Sequential calibration of options

TLDR: It is found that the filter estimates are the most accurate, followed by the PWLS estimates, and the estimates from all of the advanced methods are significantly closer to the true parameters than the WLS estimates which overfits data.

About: This article is published in Computational Statistics & Data Analysis.The article was published on 2008-02-01 and is currently open access. It has received 47 citations till now. The article focuses on the topics: Overfitting & Valuation of options.

Figures

Table 2 Summary statistics for the simulated Bates model

Fig. 4. NIG-CIR on market data. WLS (circles), PWLS (thin line) and IEKF estimates (thick line).

Table 3 The Heston, Bates and NIG-CIR models have been calibrated to a number of S&P 500 data sets using the different estimation methods

Table 1 Parameters for the Bates model used in the simulation study

Fig. 3. Bates on market data. WLS (circles), PWLS (thin line) and IEKF estimates (thick line).

Fig. 2. Heston on market data. WLS (circles), PWLS (thin line) and IEKF estimates (thick line).

Frequently asked questions

Q1. What have the authors contributed in "Sequential calibration of options" ?

A framework based on the state-space formulation of the option valuation model is introduced. A simulation study, using the Bates model, shows that the adaptive framework is capable of tracking time varying parameters and latent processes such as stochastic volatility processes. It is found that the filter estimates are the most accurate, followed by the PWLS estimates.