Good Jumps, Bad Jumps, and Conditional Equity Premium

Guo, Hui; Wang, Kent; Zhou, Hao

doi:10.2139/ssrn.2515212

Cited by 13 publications

(2 citation statements)

References 69 publications

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“…Drawing on similar "infill asymptotics" arguments, Barndorff-Nielsen, Kinnebrock, and Shephard (2010) show, in a model-free way, how to dissect the realized variance into upside and downside semivariances obtained by summing high-frequency positive and negative squared returns, respectively. This decomposition has been used to improve realized variance forecasts (Patton and Sheppard (2015)), predict the equity risk premium given the standard risk-return trade-off (Guo, Wang, and Zhou (2015)), or explain the cross-section of stock returns (Bollerslev, Li, and Zhao (2017)). We extend these studies by helping gauge the economic value added of disentangling the upside (semi-)variance motion from its downside counterpart in our option pricing framework.…”

Section: Introductionmentioning

confidence: 99%

Good Volatility, Bad Volatility, and Option Pricing

Feunou¹,

Okou²

2018

J. Financ. Quant. Anal.

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Advances in variance analysis permit the splitting of the total quadratic variation of a jump-diffusion process into upside and downside components. Recent studies establish that this decomposition enhances volatility predictions and highlight the upside/downside variance spread as a driver of the asymmetry in stock price distributions. To appraise the economic gain of this decomposition, we design a new and flexible option pricing model in which the underlying asset price exhibits distinct upside and downside semivariance dynamics driven by the model-free proxies of the variances. The new model outperforms common benchmarks, especially the alternative that splits the quadratic variation into diffusive and jump components.

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Section: Introductionmentioning

confidence: 99%

Good Volatility, Bad Volatility, and Option Pricing

Feunou¹,

Okou²

2018

J. Financ. Quant. Anal.

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“…A second channel through which disaster risks can affect bond return dynamics is via its contribution to jump risk in bond prices. The presence of jump risk driving stock and bond returns is well documented in the literature (Caporin, Rossi, & Santucci de Magistris, 2016;Dunham & Friesen, 2007;Gkillas, Gupta, & Wohar, 2018;Guo, Wang, & Zhou, 2016;Huang & Tauchen, 2005;Maheu & McCurdy, 2004;Maheu, McCurdy, & Zhao, 2013). In the context of asset returns, Wachter (2013) relates time-varying disaster probabilities to large instantaneous changes, that is, jumps in aggregate consumption.…”

Section: Risk S With the Bond Marketmentioning

confidence: 99%

The role of time‐varying rare disaster risks in predicting bond returns and volatility

2018

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This paper aims to provide empirical evidence to the theoretical claim that rare disaster risks affect government bond market movements. Using a nonparametric quantiles‐based methodology, we show that rare disaster‐risks affect only volatility, but not returns, of 10‐year government bond of the United States over the monthly period of 1918:01 to 2013:12. In addition, the predictability of volatility holds for the majority of the conditional distribution of the volatility, with the exception of the extreme ends. Moreover, in general, similar results are also obtained for long‐term government bonds of an alternative developed country (UK) and an emerging market (South Africa).

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