Herding and market volatility

In this paper, we explore the impact of investor herding behavior on stock market volatility. We adopt a direct herding measure based on the variation of cross-sectional stock betas. The measure can be readily separated into positive and adverse components, whereby investors herd towards and away from the market portfolio, respectively. Using A-shares listed in the Chinese equity market from August 2005 to March 2021, we show that the market volatility is Granger caused by the measure, and that there exists an asymmetric effect between positive and adverse herding on volatility. Furthermore, we provide robust evidence that the information contained in the herding measure helps generate significantly improved volatility forecasts and add economic value to investors. Our paper not only contributes to the volatility forecasting literature but also advances our understanding of herding in the equity market.     

The Impact of Trades on Daily Volatility

This article proposes a trading-based explanation for the asymmetriceffect in daily volatility of individual stock returns. Previousstudies propose two major hypotheses for this phenomenon: leverageeffect and time-varying expected returns. However, leveragehas no impact on asymmetric volatility at the daily frequencyand, moreover, we observe asymmetric volatility for stocks withno leverage. Also, expected returns may vary with the businesscycle, that is, at a lower than daily frequency. Trading activityof contrarian and herding investors has a robust effect on therelationship between daily volatility and lagged return. Consistentwith the predictions of the rational expectation models, thenon-informational liquidity-driven (herding) trades increasevolatility following stock price declines, and the informed(contrarian) trades reduce volatility following stock priceincreases. The results are robust to different measures of volatilityand trading activity. (JEL C30, G11, G12)     

Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models

The objective of this paper is to select the most optimum model or set of models useful for modeling sixteen of the most popular crypto-currencies associated volatility. Five GARCH models, with different error distributions, are fitted to each of these crypto-currencies. The most effectively fit model or superior set of models is then selected through maximizing the likelihood and minimizing the AIC and BIC information criteria. The reached results prove that the majority of crypto-currencies turn out to be rather effectively modulated via the TGARCH with double exponential distribution. Indeed, the attained findings report an asymmetric effect whereby volatility turns out to increase rather by response to positive shocks than by response to negative shocks, implying an asymmetric effect that differs from that generally observed in stock markets. The increase in volatility, as emanating in response to positive shocks may well have its justification in the uninformed investors’ undertaken herding strategies.     

The destabilizing effect of mutual fund herding: Evidence from China

This paper examines the impact of mutual fund herding on the stability of China's stock market through the lens of idiosyncratic volatility. We show that mutual fund herding significantly amplifies future idiosyncratic volatility up to one-and-half years, suggesting a persistent destabilizing effect. We find that the destabilizing effect is larger for stocks with high mutual fund ownership and in high investor-sentiment periods. Furthermore, mutual fund herding destabilizes the stock market even further after China implemented the share reform in 2011. The destabilizing effect of mutual fund herding is robust to alternative measures of risk and China's institutional setting helps mitigate endogeneity concerns.     

The Determinants of Asymmetric Volatility

Volatility in equity markets is asymmetric: contemporaneousreturn and conditional return volatility are negatively correlated.In this article I develop an asymmetric volatility model wheredividend growth and dividend volatility are the two state variablesof the economy. The model allows both the leverage effect andthe volatility feedback effect, the two popular explanationsof asymmetry. The model is estimated by the simulated methodof moments. I find that both the leverage effect and volatilityfeedback are important determinants of asymmetric volatility,and volatility feedback is significant both statistically andeconomically.     

Industry herding in crypto assets

The aim of this paper is to investigate if herd behaviour is present in crypto assets at industry level. Using price information extracted from coinmarketcap.com between 29 April 2013 and 9 May 2022, we find evidence of herding and reverse herding in the crypto assets market. Concentrated periods of herding and reverse herding are particularly evident in the January 2020–April 2022 Covid period. At industry level, herding is more profound in large sectors with higher volatility. In smaller sectors where ventures are backed by ‘real assets’, very short periods of herding with marginal significance are detected. Reverse herding is present in all industries except Real Estate between June 2021 and May 2022, implying that strategies such as excessive ‘flight to quality’ or/and token picking are at play during the recent crypto crash. We also detect varying asymmetric herding at industry level. This paper further examines the factors that drive such industry herding and reverse herding in the crypto assets market, and our results show that industry concentration and investor sentiments contribute to the probability of herding/reverse herding. Our study provides further insights to the forces that drive the dispersion in crypto assets prices and contribute to the behavioural studies of the crypto market.     

