This paper examines the ability of two recent approaches to estimate implied risk neutral probability density functions (RNDs) - the smoothed implied volatility smile method (SML) and the density functionals based on the confluent hypergeometric functions (DFCH) from the prices of European-style options. A Monte Carlo experiment is conducted to compare the capability of the two techniques to recover simulated distributions based on Heston's (1993) stochastic volatility model. The paper investigates the accuracy and stability of the two methods via two categories of estimated summary statistics. We find that while the SML method outperforms the DFCH method for the summary statistics which are sensitive to the tails of the distribution, the DFCH method dominates the SML method for the summary statistics that are less sensitive to outliers. Due to the lack of observations in the tails when estimating RNDs, we feel that the most appropriate measures for comparing the two methods are the ones less sensitive to extreme values. In this sense, the DFCH method seems to be more appealing. We also apply the two methods via an empirical application.
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Paper provided by University of Liverpool Management School in its series Research Papers with number
200510.
Length: 42 pages Date of creation: 2005 Date of revision: Handle: RePEc:liv:livedp:200510
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Find related papers by JEL classification: C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
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