engleski

Predictive accuracy of option pricing models considering high-frequency data

Recently, a great attention has been given to forecasting, not only future expectations and the variance of financial time-series, but also the entire probability density function of the underlying asset. For that purpose, call and put European option prices are employed in this research which includes two steps. In the first step, several probability density functions are estimated using different option pricing models, considering data of major market indices with different maturities. These implied probability density functions are risk neutral at expiration date. In the second step, implied pdf’s are compared against “true” density obtained from the high-frequency data to examine which one gives the best fit out-of- sample, i.e. which implied probability density function fits the “true” density most accurately ate expiration date. The “true” density function is unknown, but it can be estimated using high-frequency data adjusted for risk preferences. Therefore, the main objective of this research is to find a data driven benchmark of the “true” density function for major market indices in consideration. This research contributes to the existing literature in two ways: i) finding the benchmark of the “true” density function using high-frequency data within Kernel estimator and ii) determining the predictive accuracy of the option pricing models, which is the purpose of this research. The comparison of benchmark density function against estimated risk neutral probability functions produces applicative results for market participants and public authorities, respectively. Moreover, research cognitions are offer better insights into high- frequency data issues.

option pricing models ; high-frequency data ; kernel estimation ; benchmark density function ; predictive accuracy

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