Is volatility clustering of asset returns asymmetric?

Volatility clustering is a well-known stylized feature of financial asset returns. This paper investigates asymmetric pattern in volatility clustering by employing a univariate copula approach of Chen and Fan (2006). Using daily realized kernel volatilities constructed from high frequency data from stock and foreign exchange markets, we find evidence that volatility clustering is highly nonlinear and strongly asymmetric in that clusters of high volatility occur more often than clusters of low volatility. To the best of our knowledge, this paper is the first one to address and uncover this phenomenon. In particular, the asymmetry in volatility clustering is found to be more pronounced in the stock markets than in the foreign exchange markets. Further, the volatility clusters are shown to remain persistent for over a month and asymmetric across different time periods. Our findings have important implications for risk management. A simulation study indicates that models which accommodate asymmetric volatility clustering can significantly improve the out-of-sample forecasts of Value-at-Risk.     

Asymmetric Mean Reversion in European Interest Rates: A Two-factor Model

Abstract

This paper tests for asymmetric mean reversion in European short-term interest rates using a combination of the interest rate models introduced by Longstaff and Schwartz (Longstaff, F.A., Schwarts, E.S. (1992) Interest rate volatility and the ferm structure: A two factor general equilibrium model, Journal of Finance, 48, pp. 1259–1282.) and Bali (Bali, T. (2000) Testing the empirical performance of stochastic volatility models of the short-term interest rates, Journal of Financial and Quantitative Analysis, 35, pp. 191–215.). Using weekly rates for France, Germany and the United Kingdom, it is found that short-term rates follow in all instances asymmetric mean reverting processes. Specifically, interest rates exhibit non-stationary behavior following rate increases, but they are strongly mean reverting following rate decreases. The mean reverting component is statistically and economically stronger thus offsetting non-stationarity. Volatility depends on past innovations past volatility and the level of interest rates. With respect to past innovations volatility is asymmetric rising more in response to positive innovations. This is exactly opposite to the asymmetry found in stock returns.     

Herding and excessive risk in the American stock market: A sectoral analysis

This paper aims to test whether herding behavior is a driving force of excessive market volatility and increasing bubbles in the US stock market at a sectoral level. Trading volume turnover and investors’ sentiment are ubiquitous factors besides market return to fuel herding movement in most sectors. Our sample covers all listed companies in the American stock market over four major turmoil periods. Granger causality test shows that herding is a vital ingredient to increasing bubbles in some sectors, but not all. Moreover, herding and trading volume have an inhibiting effect on both overall and in-sector market volatility in large markets, as opposed to concentrated markets commonly studied in the literature.     

Investors’ Fear and Herding Behavior: Evidence from the Taiwan Stock Market

This article analyzes the influence of investors’ fear on their investment behavior in the Taiwan stock market. This study used the volatility index (VIX) as a barometer of investors’ fear. Our results show that herding behavior increases with the VIX; that is, herding behavior is encouraged by an increase in investors’ fear. Moreover, our results demonstrate that investors react more quickly to bad news than to good news when their fear increases, supporting the hypothesis of the presence of an asymmetric reaction to news. However, investors react more quickly to good news when their fear decreases, indicating an inverse asymmetric reaction. In addition, our empirical results reveal that herding behavior tends to exist on days with a large trading volume.     

Oil vs. gasoline: The dark side of volatility and taxation

This paper investigates the relation between gasoline volatility and crude oil volatility. The objective is to examine whether the so-called asymmetric relation between gasoline and oil prices still holds for volatility, particularly, when considering the taxation effect. The approach hinges on the Volatility Threshold Dynamic Conditional Correlation (VT DCC) model. An application to the U.S. WTI oil volatility and the U.S. premium gasoline volatility is provided from 1990 to 2015. The main results reveal that oil volatility influences gasoline volatility, but without any form of asymmetry. The role of taxation seems to particularly affect the volatility of volatility for gasoline.     

