Investor Fear Gauge Research Articles

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).

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&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.

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.

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.

Web-Based Investor Fear Gauge and Stock Market Volatility: An Emerging Market Perspective

This article considers web-based global investors’ crash fears as a gauge of global investors’ fears, and examines its effect on stock market volatility in a sample of emerging stock markets. We show that an increase in global investors’ crash fears significantly affects the volatility of stock index returns in emerging markets. The results are robust to the inclusion of the conventional investor sentiment/fear gauge measure, VIX. Thus broadening the set of measures of global investors’ fears is important when explaining emerging market volatilities. JEL Classification: F30, G11, G15

Introducing the BITIX: The Bitcoin Fear Gauge

We acquire a unique dataset of high-frequency traded prices for bitcoin call and put options from the Deribit cryptocurrency derivatives exchange, by 15-minute sampling via the application programming interface. We use these prices to construct a term structure of bitcoin implied volatility indices using a variance swap fair-value formula that is employed by the CBOE for the VIX, an index commonly referred to as the ‘investor fear gauge’ for the US stock market. Employing over 7 million option prices, we construct the bitcoin implied volatility indices with maturities from one week to three months, sampled every 15 minutes from March 2019 to March 2020. We discuss the features of the index and the associated bitcoin variance risk premia, with three different regular time partitions for realised variance, viz. 15-minutes, hourly and daily. We also examine the relationship between bitcoin’s 30-day realised variance, volatility index and variance risk premium with their equivalent for US equities, oil, gold, the USD/EUR exchange rate and the 10-year US treasury note.

The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market

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

Is VIX Still the Investor Fear Gauge? Evidence for the US and BRIC Markets

We investigate the relationships of the VIX with US and BRIC markets. In detail, we pick up the analysis from the point left off by (Sarwar, 2012), and we focus on the period: Jan 2007 - Feb 2018, thus capturing the relations before, during and after the 2008 financial crisis. Results pinpoint frequent structural breaks in the VIX and suggest an enhancement around 2008 of the fear transmission in response to negative market moves; largely depending on overlaps in trading hours, this has become even stronger post-crisis for the US, while for BRIC countries has gone back towards pre-crisis levels.

The Global Financial Cycle, Bank Capital Flows and Monetary Policy. Evidence from Norway

We investigate the importance of a global financial cycle for gross capital inflows based on monthly balance sheet data for Norwegian banks. The VIX index has been interpreted as an investor fear gauge and associated with a global financial cycle. This index has also been found to impact real activity. We include both a global activity variable and the VIX index in our structural VAR model of capital inflows. We find that when global activity falls, banks' foreign funding share falls. Our results suggest that global real activity rather than a global financial cycle is a main driver behind the volume of bank capital inflows. We also study domestic monetary policy and implications for capital flows. Domestic monetary policy helps absorb VIX shocks and there is no indication of procyclical (carry trade) effects on funding. Monetary policy affects activity and inflation in a standard fashion, and the exchange rate acts as a buffer when shocks hit the economy.

A specialised volatility index for the new GICS sector - Real estate

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

The Dynamics of House Prices in Israel and the Effect of the Investor's Fear Gauge

In this paper, we investigate the macroeconomic drivers of house prices in Israel, the OECD country with the highest growth rate in recent years, and test for the divergence of observed prices from underlying fundamentals using cointegration analysis and error correction models for the 1998 to 2013 period. While the recent surge in house prices is partially explained by fundamentals such as population growth, low unemployment, and interest rates along with supply constraints, the results suggest that prices have deviated from fundamental values by approximately 20% from 2009 onwards. Stock market volatility is found to be a key predictor of house prices in the short run, indicating a shift towards increased investment in the housing market when other asset classes, notably the stock market, are perceived as very risky.

Stock market return predictability: Google pessimistic sentiments versus fear gauge

AbstractThis study aims at comparing Google Search Volume Indices (GSVIs—including market crash and bear market) and VIX (Investor Fear Gauge Index) in terms of explaining the S&P 500 returns. The ...

Informativeness of the market news sentiment in the Taiwan stock market

This study constructs a market “aggregate news sentiment index” (ANSI) based on Chinese financial news relating to all the listed firms on the Taiwan Stock Exchange. We analyze the relationship between the ANSI and the market responses, which includes the returns, trading values, turnover ratios and volatility index, and the ANSI is divided into five regions. Our empirical results reveal that with an increase in the ANSI in the previous month, there is a corresponding increase in the trading value along with a reduction in the investor fear gauge of the Taiwan volatility index (TVIX). Granger causality tests indicate that both the weekly and monthly ANSIs could be leading indicators of market returns if the different regions of the ANSI are accounted for. In a potential application of our findings to portfolio management, we find that when the lagged term of the ANSI is classified in the highest regions, value-weighted portfolios comprising stocks with the highest market values, lowest price-to-book ratios and lower turnover ratios will earn the highest returns. The efficiency of the news sentiment in portfolio applications is consistent over time. This study contributes to the existing literature by demonstrating that the news sentiment level reflected in qualitative news reports can be effectively incorporated as a proxy to provide valuable support to portfolio decision making.

