A study towards a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation

By Mikhail Chernov, Eric Ghysels. 2000

579 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0b8bc86b23ea32c2d8004b86fd42efce73c4c2f9

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Citations in the corpus

A Nonlinear Factor Analysis of S&P 500 Index Option Returns (2006, methodology): “To approximate this function I use a second-order Legendre series in the variables in (14), appropriately rescaled.”
Ex Ante Skewness and Expected Stock Returns (2013, unknown): “15 Once physical and risk-neutral distributions are estimated using the NIG method, the τ -period SDF, Mt(τ) is computed as in equation (19) by taking the risk-free discounted ratio of the risk-neutral to physical distribution.”
The Cross-Section of Volatility and Expected Returns (2006, methodology): “To proxy innovations in aggregate volatility,(vt+1 − γv,t), we use changes in theV IX index from the Chicago Board Options Exchange (CBOE).5 Second, we reduce the number of factors in equation (1) to just the market factor and the proxy for aggregate volatility risk.”
Empirical reverse engineering of the pricing kernel (2003, background): “This is impossible in the constant drift speci+cation in Heston (1993), since there are arbitrage opportunities in this case (see Chernov and Ghysels, 2000 for further details).”
The Impact of Jumps in Volatility and Returns (2003, unknown): “A number of recent papers examine equity price models with jumps in returns and stochastic volatility (see Bakshi, Cao, and Chen (1997), Bates (2000), Andersen, Benzoni, and Lund (2002), Chernov et al. (2002), and Pan (2002)).While it is clear that both stochastic volatility and jumps in returns are”
Estimating the Parameters of Stochastic Volatility Models Using Option Price Data (2015, methodology): “Estimation based on variants of the method of moments includes studies by Chernov and Ghysels (2000) and Pan (2002).”
The jump-risk premia implicit in options: evidence from an integrated time-series study $ (2002, methodology): “[1996] propose an MLE-based two-step iterative procedure; Pastrorello, Renault, and Touzi [1996] apply simulated method of moments (SMM) to time series of spot and option prices separately; Chernov and Ghysels [2000] adopt the SNP/EMM empirical strategy developed by Gallant and Tauchen [1998] and ”
Approximation and Calibration of Short-Term Implied Volatilities under Jump-Diffusion Stochastic Volatility (2007, unknown): “I(θ, τ ;σ) = σ + I1(θ;σ) √ τ + I2(θ;σ)τ +O(τ √ τ), (15)”
Model Specification and Risk Premia: Evidence from Futures Options (2007, background): “These variances differ from Black–Scholes implied variance, as the model-based variance takes into account, for example, jumps or mean reversion in volatility.”
Volatility Components, Affine Restrictions and Non-Normal Innovations (2008, unknown): “…be seen if the differences in performance between models are confirmed when using model parameters estimated from option prices, or when using an integrated analysis that uses option prices as well as underlying returns (see Bates (2000), Chernov and Ghysels (2000), Eraker (2004) and Pan (2002)).”
VOLATILITY AND CORRELATION FORECASTING (2006, background): “In addition to some of the key theoretical contributions mentioned above, noteworthy empirical studies based on continuous-time methods include Bakshi, Cao and Chen (1997), Bates (1996), Chernov and Ghysels (2000), Eraker (2004), Melino and Turnbull (1990), and Pan (2002).”
Simulation-Based Method of Moments and Efficiency (2002, methodology): “EMM has been applied to the estimation of stochastic volatility (SV) models by Andersen, Chung, and Sorensen (1999), Chernov and Ghysels (2000), Andersen and Lund (1997), Gallant, Hsieh, and Tauchen (1997), and Solibakke (2001); to SV models with jumps by Andersen, Benzoni and Lund (2001); to a…”
Crashes, Volatility, and the Equity Premium: Lessons from S&P 500 Options (2010, result): “Overall, our results are also consistent with the recent literature on multifactor variance models (Alizadeh, Brandt, & Diebold, 2002; Chacko & Viceira, 2003; Chernov et al., 2002; Engle & Lee, 1999; Ghysels, Santa-Clara, & Valkanov, 2005), which finds reliable support for the existence of two facto”