The Experts below are selected from a list of 54564 Experts worldwide ranked by ideXlab platform

A Mcclelland - One of the best experts on this subject based on the ideXlab platform.

  • estimating the parameters of stochastic volatility models using Option Price data
    Journal of Business & Economic Statistics, 2015
    Co-Authors: Aubrey Hurn, Kenneth Lindsay, A Mcclelland
    Abstract:

    This article describes a maximum likelihood method for estimating the parameters of the standard square-root stochastic volatility model and a variant of the model that includes jumps in equity Prices. The model is fitted to data on the S&P 500 Index and the Prices of vanilla Options written on the index, for the period 1990 to 2011. The method is able to estimate both the parameters of the physical measure (associated with the index) and the parameters of the risk-neutral measure (associated with the Options), including the volatility and jump risk premia. The estimation is implemented using a particle filter whose efficacy is demonstrated under simulation. The computational load of this estimation method, which previously has been prohibitive, is managed by the effective use of parallel computing using graphics processing units (GPUs). The empirical results indicate that the parameters of the models are reliably estimated and consistent with values reported in previous work. In particular, both the volatility risk premium and the jump risk premium are found to be significant.

Shianchang Huang - One of the best experts on this subject based on the ideXlab platform.

  • online Option Price forecasting by using unscented kalman filters and support vector machines
    Expert Systems With Applications, 2008
    Co-Authors: Shianchang Huang
    Abstract:

    This study develops a hybrid model that combines unscented Kalman filters (UKFs) and support vector machines (SVMs) to implement an online Option Price predictor. In the hybrid model, the UKF is used to infer latent variables and make a prediction based on the Black-Scholes formula, while the SVM is employed to model the nonlinear residuals between the actual Option Prices and the UKF predictions. Taking Option data traded in Taiwan Futures Exchange, this study examined the forecasting accuracy of the proposed model, and found that the new hybrid model is superior to pure SVM models or hybrid neural network models in terms of three types of Options. This model can help investors for reducing their risk in online trading.

Sanket Nandan - One of the best experts on this subject based on the ideXlab platform.

  • convergence of estimated Option Price in a regime switching market
    Indian Journal of Pure & Applied Mathematics, 2016
    Co-Authors: Anindya Goswami, Sanket Nandan
    Abstract:

    In an observed generalized semi-Markov regime, estimation of transition rate of regime switching leads towards calculation of locally risk minimizing Option Price. Despite the uniform convergence of estimated step function of transition rate, to meet the existence of classical solution of the modified Price equation, the estimator is approximated in the class of smooth functions and furthermore, the convergence is established. Later, the existence of the solution of the modified Price equation is verified and the point-wise convergence of such approximation of Option Price is proved to answer the tractability of its application in Finance. To demonstrate the consistency in result a numerical experiment has been reported.

  • Convergence of estimated Option Price in a regime switching market
    Indian Journal of Pure and Applied Mathematics, 2016
    Co-Authors: Anindya Goswami, Sanket Nandan
    Abstract:

    In an observed semi-Markov regime, estimation of transition rate of regime switching leads towards calculation of locally risk minimizing Option Price. Despite the uniform convergence of estimated step function of transition rate, to meet the existence of classical solution of the modified Price equation, the estimator is approximated in the class of smooth functions and furthermore, the convergence is established. Later, the existence of the solution of the modified Price equation is verified and the point-wise convergence of such approximation of Option Price is proved to answer the tractability of its application in Finance. To demonstrate the consistency in result a numerical experiment has been reported.

Ying Chen - One of the best experts on this subject based on the ideXlab platform.

  • improving Option Price forecasts with neural networks and support vector regressions
    Neurocomputing, 2009
    Co-Authors: Xun Liang, Haisheng Zhang, Jianguo Xiao, Ying Chen
    Abstract:

    Options are important financial derivatives that allow investors to control their investment risks in the securities market. Determining the theoretical Price for an Option, or Option pricing, is regarded as one of the most important issues in financial research; a number of parametric and nonparametric Option pricing approaches have been presented. While the objective of Option pricing is to find the current fair Price, for decision making, in contrast, the forecasting activity has to accurately predict the future Option Price without advance knowledge of the underlying asset Price. In this paper, a simple and effective nonparametric method of forecasting Option Prices based on neural networks (NNs) and support vector regressions (SVRs) is presented. We first modified the improved conventional Option pricing methods, allowing them to forecast the Option Prices. Second, we employed the NNs and SVRs to further decrease the forecasting errors of the parametric methods. Since the conventional methods mimic the trends of movement of the real Option Prices, using these methods in a first stage allows the NNs and SVRs to concentrate their power in nonlinear curve approximation to further reduce the forecasting errors in a second stage. Finally, extensive experimental studies with data from the Hong Kong Option market demonstrated the ability of NNs and SVRs to improve forecast accuracy.

Aubrey Hurn - One of the best experts on this subject based on the ideXlab platform.

  • estimating the parameters of stochastic volatility models using Option Price data
    Journal of Business & Economic Statistics, 2015
    Co-Authors: Aubrey Hurn, Kenneth Lindsay, A Mcclelland
    Abstract:

    This article describes a maximum likelihood method for estimating the parameters of the standard square-root stochastic volatility model and a variant of the model that includes jumps in equity Prices. The model is fitted to data on the S&P 500 Index and the Prices of vanilla Options written on the index, for the period 1990 to 2011. The method is able to estimate both the parameters of the physical measure (associated with the index) and the parameters of the risk-neutral measure (associated with the Options), including the volatility and jump risk premia. The estimation is implemented using a particle filter whose efficacy is demonstrated under simulation. The computational load of this estimation method, which previously has been prohibitive, is managed by the effective use of parallel computing using graphics processing units (GPUs). The empirical results indicate that the parameters of the models are reliably estimated and consistent with values reported in previous work. In particular, both the volatility risk premium and the jump risk premium are found to be significant.