Time-varying relationship of news sentiment, implied volatility and stock returns

ABSTRACTI examine the relationship between aggregate news sentiment, S&P 500 index (SPX) returns, and changes in the implied volatility index (VIX). I find a significant negative contemporaneous relationship between changes in VIX and both news sentiment and stock returns. This relationship is asymmetric whereby changes in VIX are larger following negative news and/or stock market declines. Vector autoregression (VAR) analysis of the dynamics and cross-dependencies between variables reveals a strong positive relationship between previous and current period changes in implied volatility and stock returns, while current period and lagged news sentiment has a significant positive (negative) relationship with stock returns (changes in VIX). I develop a simple trading strategy whereby high (low) levels of implied volatility signal attractive opportunities to take short (long) positions in the underlying index, while extremely negative (positive) news sentiment signals opportunities to enter short (long) index positions. The investor fear gauge (VIX) appears to perform better than news sentiment measures in forecasting future returns.

Partial moment volatility indices

AbstractForward‐looking partial moment volatility indices are developed using state‐pricing, called the bear index (BEX) and bull index (BUX). Using S&P 500 index (SPX) option prices, we find that BEX and BUX provide superior forecasts for the lower and upper partial moments of future market realised volatility, respectively. We examine the relation between SPX returns and changes in BEX and BUX at the daily level. Results are consistent with the volatility feedback hypothesis. Further, we show that BEX may be more suitable as the ‘investor fear gauge’ than VIX.

Modelling the joint dynamics of oil prices and investor fear gauge

This paper investigates the interdependent relationship between WTI returns and the newly published crude oil volatility index (OVX), combining a cross-correlation function approach, a time-varying parameter (TVP) GARCH model, and a multivariate regression analysis, by which the direction, dynamics, magnitude and asymmetry of their relationship are modelled. At the same time, the implied volatility indexes in the stock market and the gold market are considered for comparison. It is found that there is a significant unidirectional causality-in-mean from WTI returns to the OVX changes, while causality-in-variance from the OVX changes to WTI returns is also significant. The contemporaneous relationship between the OVX changes and WTI returns is significantly negative, and their asymmetric relationship implies that OVX has played a greater role as a gauge of investor fear than as risk preference. The time-varying results indicate that the relationship between the changes in OVX and WTI returns is not always negative.

Does the US Fear Gauge Impact on the Investor Fear Gauge in the Emerging Markets?

This article investigates the investor fear gauge linkages among the American and emerging markets, such as, India (India volatility index (IVIX)), South Korea (Volatility Korea Composite Stock Price Index (VKSOPI)), South Africa (South African Volatility Index (SAVI)) and Russian (Russian Volatility Index (RTSVX)). This article further captures the diffusion and jump relationships between the volatility indices (VIXs) in US and emerging markets by applying the correlated bivariate Poisson (CBP) jump model. The empirical result found that the change in the VIX in the US has a substantial impact over the change in the VIXs of emerging markets. The changes in the VIXs exhibit individual jump behaviour. Finally, this article found the time-varying joint jump intensity between the VIXs in US and emerging markets. JEL Classification: C32, C5

Pairs trading: does volatility timing matter?

Pairs trading is a dollar-neutral trading strategy. Using the components of two major stock indices, the S&P 500 and the Nikkei 225, this article deals with the performance of a pairs trading system based on various pairs selection methods (distance, stationarity, cointegration) over a 10-year period. On both markets, using a classical framework, cointegration appears superior and effective. On the U.S. market and also in Japan to a lower extent, pairs trading strategies exhibited an impressive performance during the 2008 financial crisis. Bearish periods are associated with a high level of the VIX index: the ‘investor fear gauge’. Using a modified trading system, this article examines the link between pairs trading performance and volatility/VIX timing. It is shown that for the best selection technique (cointegration), timing volatility has no economic value in a pairs trading context.

Real Estate Volatility Index: An Application of the State Preference Approach

This paper develops a real estate volatility index (RVX) based on Real Estate Investment Trusts (REITs) over an extensive period of 1996-2012. In line with the Chicago Board Options Exchange (CBOE) Volatility Index (VIX), our RVX, computed on a daily basis, is an ex-ante measure of the next 30-day real estate volatility. The state preference approach employed does a good job in incorporating different market conditions and special economic events into RVX. Thus, our RVX has a reasonably high correlation of 0.85 with the realized volatility of REITs and for all practical purposes, is unbiased. As the first attempt aiming at creating a core systematic risk measure for the real estate market, our RVX can serve as a specialised investor fear gauge for this important asset class and supplementing other indices such as VIX and the Merrill lynch Option Volatility Estimate (MOVE). In addition, RVX also brings a variety of other significant applications, including being used for a market timing tool, helping shape hedging strategies and as an input to compute a realized-implied volatility spread which might be a predictor for future REIT returns.

The implied volatility index: Is ‘investor fear gauge’ or ‘forward-looking’?

The paper aims to examine implied volatility as the investor fear gauge or/and forward-looking expectation of future stock market volatility within emerging markets setting-India VIX. The earliest results evidenced that VIX is the gauge of investor fear, where in the expected stock market volatility rises when the given market is declined. It is also proven that expected volatility is being unbiased estimate of the actual return volatility (30-calendar days); hence, during the market turmoil VIX likely to be biased. Lastly, it is suggested that the nervousness of investor yields potential profit to the options seller (market crises). Thus, our research has practical implications for various reasons, such as, portfolio risk management, stock market volatility forecast, and options pricing.