression

The study investigated one of the vital issues on market microstructure, the relationship between volatility, stock returns and trading volume on Indian stock market for daily frequency from 2nd March 2009 to 31st July 2018. The investor’s fear gauge measure of VIX was used as a volatility variable. The asymmetric impact of returns was estimated on volume and volatility changes. The MWALD granger test revealed that trading volume is undirectionally caused be negative returns and implied volatility VIX. Feedback relationship exists between positive returns, negative returns and VIX. Also positive and negative returns have a bi-directional causality. But the results of Toda-Yamamoto only capture the central values of dependent variable’s distribution. Therefore we also estimate Quantile Regression models. The asymmetric significant relation of stock returns on changes of volatility and volume distribution was found and stronger at extreme ends of dependent variable’s distribution. The study supports the behavioral justification for negative return-volatility contemporaneous relationship but not unconditionally. The evidence of volatility feedback and leverage hypothesis was also significant for lagged period. The contemporaneous negative relationship was found between volatility and volume changes highlighting that investors in India are risk-averse and informed. But the positive lagged effect of changes in volatility on trading volume supports SAIH and affirms the fact that when information gets assimilated with time, noise traders entre the market and increase liquidity. Thus their presence increases trading volume with increase in VIX level.

COVID-19 Times: Where Lies Financial Safety?

This study examines the impact of multiple facets of the COVID-19 (C-19) pandemic on return variation and volatility of different financial securities. These facets include morbidity and mortality rates; market sentiment and policy interventions. Results suggest that the effects of the wide-ranging policy responses have positively affected returns; however, it bears no effect on the unrelenting volatility – almost across all assets. We report that our C-19 index, fear gauges and sentiment index are plausible and significant predictors for asset return and volatility changes in the pre-pandemic announcement (PA) sample. The variations in return and volatility in USD, gold and bond premia are affected by infection fundamental and sentiment related changes across periods. When it comes to equities only changes in EUROSTOXX’s return and volatility changes are significantly predicted by infection data and policy changes. For the US equities only economic policy intervention influences return and volatility shifts in the post PA period. We conclude that predictions for bond premia and USD changes by investors sentiment display investors’ flight to safety across samples. Overall, the global price recovery in several financial securities is hinging on policy intervention and is adding to the future uncertainty, potential bubble formation and testing the tightness of USD’s reserve currency status when global equities and gold prices are simultaneously picking up.

Time series analysis of Cryptocurrency returns and volatilities

There is a significant interest in the growth and development of cryptocurrencies, the most notable ones being Bitcoin and Ripple. Global trading in these cryptocurrencies has led to highly speculative and “bubble-like” price movements. Since these cryptocurrencies trade like stocks, provide a feasible alternative to gold and appreciate during uncertain times, it can be hypothesized that their prices are partly determined by the global stock indices, gold prices, and fear gauges such as the VIX and the US Economic Policy Uncertainty Index. In this paper, we test this hypothesis by conducting a time series analysis of returns and volatilities of Bitcoin and of Ripple. We use the Autoregressive-moving-average model with exogenous inputs model (ARMAX), Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model, Vector Autoregression (VAR) model, and Granger causality tests to determine linkages between returns and volatilities of Bitcoin and of Ripple. We find that the Bitcoin crash of 2018 could have been explained using these time series methods. We also find that returns of global stock markets and of gold do not have a causal effect on Bitcoin returns, but we do find returns on Ripple have a causal effect on Bitcoin prices.

What Do Index Options Teach Us About COVID-19?

Risk-neutral distributions of the S&P 500 are informative about the COVID-19 pandemic beyond what one can learn from index values and the market fear gauge of the VIX alone. We learn that, on February 20, 2020, the index did not yet reflect the impending crisis. Only on March 16, 2020, was the full impact visible, with a pronounced bimodality for longer-maturity options revealing a sizeable crash scenario. The corresponding physical distribution is more symmetric and features a high-volatility crash scenario. Firms bought crash protection ahead of the index crash, whereas retail customers bought it as the index was already recovering.

Exploiting the heteroskedasticity in measurement error to improve volatility predictions in oil and biofuel feedstock markets

We examine whether heteroskedasticity in measurement errors improves the volatility forecasting ability of the Heterogenous Autoregressive (HAR) model in crude oil and biofuel feedstock markets. We also examine the incremental explanatory power of jumps and the investor fear gauge (IFG) over heteroskedasticity in measurement errors in improving the volatility forecasting ability of the HAR model in each of these markets. For the in-sample evaluation, we find that exploiting the heteroskedasticity of measurement errors in the HAR model improves the model's goodness of fit (measured by adjusted R2) by up to 10% depending on the market. IFG has a significant incremental role over heteroskedasticity in measurement errors in improving the fit of the HAR model in both the crude oil and biofuel feedstock markets, while jumps have a significant incremental role in improving the fit of the HAR model in the crude oil market, but not the biofuel feedstock markets. For the out-of-sample forecasts, including regime switching improves volatility predictions in the corn and wheat markets across all forecasting horizons, while for the soybean market, including regime switching improves the performance of multi-step volatility forecasts. In the out-of-sample forecasts the best ranked models almost always include heteroskedasticity of measurement error and IFG.

What do Index Options Teach us About Covid-19?

Risk-neutral distributions of the S&P 500 inform about the COVID-19 pandemic beyond what one can learn from index values and the market fear gauge VIX alone. We learn that, on February 20, 2020, the index did not reflect the impending crisis yet. Only on March 16, 2020, was the full impact visible, with a pronounced bimodality for longer-maturity options showing a sizeable crash scenario. The corresponding physical distribution is more symmetric and features a high-volatility crash scenario instead. Firms bought crash protection ahead of the index crash, while retail customers bought it as the index was already recovering.

Does Fear has Stronger Impact than Confidence on Stock Returns? The Case of Asia-Pacific Developed Markets

Employing data from Australia, Hong Kong, and Japan over the period between January 2004 to December 2017, this study investigates the relationship between investor sentiment and stock returns. We analyze two reversed sentiment indicators, namely Consumer Confidence Index (CCI) and Volatility Index (VIX), in two conversing situations: low and high sentiment. The empirical evidence suggests that sentiment has a significant link with concurrent returns, but its influence seems to wipe out quickly as the little to no return predictability is detected. More importantly, we find that “investor fear gauge” (VIX) generates a more significant contemporaneous effect on market returns than investor confidence. The impact on future returns, on the contrary, is inconclusive since low CCI and VIX dominate the opposite ones most of the time.