Estimating option implied risk‐neutral densities using spline and hypergeometric functions
53 citations recorded by the source corpus.
We examine the ability of two recent methods -- the smoothed implied volatility smile method (SML) and the density functionals based on confluent hypergeometric functions (DFCH) -- for estimating implied risk-neutral densities (RNDs) from European-style options. Two complementary Monte Carlo experiments are conducted and the performance of the two RND estimators is evaluated by the root mean integrated squared error (RMISE) criterion. Results from both experiments show that the DFCH method outperforms the SML method for the overall quality of the estimated RNDs concerning both accuracy and stability. An application of the two methods to the OTC currency options market is also presented. Copyright Royal Economic Society 2007
Subfields: Demography, Finance
View Paper: https://www.semanticscholar.org/paper/25e0c46f7c184d9bd11f1a3bb9698d3149eea488
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Citations in the corpus
- Forecasting with Option-Implied Information (2012, unknown): “See Söderlind and Svensson (1997), Galati (1999), Jackwerth (1999), Jondeau and Rockinger (2000), Bliss and Panigirtzoglou (2002), Rebonato (2004), Taylor (2005), Bu and Hadri (2007), Jondeau, Poon, and Rockinger (2007), Figlewski (2010), Fusai and Roncoroni (2008), and Markose and Alentorn (2011).”
- A Simple and Reliable Way to Compute Option-Based Risk-Neutral Distributions (2014, background): “Bu and Hadri (2007) discuss the phenomenon, which intuitively seems likely to be due to no-arbitrage violations in the data.”