Dual-Hybrid Modeling for Option Pricing of CSI 300ETF

Abstract

The reasonable pricing of options can effectively help investors avoid risks and obtain benefits, which plays a very important role in the stability of the financial market. The traditional single option pricing model often fails to meet the ideal expectations due to its limited conditions. Combining an economic model with a deep learning model to establish a hybrid model provides a new method to improve the prediction accuracy of the pricing model. This includes the usage of real historical data of about 10,000 sets of CSI 300 ETF options from January to December 2020 for experimental analysis. Aiming at the prediction problem of CSI 300ETF option pricing, based on the importance of random forest features, the Convolutional Neural Network and Long Short-Term Memory model (CNN-LSTM) in deep learning is combined with a typical stochastic volatility Heston model and stochastic interests CIR model in parameter models. The dual hybrid pricing model of the call option and the put option of CSI 300ETF is established. The dual-hybrid model and the reference model are integrated with ridge regression to further improve the forecasting effect. The results show that the dual-hybrid pricing model proposed in this paper has high accuracy, and the prediction accuracy is tens to hundreds of times higher than the reference model; moreover, MSE can be as low as 0.0003. The article provides an alternative method for the pricing of financial derivatives.

Considering momentum spillover effects via graph neural network in option pricing

“…Although the work of Wei et al (2021) improves the RMSE of option pricing, the CNN model cannot capture the impact of dynamic changes in correlated options on option prices, which is the main problem this study attempts to solve. In addition, combining the advantages of CNN and LSTM algorithmic approaches, the tandem CNN–LSTM algorithmic approach is proposed (Zhao et al, 2022). Zhao et al (2022) combined CNN–LSTM with the SV and stochastic equity CIR models in the parametric model, using the CSI 300 ETF option data from January to December 2020 for experimental analysis.…”

“…In addition, combining the advantages of CNN and LSTM algorithmic approaches, the tandem CNN–LSTM algorithmic approach is proposed (Zhao et al, 2022). Zhao et al (2022) combined CNN–LSTM with the SV and stochastic equity CIR models in the parametric model, using the CSI 300 ETF option data from January to December 2020 for experimental analysis. The results show that the prediction accuracy of the proposed hybrid pricing model is tens to hundreds of times higher than that of the reference model.…”

“…The traditional variables include the five factors proposed by Black and Scholes (1973) on option pricing, namely, the strike price ($$K$$), TM ($$\tau $$), the underlying price ($$S$$), volatility ($$\sigma $$), and the risk‐free rate ($$r$$). The trading variables reflect investors' option investment trading behavior (Montesdeoca & Niranjan, 2016; Zhao et al, 2022), including settlement price ($${settle}$$), open price ($${open}$$), close price ($${close}$$), highest price ($${high}$$), lowest price ($${low}$$), volume contract ($${volume}$$), and holding contract ($${hold}$$). Greeks calculated from traditional variables reflect the risk premium of options under the influence of various variables, which are important influences on option prices (Ahn et al, 2012; Zhao et al, 2022), mainly including Delta ($$\Delta $$), Gamma ($$\Gamma $$), Vega ($$v$$), Theta ($$\Theta $$), and Rho (…”

“…The traditional variables include the five factors proposed by Black and Scholes (1973) on option pricing, namely, the strike price (K ), TM (τ), the underlying price (S), volatility (σ), and the risk-free rate (r). The trading variables reflect investors' option investment trading behavior (Montesdeoca & Niranjan, 2016;Zhao et al, 2022), including settlement price (settle), open price (open), close price (close), highest price (high), lowest price (low), volume contract (volume), and holding contract (hold). Greeks calculated from traditional variables reflect the risk premium of options under the influence of various variables, which are important influences on option prices (Ahn et al, 2012;Zhao et al, 2022), mainly including Delta (Δ), Gamma (Γ), Vega (v), Theta (Θ), and Rho (ρ).…”

Enhancing Option Pricing Accuracy in theIndian Market: A CNN-BiLSTM Approach

“…To predict stock prices, Lu et al suggested a CNN-LSTM-based model [35]. To solve the option pricing prediction problem, Zhao et al merged CNN and LSTM model with a standard stochastic volatility Heston model and a stochastic interests CIR model, and the results demonstrated the high accuracy of the dual hybrid model [55]. According to the research studies discussed above, most papers skim the surface of how we can use technical indicators in sequential deep networks.…”