On Black–Scholes option pricing model with stochastic volatility: an information theoretic approach

2020 ◽  
pp. 1-12
Author(s):  
Luckshay Batra ◽  
H. C. Taneja
Keyword(s):  
Option Pricing ◽  
Stochastic Volatility ◽  
Theoretic Approach ◽  
Pricing Model ◽  
Information Theoretic ◽  
Black Scholes ◽  
Option Pricing Model ◽  
Information Theoretic Approach

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

2021 ◽  
Author(s):  
Songyan Zhang ◽  
Chaoyong Hu
Keyword(s):  
Monte Carlo Simulation ◽  
Option Pricing ◽  
Stochastic Volatility ◽  
Option Price ◽  
Pricing Model ◽  
Black Scholes ◽  
Option Pricing Model ◽  
Research Findings ◽  
S Model ◽  
The Empirical Analysis

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.

scholarly journals Empirical Performance of Black-Scholes and GARCH Option Pricing Models during Turbulent Times: The Indian Evidence

2016 ◽  
Vol 8 (3) ◽  
pp. 123
Author(s):  
Aparna Bhat ◽  
Kirti Arekar
Keyword(s):  
Option Pricing ◽  
Closed Form Solution ◽  
Time Varying ◽  
Pricing Model ◽  
Currency Options ◽  
Pricing Models ◽  
Pricing Options ◽  
Volatility Model ◽  
Black Scholes ◽  
Option Pricing Model

Exchange-traded currency options are a recent innovation in the Indian financial market and their pricing is as yet unexplored. The objective of this research paper is to empirically compare the pricing performance of two well-known option pricing models – the Black-Scholes-Merton Option Pricing Model (BSM) and Duan’s NGARCH option pricing model – for pricing exchange-traded currency options on the US dollar-Indian rupee during a recent turbulent period. The BSM is known to systematically misprice options on the same underlying asset but with different strike prices and maturities resulting in the phenomenon of the ‘volatility smile’. This bias of the BSM results from its assumption of a constant volatility over the option’s life. The NGARCH option pricing model developed by Duan is an attempt to incorporate time-varying volatility in pricing options. It is a deterministic volatility model which has no closed-form solution and therefore requires numerical techniques for evaluation. In this paper we have compared the pricing performance and examined the pricing bias of both models during a recent period of volatility in the Indian foreign exchange market. Contrary to our expectations the pricing performance of the more sophisticated NGARCH pricing model is inferior to that of the relatively simple BSM model. However orthogonality tests demonstrate that the NGARCH model is free of the strike price and maturity biases associated with the BSM. We conclude that the deterministic BSM does a better job of pricing options than the more advanced time-varying volatility model based on GARCH.

scholarly journals Nonparametric Estimation of Fractional Option Pricing Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Qing Li ◽  
Songlin Liu ◽  
Misi Zhou
Keyword(s):  
Option Pricing ◽  
Stock Exchange ◽  
Option Price ◽  
Integral Function ◽  
The State ◽  
Pricing Model ◽  
State Price ◽  
Black Scholes ◽  
Option Pricing Model ◽  
State Price Density

The establishment of the fractional Black–Scholes option pricing model is under a major condition with the normal distribution for the state price density (SPD) function. However, the fractional Brownian motion is deemed to not be martingale with a long memory effect of the underlying asset, so that the estimation of the state price density (SPD) function is far from simple. This paper proposes a convenient approach to get the fractional option pricing model by changing variables. Further, the option price is transformed as the integral function of the cumulative density function (CDF), so it is not necessary to estimate the distribution function individually by complex approaches. Finally, it encourages to estimate the fractional option pricing model by the way of nonparametric regression and makes empirical analysis with the traded 50 ETF option data in Shanghai Stock Exchange (SSE).

Predicting the stock price of frontier markets using machine learning and modified Black–Scholes Option pricing model

2020 ◽  
Vol 555 ◽  
pp. 124444 ◽  
Author(s):  
Reaz Chowdhury ◽  
M.R.C. Mahdy ◽  
Tanisha Nourin Alam ◽  
Golam Dastegir Al Quaderi ◽  
M. Arifur Rahman
Keyword(s):  
Machine Learning ◽  
Option Pricing ◽  
Stock Price ◽  
Pricing Model ◽  
Black Scholes ◽  
Option Pricing Model

Black-Scholes Option Pricing Model

2012 ◽  
Author(s):  
Svetlozar T. Rachev ◽  
Christian Menn ◽  
Frank J. Fabozzi
Keyword(s):  
Option Pricing ◽  
Pricing Model ◽  
Black Scholes ◽  
Option Pricing Model

scholarly journals Efficient Markets and Contingent Claims Valuation: An Information Theoretic Approach

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1283
Author(s):  
Jussi Lindgren
Keyword(s):  
Financial Market ◽  
Optimal Transport ◽  
Processing System ◽  
Theoretic Approach ◽  
Derivative Securities ◽  
Thermodynamical Equilibrium ◽  
Information Theoretic ◽  
Black Scholes ◽  
Hamilton Jacobi Bellman ◽  
Information Theoretic Approach

This research article shows how the pricing of derivative securities can be seen from the context of stochastic optimal control theory and information theory. The financial market is seen as an information processing system, which optimizes an information functional. An optimization problem is constructed, for which the linearized Hamilton–Jacobi–Bellman equation is the Black–Scholes pricing equation for financial derivatives. The model suggests that one can define a reasonable Hamiltonian for the financial market, which results in an optimal transport equation for the market drift. It is shown that in such a framework, which supports Black–Scholes pricing, the market drift obeys a backwards Burgers equation and that the market reaches a thermodynamical equilibrium, which minimizes the free energy and maximizes entropy.

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