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Model Uncertainty and Pricing Performance in Option Valuation ; ; Lehnert, Thorsten in The Journal of Derivatives (2020), 27(3), 31-49 The objective of this paper is to evaluate option pricing model performance at the cross sectional level. For this purpose, we propose a statistical framework, in which we in particular account for the ... [more ▼] The objective of this paper is to evaluate option pricing model performance at the cross sectional level. For this purpose, we propose a statistical framework, in which we in particular account for the uncertainty associated with the reported pricing performance. Instead of a single figure, we determine an entire probability distribution function for the loss function that is used to measure option pricing model performance. This methodology enables us to visualize the effect of parameter uncertainty on the reported pricing performance. Using a data driven approach, we confirm previous evidence that standard volatility models with clustering and leverage effects are sufficient for the option pricing purpose. In addition, we demonstrate that there is short-term persistence but long-term heterogeneity in cross-sectional option pricing information. This finding has two important implications. First, it justifies the practitioner’s routine to refrain from time series approaches, and instead estimate option pricing models on a cross-section by cross-section basis. Second, the long term heterogeneity in option prices pinpoints the importance of measuring, comparing and testing option pricing model for each cross-section separately. To our knowledge no statistical testing framework has been applied to a single cross-section of option prices before. We propose a methodology that addresses this need. The proposed framework can be applied to a broad set of models and data. In the empirical part of the paper, we show by means of example, an application that uses a discrete time volatility model on S&P 500 index options. [less ▲] Detailed reference viewed: 110 (8 UL)Data Filtering Rules in Option Valuation ; ; Lehnert, Thorsten E-print/Working paper (2018) The choice of data filtering rules are, next to model selection and parameter calibration, an important step in an option pricing exercise. We illustrate the implications of data filtering rules, by ... [more ▼] The choice of data filtering rules are, next to model selection and parameter calibration, an important step in an option pricing exercise. We illustrate the implications of data filtering rules, by investigating three alternative renowned filtering rules in the context of pricing European S&P 500 index options. Different filtering rules result in strongly diverging samples, which carry different information and therefore lead to different parameter estimates. This is illustrated for the Ad Hoc Black-Scholes model. No filtering rule is, in terms of pricing performance, superior on the whole range of options. Instead, each filtering rule is specialized toward better pricing of options types that were included in the calibration sample at the costs of excluded options. Included options are unable to perfectly represent the properties and characteristics of excluded options. In particular, option prices are heterogeneous in the maturity dimension, which is a major driving force underlying the impact of exclusion filters on pricing performance. Additionally, small deviations from the put-call parity strongly affect parameter estimates as well as the accompanying pricing performance. These results emphasize the prominent role of filtering rules as an important implicit choice for an option pricing model calibration. [less ▲] Detailed reference viewed: 41 (0 UL)Model Uncertainty and Pricing Performance in Option Valuation ; ; Lehnert, Thorsten E-print/Working paper (2018) The objective of this paper is to evaluate option pricing model performance at the cross sectional level. For this purpose, we propose a statistical framework, in which we in particular account for the ... [more ▼] The objective of this paper is to evaluate option pricing model performance at the cross sectional level. For this purpose, we propose a statistical framework, in which we in particular account for the uncertainty associated with the reported pricing performance. Instead of a single figure, we determine an entire probability distribution function for the loss function that is used to measure option pricing model performance. This methodology enables us to visualize the effect of parameter uncertainty on the reported pricing performance. Using a data driven approach, we confirm previous evidence that standard volatility models with clustering and leverage effects are sufficient for the option pricing purpose. In addition, we demonstrate that there is short-term persistence but long-term heterogeneity in cross-sectional option pricing information. This finding has two important implications. First, it justifies the practitioner’s routine to refrain from time series approaches, and instead estimate option pricing models on a cross-section by cross-section basis. Second, the long term heterogeneity in option prices pinpoints the importance of measuring, comparing and testing option pricing model for each cross-section separately. To our knowledge no statistical testing framework has been applied to a single cross-section of option prices before. We propose a methodology that addresses this need. The proposed framework can be applied to a broad set of models and data. In the empirical part of the paper, we show by means of example, an application that uses a discrete time volatility model on S&P 500 European options. [less ▲] Detailed reference viewed: 83 (0 UL)The Impact of Uncertainty in the Oil and Gold Market on the Cross-Section Lehnert, Thorsten ; ; et al E-print/Working paper (2015) Detailed reference viewed: 58 (0 UL)Volatility Concepts and Risk Management Tools Lehnert, Thorsten ; ; E-print/Working paper (2015) Detailed reference viewed: 57 (8 UL)Evaluating Option Pricing Model Performance Using Model Uncertainty Lehnert, Thorsten ; ; E-print/Working paper (2014) Detailed reference viewed: 33 (0 UL)Estimation Risk in Option Pricing Lehnert, Thorsten ; ; E-print/Working paper (2013) Detailed reference viewed: 46 (3 UL) |
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