Dual-Hybrid Modeling for Option Pricing of CSI 300ETF

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.

PENGARUH PEMBAGIAN DIVIDEN MELALUI MODEL BLACK-SCHOLES

Stock trading has a risk that can be said to be quite large due to fluctuations in stock prices. In stock trading, one alternative to reduce the amount of risk is options. The focus of this research is on European options which are financial contracts by giving the holder the right, not the obligation, to sell or buy the principal asset from the writer when it expires at a predetermined price. The Black-Scholes model is an option pricing model commonly used in the financial sector. This study aims to determine the effect of dividend distribution through the Black-Scholes model on stock prices. The effect of dividend distribution through the Black-Scholes model on stock prices results in the stock price immediately after the dividend distribution being lower than the stock price shortly before the dividend distribution

Empirical study based on the model of rough fractional stochastic volatility (RFSV)

To estimate the parameters of the model of option pricing based on the model of rough fractional stochastic volatility (RFSV), we have carried out the empirical analysis during our study on the pricing of SSE 50ETF options in China. First, we have estimated the parameters of option pricing model by adopting the Monte Carlo simulation. Subsequently, we have empirically examined the pricing performance of the RFSV model by adopting the SSE 50ETF option price from January 2019 to December 2020. Our research findings indicate that by leveraging the RFSV model, we are able to attain a more accurate and stable level of option pricing than the conventional Black–Scholes (B-S) model on constant volatility. The errors of option pricing incurred by the B-S model proved to be larger and exhibited higher volatility, revealing the significant impact imposed by stochastic volatility on option pricing.

Option Valuation in the Presence of Market Sentiment: Application to Listed Companies in the CAC40 Index

Asset pricing theory based on rationality was widely criticized in literature. Indeed, the non-inclusion of investor behavior and assuming market efficiency led to the weaknesses of option valuation through the traditional Black and Scholes model (1973). In this paper we examine the effect of the inclusion of investor behavior in the option pricing model. We test whether the Black and Scholes model in presence of sentiment behavior can lead to an improvement of the calculation of call price. Using daily data of 30 listed companies of France in the CAC40 index for the period June 18, 2009 to May 09, 2018, results showed that the introduction of sentiment effect in the Black and Scholes model provides better estimates of the call price than that obtained by the standard Black-Scholes model. In fact, we obtain an average gain of about 44% in terms of relative change in mean square error between both methods.

The Sunk Cost and the Real Option Pricing Model

Although the academic literature on real options has grown enormously over the past three decades, hitherto an accurate real option pricing model has not been developed for investment decision analyses. In this paper, we propose a real option pricing model based on sunk cost characteristics, which can estimate the value of real options more accurately. First, we explore the distinctive features that distinguish real options from financial options. The study shows that the distinguishing feature of the real options is the sunk cost, which does not exist in the financial options. Based on the sunk cost characteristic of real options, we find that the exercise conditions of real and financial options are different. Second, we introduce the sunk cost into the intrinsic value function of real options and establish a new real option pricing model. Finally, this paper also discusses the properties of the intrinsic value function and pricing model of real options. We find that the application of the Black–Scholes option pricing model will overestimate the value of real options.