A Discrete-Time Model for Daily S & P500 Returns and Realized Variations: Jumps and Leverage Effects

By Tim Bollerslev, U. Kretschmer, Christian Pigorsch, George Tauchen. 2009

233 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/5eac291586b66a2a0d817a106423c194f1a07dee

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Citations in the corpus

Option Valuation with Observable Volatility and Jump Dynamics (2015, unknown): “Et [exp (Rt+1)] = exp (r + λzhz,t + λyhy,t) , (11)”
Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns (2010, unknown): “However, the related empirical evidence in Bollerslev et al. (2008) suggests that the asymmetry works almost exclusively through the diffusive component.”
Forecasting Volatility Using High-Frequency Data (2011, unknown): “Li (2002) compares the ARFIMA forecasting power to that of implied volatility from options on three currencies. Martens and Zein (2002) question the statistical validity of Li (2002) because overlapping data are used in the forecast evaluation. For the S&P 500, YEN/USD, and Light, Sweet Crude Oil Ma”
The Volatility of Realized Volatility (2005, background): “Both extensions are also considered in Bollerslev et al. (2005) as part of a highly accurate three–equation auxiliary model for returns and realized variations that can also be used for indirect inference.”
Nonparametric Estimation of the Leverage Effect: A Trade-Off Between Robustness and Efficiency (2017, background): “Finally, Bollerslev et al. (2009) found that the leverage effect works primarily through the continuous components.”
Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions? (2011, methodology): “The first model uses a heterogeneous autoregressive (HAR) specification (Corsi (2003), Andersen, Bollerslev, and Diebold (2007)) of log(RV ). A second model allows different components of log(RV ) to have different decay rates (Maheu and McCurdy (2007)). We also consider two ways to link RV to the v”
Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting (2010, background): “…and Shephard (2006), Lee and Mykland (2008) and Aı̈t-Sahalia and Jacod (2009), as well as the empirical studies of and Maheu and McCurdy (2004); Bollerslev et al. (2009); Andersen et al. (2007); nonparametric estimation in the presence of jumps, as in Bandi and Nguyen (2003); Johannes…”
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.”
On the return-volatility relationship in the Bitcoin market around the price crash of 2013 (2017, unknown): “There is ample evidence that negative shocks to equities generate more volatility than positive shocks of the same magnitude (Glosten et al., 1993; Bollerslev et al., 2007).”
Realising the future: forecasting with high frequency based volatility (HEAVY) models (2010, background): “25: 197–231 (2010) DOI: 10.1002/jae Bollerslev et al. (2009) model multiple volatility indicators and daily returns, where the return model has a conditional variance which is contemporaneous realised variance.”
Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling (2012, unknown): “…well known that volatility tends to increase more after a negative shock than after a positive shock of the same magnitude see e.g. Christie (1982); Campbell and Hentschel (1992); Glosten et al. (1989) and more recently Bollerslev et al. (2006), Bollerslev et al. (2009) and Martens et al. (2009).”
Realised quantile-based estimation of the integrated variance (2010, background): “Diebold (2007) and Bolleslev, Kretschmer, Pigorsch, and Tauchen (2009) also point to its importance for the empirical”