Uncertainty and herding behavior: evidence from cryptocurrencies

The aim of this study is to examine the existence of herding behavior in the cryptocurrency market under uncertainty by employing cross-sectional absolute deviation (CSAD) of returns, ordinary least squares (OLS), generalized autoregressive conditional heteroscedasticity (GARCH) methods and Time-Varying Markov-Switching (TV-MS) model for both overall sample and sub-periods which was determined based on the results of Quandt-Andrews and Bai-Perron breakpoint tests. We utilized the daily data of the 14 leading cryptocurrencies in terms of closing price, market cap and transaction volume. We also used dummy variables to analyze whether or not an asymmetric behavior occurred during the "up and down" market periods. Our results for the overall sample refer to an anti-herding behavior in each model. However, the results of the TV-MS model for the 3rd sub-period (2/28/2017−1/16/2018) imply the existence of a herding behavior in the low volatility regime, an anti-herding behavior occurred during the high volatility regime and the effect of uncertainty was significant on the anti-herding behavior. Finally, our results suggest that there was no significant asymmetric behavior during the "up and down" market periods.     

Asymmetric volatility in the foreign exchange markets

We examine the presence or absence of asymmetric volatility in the exchange rates of Australian dollar (AUD), Euro (EUR), British pound (GBP) and Japanese yen (JPY), all against US dollar. Our investigation is based on a variant of the heterogeneous autoregressive realized volatility model, using daily realized variance and return series from 1996 to 2004. We find that a depreciation against USD leads to significantly greater volatility than an appreciation for AUD and GBP, whereas the opposite is true for JPY. Relative to volatility on days following a positive one-standard-deviation return, volatility on days following a negative one-standard-deviation return is higher by 6.6% for AUD, 6.1% for GBP, and 21.2% for JPY. The realized volatility of EUR appears to be symmetric. These results are robust to the removal of jump component from realized volatility and the sub-samplings defined by structural-changes. The asymmetry in AUD, GBP and JPY appears to be embedded in the continuous component of realized volatility rather than the jump component.     

中国股市羊群效应的区制转移时变性研究

传统研究采用静态CCK模型检验股票市场的羊群效应,但无法描述羊群行为的动态变化以及市场可能受到的外部影响。本文基于中国股市日频交易数据,在静态CCK模型中引入参数的区制转移性质识别股市在不同状态间的转换,并分析中国股市羊群效应和交叉羊群效应的时变特征。研究表明,中国股市运行周期可被划分为两个区制,分别呈现低波动和高波动的行情特征;羊群效应的程度随区制转移而变化,具有区制依存性。其中,沪深股市在高(低)波动区制中,羊群效应更强(弱),相应区制持续时间较短(长);中国台湾股市仅在高波动区制中出现羊群效应,相应区制持续时间较短;中国香港股市无论在低波动区制或是高波动区制中,均不存在羊群效应。此外,沪深A股在低波动区制中对美国股市和中国香港股市存在交叉羊群效应。     

Asymmetry in spillover effects: Evidence for international stock index futures markets

The paper investigates the asymmetry in return and volatility spillovers across futures markets with non-overlapping stock exchange trading hours. The transmission of positive and negative return and volatility shocks is analysed for 104 channels of information conveyance identified by combining 9 developed and 11 emerging markets in markets pairs with non-overlapping trading hours. The asymmetric causality test is employed to daily stock index futures returns and volatilities for the period from 03 October 2010 to 03 October 2014. The paper sheds light on the relatively little explored concept of asymmetry in return and volatility spillovers across markets, providing novel evidence on stabilizing and destabilizing spillover effects.     

