Loss Functions in Option Valuation: A Framework for Selection
In this paper, we investigate the importance of different loss functions when estimating and evaluating option pricing models. Our analysis shows that it is important to take into account parameter uncertainty, because this leads to uncertainty in the predicted option price. We illustrate the effect...
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| Veröffentlicht in: | Management science 2009-05, Vol.55 (5), p.853-862 |
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| creator | Bams, Dennis Lehnert, Thorsten Wolff, Christian C. P |
| description | In this paper, we investigate the importance of different loss functions when estimating and evaluating option pricing models. Our analysis shows that it is important to take into account parameter uncertainty, because this leads to uncertainty in the predicted option price. We illustrate the effect on the out-of-sample pricing errors in an application of the ad hoc Black-Scholes model to DAX index options. We confirm the empirical results of Christoffersen and Jacobs (Christoffersen, P., K. Jacobs. 2004. The importance of the loss function in option valuation. J. Financial Econom. 72 291–318) and find strong evidence for their conjecture that the squared pricing error criterion may serve as a general-purpose loss function in option valuation applications. At the same time, we provide a first yardstick to evaluate the adequacy of the loss function. This is accomplished through a data-driven method to deliver not just a point estimate of the root mean squared pricing error, but a distribution . |
| doi_str_mv | 10.1287/mnsc.1080.0976 |
| format | Article |
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P</creatorcontrib><title>Loss Functions in Option Valuation: A Framework for Selection</title><title>Management science</title><description>In this paper, we investigate the importance of different loss functions when estimating and evaluating option pricing models. Our analysis shows that it is important to take into account parameter uncertainty, because this leads to uncertainty in the predicted option price. We illustrate the effect on the out-of-sample pricing errors in an application of the ad hoc Black-Scholes model to DAX index options. We confirm the empirical results of Christoffersen and Jacobs (Christoffersen, P., K. Jacobs. 2004. The importance of the loss function in option valuation. J. Financial Econom. 72 291–318) and find strong evidence for their conjecture that the squared pricing error criterion may serve as a general-purpose loss function in option valuation applications. At the same time, we provide a first yardstick to evaluate the adequacy of the loss function. This is accomplished through a data-driven method to deliver not just a point estimate of the root mean squared pricing error, but a distribution .</description><subject>Applied sciences</subject><subject>Asset pricing</subject><subject>Business losses</subject><subject>Call options</subject><subject>Decision theory. Utility theory</subject><subject>Dividends</subject><subject>Econometrics</subject><subject>Economic models</subject><subject>Estimates</subject><subject>Estimation</subject><subject>estimation risk</subject><subject>Exact sciences and technology</subject><subject>Financial engineering</subject><subject>Financial instruments</subject><subject>GARCH</subject><subject>GARCH models</subject><subject>implied volatility</subject><subject>Influence</subject><subject>Linear inference, regression</subject><subject>loss functions</subject><subject>Management science</subject><subject>Market prices</subject><subject>Mathematical functions</subject><subject>Mathematical independent variables</subject><subject>Mathematics</subject><subject>Measurement</subject><subject>Modeling</subject><subject>Operational research and scientific management</subject><subject>Operational research. 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P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Loss Functions in Option Valuation: A Framework for Selection</atitle><jtitle>Management science</jtitle><date>2009-05-01</date><risdate>2009</risdate><volume>55</volume><issue>5</issue><spage>853</spage><epage>862</epage><pages>853-862</pages><issn>0025-1909</issn><eissn>1526-5501</eissn><coden>MSCIAM</coden><abstract>In this paper, we investigate the importance of different loss functions when estimating and evaluating option pricing models. Our analysis shows that it is important to take into account parameter uncertainty, because this leads to uncertainty in the predicted option price. We illustrate the effect on the out-of-sample pricing errors in an application of the ad hoc Black-Scholes model to DAX index options. We confirm the empirical results of Christoffersen and Jacobs (Christoffersen, P., K. Jacobs. 2004. The importance of the loss function in option valuation. J. Financial Econom. 72 291–318) and find strong evidence for their conjecture that the squared pricing error criterion may serve as a general-purpose loss function in option valuation applications. At the same time, we provide a first yardstick to evaluate the adequacy of the loss function. This is accomplished through a data-driven method to deliver not just a point estimate of the root mean squared pricing error, but a distribution .</abstract><cop>Hanover, MD</cop><pub>INFORMS</pub><doi>10.1287/mnsc.1080.0976</doi><tpages>10</tpages></addata></record> |
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| ispartof | Management science, 2009-05, Vol.55 (5), p.853-862 |
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| subjects | Applied sciences Asset pricing Business losses Call options Decision theory. Utility theory Dividends Econometrics Economic models Estimates Estimation estimation risk Exact sciences and technology Financial engineering Financial instruments GARCH GARCH models implied volatility Influence Linear inference, regression loss functions Management science Market prices Mathematical functions Mathematical independent variables Mathematics Measurement Modeling Operational research and scientific management Operational research. Management science Option pricing Portfolio theory Price volatility Pricing Probability and statistics Sciences and techniques of general use Securities prices Selection Selection methods (Regression analysis) Statistics Stochastic models Studies Valuation Volatility |
| title | Loss Functions in Option Valuation: A Framework for Selection |
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