Leverage and Volatility Feedback Effects in High-Frequency Data

By Tim Bollerslev, Julia Litvinova, George Tauchen. 2006

471 citations recorded by the source corpus.

Subfields: Finance

View Paper: https://www.semanticscholar.org/paper/63f658586d5a36290b4a4d2cc9ba226f3c6558e3

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Citations in the corpus

MGARCH models: Trade-off between feasibility and flexibility (2018, background): “…evidence of asymmetries in the responses of conditional variances and covariances to positive versus negative past returns; see, for example, Bollerslev et al. (2006) for a comprehensive list of references with empirical evidence about the asymmetric response of volatility to past returns…”
The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures (2011, methodology): “Bollerslev, Litvinova, and Tauchen (2006) and Asai, McAleer, and Medeiros (2009) present recent evidence on this empirical regularity.”
Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns (2010, background): “…stock are again reported in the supplementary 25 This is consistent with the corresponding plots for high-frequency S&P500 futures returns in Bollerslev et al. (2006), which show even more pronounced negative cross-correlations for negative lags along with cross-correlations close to zero…”
HAR Modeling for Realized Volatility Forecasting (2012, unknown): “It is well known that volatility tends to increase more after a negative shock than after a positive shock of the same magnitude: this is the so-called leverage effect (see [30, 27, 50] and more recently [19]).”
The Leverage Effect Puzzle: Disentangling Sources of Bias at High Frequency (2011, background): “High frequency data can be employed to estimate the correlation between volatility levels and returns, but only over longer horizons, as it is indeed employed in Bollerslev et al. (2006). We consider a different problem: the nature of the “leverage effect puzzle” we identify lies in the fact that it”
A Discrete-Time Model for Daily S & P500 Returns and Realized Variations: Jumps and Leverage Effects (2009, unknown): “…the stochastic volatility literature, we might naturally expect that the disturbances in the return and volatility equations are correlated due to contemporaneous (at the daily level) leverage and/or volatility feedback effects; see, e.g., the recent empirical analysis in Bollerslev et al. (2006b).”
Risk and return: Long-run relations, fractional cointegration, and return predictability $ (2013, background): “…responds asymmetrically to lagged negative and positive returns, typically referred to as a leverage effect (see, e.g., Black, 1976; Nelson, 1991; Bollerslev, Litvinova, and Tauchen, 2006); and counter to the implications from a traditional risk-return trade-off-, or volatility feedback-, type…”
A Semiparametric Stochastic Volatility Model (2012, unknown): “Bollerslev, Diebold and Ebens (2001), Bollerslev, Litvinova and Tauchen (2006) , Chen and”
Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling (2012, unknown): “This fact is not totally surprising since it echoes the results of Chernov et al. (2003) and Bollerslev et al. (2006), who also estimate (among other models) a two-factor aﬃne model on S&P500 returns via eﬃcient method of moments (using an auxiliary GARCH model) and ﬁnd the correlation coeﬃcient…”
A behavioral explanation for the negative asymmetric return–volatility relation (2008, unknown): “As pointed out by Bollerslev et al. (2007), most studies show a negative correlation between current return shocks and future volatility, with some studies illustrating that negative news is associated with a larger increase in volatility than positive news.”
Liquidity Shocks and Stock Market Reactions (2014, background): “…(1976) and Christie (1982), that is, as asset prices decline, companies’ leverage increases and they become riskier, hence volatility also increases (see, e.g., Nelson, 1991; Engle and Ng, 1993, Bekaert and Wu, 2000, Bollerslev, Litvinova, and Tauchen, 2006 and An, Ang, Bali, and Cakici, 2013).”
Nonparametric Estimation of the Leverage Effect: A Trade-Off Between Robustness and Efficiency (2017, unknown): “Their estimates using the VIX are substantially more stable than the estimates using absolute returns as a volatility proxy in Bollerslev, Litvinova, and Tauchen (2006).”