An empirical analysis of herd behavior in global stock markets

This paper examines herding behavior in global markets. By applying daily data for 18 countries from May 25, 1988, through April 24, 2009, we find evidence of herding in advanced stock markets (except the US) and in Asian markets. No evidence of herding is found in Latin American markets. Evidence suggests that stock return dispersions in the US play a significant role in explaining the non-US market’s herding activity. With the exceptions of the US and Latin American markets, herding is present in both up and down markets, although herding asymmetry is more profound in Asian markets during rising markets. Evidence suggests that crisis triggers herding activity in the crisis country of origin and then produces a contagion effect, which spreads the crisis to neighboring countries. During crisis periods, we find supportive evidence for herding formation in the US and Latin American markets.     

A behavioural explanation to the asymmetric volatility phenomenon: Evidence from market volatility index

This study examines how the behavioural explanations, in particular loss aversion, can be used to explain the asymmetric volatility phenomenon by investigating the relationship between stock market returns and changes in investor perceptions of risk measured by the volatility index. We study the behaviour of India volatility index vis‐à‐vis Hong Kong, Australia and UK volatility index, and provide a comprehensive comparative analysis. Using Bai‐Perron test, we identify structural breaks and volatility regimes in the time series of volatility index, and investigate the volatility index‐return relation during high, medium and low volatility periods. Regardless of volatility regimes, we find that volatility index moves in opposite direction in response to stock index returns, and contemporaneous return is the most dominating across the four markets. The negative relation is strongest for UK followed by Australia, Hong Kong and India. Second, volatility index reacts significantly different to positive and negative returns; negative return has higher impact on changes in volatility index than positive return across the markets over full‐sample and sub‐sample periods. The asymmetric effect is stronger in low volatility regime than in high and medium volatility periods for all the markets except UK. The strength of asymmetric effect is strongest for Hong Kong and weakest for India. Finally, negative returns have exponentially increasing effect and positive returns have exponentially decreasing effect on the changes in volatility index.     

Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis

This paper applies the vector AR-DCC-FIAPARCH model to eight national stock market indices' daily returns from 1988 to 2010, taking into account the structural breaks of each time series linked to the Asian and the recent Global financial crisis. We find significant cross effects, as well as long range volatility dependence, asymmetric volatility response to positive and negative shocks, and the power of returns that best fits the volatility pattern. One of the main findings of the model analysis is the higher dynamic correlations of the stock markets after a crisis event, which means increased contagion effects between the markets. The fact that during the crisis the conditional correlations remain on a high level indicates a continuous herding behaviour during these periods of increased market volatility. Finally, during the recent Global financial crisis the correlations remain on a much higher level than during the Asian financial crisis.     

Peer performance and the asymmetric timeliness of earnings recognition

This paper investigates the impact of peer performance on the asymmetric timeliness of earnings recognition. We find a positive relationship between peers' weak performance and timely bad news disclosure. Our results are robust to a variety of tests, including instrument variable approach, difference-in-differences analysis, alternative measures and subsample analysis. Consistent with the notion that weak peer performance increases investors' demand for information, the relationship is more profound for firms suffering from high information externality, with weak governance and high information asymmetry. Furthermore, we find that the relationship is difficult to reconcile with the explanation of managers' herding behaviour. In addition, we show that conservative accounting information disclosure due to weak peer performance alleviates managerial bad news hoarding and information asymmetry for underperforming firms, but distorts investment decisions for outperforming firms. We highlight the spillover effect of peer performance on conservative accounting information and the related heterogeneous outcomes.     

The Asymmetric Momentum Effect in the Chinese Class A Share Market Amid Market Swings

This paper investigates the asymmetric momentum effect over time periods following UP and DOWN market states in the Shanghai and Shenzhen Stock Exchanges of the Chinese Class A share market. We show that the post-UP-market momentum effect eclipses the post-DOWN-market momentum effect in unison in both market segments. Notably, the asymmetric pattern of the market-state-dependent momentum effect in the Shenzhen Stock Exchange is outpaced by that found in the Shanghai Stock Exchange. Furthermore, through decomposing momentum returns, we reveal that low liquidity, higher market return volatility, and weak under-reaction of share prices towards firm-specific news jointly contribute to the subdued asymmetry of market-state-dependent momentum returns in the Shenzhen Stock Exchange.