A Simple and Reliable Way to Compute Option-Based Risk-Neutral Distributions

Malz, Allan M.

doi:10.2139/ssrn.2449692

Cited by 41 publications

(43 citation statements)

References 21 publications

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“…Finally, we consider the purely discontinuous variance gamma process (VG) and the Malz (2014) implementation of the Breeden-Litzenberger formula (BL-MALZ). We then evaluate the density obtained with a stochastic volatility model (HESTON) and a jumpdiffusion process (BATES).…”

Section: Resultsmentioning

confidence: 99%

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Financial density forecasts: A comprehensive comparison of risk‐neutral and historical schemes

Crisóstomo

Couso

2018

Journal of Forecasting

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We investigate the forecasting ability of the most commonly used benchmarks in financial economics. We approach the usual caveats of probabilistic forecasts studies-small samples, limited models, and nonholistic validations-by performing a comprehensive comparison of 15 predictive schemes during a time period of over 21 years. All densities are evaluated in terms of their statistical consistency, local accuracy and forecasting errors. Using a new composite indicator, the integrated forecast score, we show that risk-neutral densities outperform historical-based predictions in terms of information content. We find that the variance gamma model generates the highest out-of-sample likelihood of observed prices and the lowest predictive errors, whereas the GARCH-based GJR-FHS delivers the most consistent forecasts across the entire density range. In contrast, lognormal densities, the Heston model, or the nonparametric Breeden-Litzenberger formula yield biased predictions and are rejected in statistical tests.

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Section: Resultsmentioning

confidence: 99%

“…Finally, for the Malz (2014) implementation of the Breeden-Litzenberger formula, we employ a step size Δ = 0.01F t , which avoids negative probabilities in our predictive densities.…”

Section: Risk-neutral Densitiesmentioning

confidence: 99%

Financial density forecasts: A comprehensive comparison of risk‐neutral and historical schemes

Crisóstomo

Couso

2018

Journal of Forecasting

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“…Therefore, in this paper we use a flat extrapolation. 3 The convention of BSIV interpolation for different assets is shown on pp.4 in Malz (2014). 4 Results of in-sample volatility forecasting performance of restricted and unrestricted models are not reported here since it's not the main research question we are exploring.…”

Section: Notesmentioning

confidence: 99%

The Information Content of Corridor Implied Variances and Their Economic Difference in the DJX Options Market

2017

SSRN Journal

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This paper investigates the ability of corridor implied variances (CIV) with different corridors to forecast conditional volatility of DJIA index returns, and compares their performance with a CBOE volatility index, VXD, by employing several GARCH models in a model-based out-of-sample context. Besides, it explores the reasons behind the differences in the forecasting ability of CIVs and VXD through a decomposition of the model-free implied volatility. In addition, it addresses the economic difference among aforementioned implied volatility measures in a simulated options market. We find that narrow-corridor CIVs outperform wide-corridor CIVs and VXD in terms of the forecasting ability, as wide-corridor CIVs and VXD impound information from deep out-of-the-money options whose prices contain large volatility risk premiums and may not reflect a fair market expectation of volatility. In the economic sense, wide-corridor CIVs and VXD outperform narrow-corridor CIVs in turbulent periods, while narrow-corridor CIVs outperform wide-corridor CIVs and VXD in medium-and low-volatility periods. The profitability pattern is consistent both in-sample and out-of-sample, before and after transaction costs are considered, and is also robust to option strategy choices.

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“…Non-structural models can be divided into three categories: parametric, semiparametric and nonparametric models. Through the literature inspection, there are plenty of papers regarding semiparametric and nonparametric approaches for estimating RND (Jackwerth and Rubinstein, 1996;Andersen and Wagner, 2002;Tavin, 2011;Smith, 2012;Breeden and Litzenberger, 2014;Datta et al, 2014;Malz, 2014), some of them comparing parametric and nonparametric approaches Zb. rad.…”

Section: Literature Reviewmentioning

confidence: 99%