Tails, Fears and Risk Premia

By Tim Bollerslev, V. Todorov. 2010

768 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/57df7482bacc2e9536641074a0957ba2bed7452f

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Citations in the corpus

Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility (2015, positive): “Our finding that the impact of jumps depends critically on the sign of the jump helps explain these results: averaging across both positive and negative jump variation, the impact on future volatility is near 0.5 Bollerslev et al. (2006) were perhaps the first to note that the sign of high-frequency”
Risk, Uncertainty, and Expected Returns (2011, background): “The key assumptions are that γ > 1 (Bollerslev and Todorov (2011), Kelly (2014)).”
Asymmetric learning from financial information (2013, positive): “This is consistent with an increase in risk aversion after negative shocks (Todorov (2010), Bollerslev and Todorov (2011), Guiso et al. (2011)), but in addition can also be driven by traders being overly pessimistic during bad times, as suggested by the experimental results shown here.”
Roughing up Beta: Continuous vs. Discontinuous Betas, and the Cross-Section of Expected Stock Returns (2014, positive): “Our cross-sectional pricing results also complement recent time-series estimates of the equity risk premium reported in Bollerslev and Todorov (2011) and Gabaix (2012), among others, which suggest that a large portion of the aggregate equity premium and the temporal variation therein could be…”
Financial Statement Comparability and Expected Crash Risk (2016, positive): “Bollerslev and Todorov (2011) outline that, since realized crash events are invariably rare and possibly even nonexistent over a limited calendar time span, it is the fear of such events that accounts for a surprisingly large fraction of historically observed crash or crisis events.”
Estimation of Jump Tails (2011, methodology): “In addition, we adjust for the well-known diurnal pattern in volatility, by scaling α with an estimate of the time-of-day continuous variation as in Bollerslev and Todorov (2010). (31)The estimate for the spot variance used in the calculation of the jump intensities depicted in the figure is based o”
Time-Varying Jump Tails (2014, methodology): “First, unlike Bollerslev and Todorov (2011b) we explicitly allow the shape of the jump tails to vary over time.”
A regime-switching Heston model for VIX and S&P 500 implied volatilities (2014, background): “…regime change for GARCH models was identified in Hillebrand (2005), and the importance of jumps was highlighted in Ait-Sahalia et al. (2012), Bollerslev and Todorov (2011), Tauchen and Todorov (2011) and Mencía and Sentana (2013), where evidence from variance swap data suggests that fears…”
Volatility‐Managed Portfolios (2017, neutral): “If anything, jumps seem to be much more likely when volatility is high (Bollerslev and Todorov, 2011), suggesting that our strategy is less exposed to jump risk than the buyand-hold portfolio.”
Rare Macroeconomic Disasters (2012, background): “Bollerslev and Todorov (2011), henceforth BT, use options prices on the S&P 500 from 1996 to 2008 to back out jump (disaster) risk in the underlying stock-price index as priced by investors.”
Systemic Co-Jumps (2017, positive): “Rare, dramatic multi-jumps can be interpreted as systemic co-jumps in the sense of Das and Uppal (2004); that is, as infrequent events that are highly correlated across a large number of assets.(1) Co-jumps are correlated with market-wide information on economic fundamentals, with important conseque”
Understanding Systematic Risk: A High‐Frequency Approach (2020, positive): “6Empirical studies supporting this hypothesis include Bollerslev, Li, and Todorov (2016), Alexeev, Dungey, and Yao (2017), Pan (2002), Eraker, Johannes, and Polson (2003), Eraker (2004), Bollerslev and Todorov (2011) and Gabaix (2012).”
News Implied Volatility and Disaster Concerns (2017, methodology): “Bollerslev and Todorov (2011) use a model free approach to back out from option prices a measure of the risk-neutral distribution of jump sizes in the S&P 500 index.”
Tail Risk and Asset Prices (2014, positive): “Tail estimation from high-frequency data is exemplified by Bollerslev and Todorov (2011).”