Sandra Ferreruela

Does herding affect volatility? Implications for the Spanish stock market

According to rational expectation models, uninformed or liquidity trading make market price volatility rise. This paper sets out to analyse the impact of herding, which may be interpreted as one of the components of uninformed trading, on the volatility of the Spanish stock market. Herding is examined at the intraday level, considered the most reliable sampling frequency for detecting this type of investor behavior, and measured using the Patterson and Sharma (Working Paper, University of Michigan–Dearborn, 2006) herding intensity measure. Different volatility measures (historical, realized and implied) are employed. The results confirm that herding has a direct linear impact on volatility for all of the volatility measures considered, although the corresponding intensity is not always the same. In fact, herding variables seem to be useful in volatility forecasting and therefore in decision making when volatility is considered a key factor.

Testing Intentional Herding in Familiar Stocks: An Experiment in an International Context

This paper examines the intentional herd behaviour of market participants within different international markets (Germany, United Kingdom, United States, Mexico, Japan, Spain and France) using a new approach that permits the detection of even moderate herding over the whole range of market return. This approach compares the cross-sectional deviation of returns of each of the selected markets with the cross-sectional deviation of returns of an “artificially created” market free of herding effects. We suggest that intentional herding is likely to be better revealed when we analyse familiar stocks. The results show that only the Spanish market exhibits a significant herding effect.

Market Sentiment: A Key Factor of Investors’ Imitative Behaviour

The aim of this paper is to explore herding behaviour among investors to determine its rational and emotional component factors and identify relationships among them. We apply causality tests to evaluate the impact of return and market sentiment on herding intensity. The herding intensity is quantified using the measure developed by Patterson and Sharma (2006). The research was conducted during the period 1997–2003 in the Spanish stock market, where the presence of herding has been confirmed. The results reveal that the herding intensity depends on past returns and sentiment or subjective assessments and confirm the presence of both a rational and an emotional factor.

Detecting Intentional Herding: What Lies Beneath Intraday Data in the Spanish Stock Market

This paper examines the intentional herd behaviour of market participants, using Lis test to compare the probability distributions of the scaled cross-sectional deviation in returns in the intraday market with the cross-sectional deviation in returns in an ‘artificially created’ market free of intentional herding effects. The analysis is carried out for both the overall market and a sample of the most representative stocks. In addition, a bootstrap procedure is applied in order to gain a deeper understanding of the differences across the distributions under study. The results show that the Spanish market exhibits a significant intraday herding effect that is not detected using other traditional herding measures when familiar and heavily traded stocks are analysed. Furthermore, it is suggested that intentional herding is likely to be better revealed using intraday data, and that the use of a lower frequency data may obscure results revealing imitative behaviour in the market.

Herding, Volatility, and Market Stress in the Spanish Stock Market

Herding in stock markets is closely linked to market stress and volatility. This paper analyzes the relationship between herding and volatility during days of market stress. The study has been carried out in the Spanish market, given that a significant level of herding has already been detected there. The herding measure implemented is based on intraday data, and both realized volatility and conditional volatility models have been used. The results show evidence of the asymmetric effect of herding on volatility during extreme market movements, which is consistent with the differing psychological implications of extreme up and down market movements.

Are There Herding Patterns in the European Frontier Markets

Abstract This chapter attempts to determine whether there was herding behavior in the nine frontier markets included in the MSCI EFM Central and East Europe and CIS Index during the period 2011–14. Although the characteristics of these markets could suggest a strong mimetic behavior, the results do not seem to confirm this. On the one hand, using different methodologies, we find clear evidence of herding (at an individual market level) only within the Estonian market, although some other markets also show evidence of herd behavior in some extreme or fairly extreme quantiles of the return dispersion distribution. On the other hand, we find that the Bulgaria, Kazakhstan, Ukraine, and Croatia markets are more likely to herd around a global area consensus (the MSCI EFM Index) during both turbulent and calm periods. These results reveal the difficulty of characterizing herding behavior.