A lattice approach for option pricing under a regime-switching GARCH-jump model

2021 ◽  
pp. 1-33
Author(s):  
Zhiyu Guo ◽  
Yizhou Bai
Keyword(s):  
Option Pricing ◽  
Regime Switching ◽  
Garch Model ◽  
Option Prices ◽  
Good Convergence ◽  
Jump Model ◽  
Black Scholes ◽  
Option Values ◽  
Lattice Algorithm ◽  
Lattice Approach

Abstract In this study, we consider option pricing under a Markov regime-switching GARCH-jump (RS-GARCH-jump) model. More specifically, we derive the risk neutral dynamics and propose a lattice algorithm to price European and American options in this framework. We also provide a method of parameter estimation in our RS-GARCH-jump setting using historical data on the underlying time series. To measure the pricing performance of the proposed algorithm, we investigate the convergence of the tree-based results to the true option values and show that this algorithm exhibits good convergence. By comparing the pricing results of RS-GARCH-jump model with regime-switching GARCH (RS-GARCH) model, GARCH-jump model, GARCH model, Black–Scholes (BS) model, and Regime-Switching (RS) model, we show that accommodating jump effect and regime switching substantially changes the option prices. The empirical results also show that the RS-GARCH-jump model performs well in explaining option prices and confirm the importance of allowing for both jump components and regime switching.

scholarly journals Markov Chain Monte Carlo Method for Estimating Implied Volatility in Option Pricing

2018 ◽  
Vol 10 (6) ◽  
pp. 108
Author(s):  
Yao Elikem Ayekple ◽  
Charles Kofi Tetteh ◽  
Prince Kwaku Fefemwole
Keyword(s):  
Option Pricing ◽  
Implied Volatility ◽  
Call Option ◽  
Option Prices ◽  
Options Market ◽  
European Call Option ◽  
Black Scholes Model ◽  
Black Scholes ◽  
Real Market ◽  
Independent Path

Using market covered European call option prices, the Independence Metropolis-Hastings Sampler algorithm for estimating Implied volatility in option pricing was proposed. This algorithm has an acceptance criteria which facilitate accurate approximation of this volatility from an independent path in the Black Scholes Model, from a set of finite data observation from the stock market. Assuming the underlying asset indeed follow the geometric brownian motion, inverted version of the Black Scholes model was used to approximate this Implied Volatility which was not directly seen in the real market: for which the BS model assumes the volatility to be a constant. Moreover, it is demonstrated that, the Implied Volatility from the options market tends to overstate or understate the actual expectation of the market. In addition, a 3-month market Covered European call option data, from 30 different stock companies was acquired from Optionistic.Com, which was used to estimate the Implied volatility. This accurately approximate the actual expectation of the market with low standard errors ranging between 0.0035 to 0.0275.

scholarly journals Valuing Coca-Cola And PepsiCo Options Using The Black-Scholes Option Pricing Model And Data Downloads From The Internet

2012 ◽  
Vol 8 (6) ◽  
pp. 559-564
Author(s):  
John C. Gardner ◽  
Carl B. McGowan Jr
Keyword(s):  
Option Pricing ◽  
Stock Price ◽  
Option Price ◽  
Option Prices ◽  
Pricing Model ◽  
Black Scholes ◽  
Option Pricing Model ◽  
Risk Free Rate ◽  
Daily Returns ◽  
Free Rate

In this paper, we demonstrate how to collect the data and compute the actual value of Black-Scholes Option Pricing Model call option prices for Coca-Cola and PepsiCo.The data for the current stock price and option price are taken from Yahoo Finance and the daily returns variance is computed from daily prices.The time to maturity is computed as the number of days remaining for the stock option.The risk-free rate is obtained from the U.S. Treasury website.

An Analytical Approximation for Option Price under the Affine GARCH Model - A Comparison with the Closed-Form Solution of Heston-Nandi

GIS Business ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. 32-46
Author(s):  
Noureddine Lahouel ◽  
Slaheddine Hellara
Keyword(s):  
Option Pricing ◽  
Closed Form ◽  
Empirical Work ◽  
Closed Form Solution ◽  
Garch Model ◽  
Analytical Approximation ◽  
Form Solution ◽  
Option Prices ◽  
European Option ◽  
Comparative Performance

In the option pricing theory, two important approaches have been developed to evaluate the prices of a European option. The first approach develops an almost closed-form option pricing formula under a specific GARCH process (Heston & Nandi, 2000). The second approach develops an analytical approximation for computing European option prices with more widespread NGARCH models (Duan, Gauthier & Simonato, 1999). The analytical approximation was also developed under GJR-GARCH and EGARCH models by Duan, Gauthier, Sasseville & Simonato (2006). However, no empirical work was performed to study the comparative performance of these two formulas (closed-form solution and analytical approximation). Also, it is possible to develop an analytical approximation under the specific GARCH model of Heston & Nandi (2000). In this paper, we have filled up those gaps. We started with the development of an analytical approximation, for computing European option prices, under Heston-Nandis GARCH model. In the second step, we carried out a comparative analysis of the three formulas using CAC 40 index returns from 31 December 1987 to 31 December 2013.

Ensuring More Is Better: On the Simultaneous Application of Stock and Options Data to Estimate the GARCH Options Pricing Model

2018 ◽  
Keyword(s):  
Option Pricing ◽  
Stock Prices ◽  
Options Pricing ◽  
Asymptotic Result ◽  
Option Prices ◽  
Options Market ◽  
Pricing Model ◽  
Simultaneous Application ◽  
Option Values ◽  
Option Pricing Model

The most common approach in fitting option pricing models to market data is first to make an assumption about the underlying asset’s returns process and then develop an option pricing model for that process that is tested against market option prices. The returns process is estimated from historical data, option values are computed, and then compared against a cross-section of prices from the options market. Unfortunately, this often does not work well, and plainly it is inefficient in its use of the data. However, efforts to combine returns data from the asset market and prices from the options market into a single estimation have also not had much success. In this article, Chang, Cheng, and Fuh propose a new procedure to combine data from both markets in the estimation, in which options are assumed to be subject to random pricing noise relative to model values. The additional slack gives the estimator better ability to match prices in both markets. The article contrasts the performance of the full model approach with an approach that only uses stock prices or options prices to fit an option pricing model based on an underlying GARCH process. The value of the combined approach is demonstrated both theoretically as an asymptotic result in the model and also in a Monte Carlo simulation.

scholarly journals Correcting the Bias in the Practitioner Black-Scholes Method

2019 ◽  
Vol 12 (4) ◽  
pp. 157
Author(s):  
Yun Yin ◽  
Peter G. Moffatt
Keyword(s):  
Linear Regression ◽  
Implied Volatility ◽  
Option Price ◽  
Option Prices ◽  
Technical Problems ◽  
Black Scholes ◽  
Option Values ◽  
Non Linear ◽  
Alternative Means ◽  
Log Linear

We address a number of technical problems with the popular Practitioner Black-Scholes (PBS) method for valuing options. The method amounts to a two-stage procedure in which fitted values of implied volatilities (IV) from a linear regression are plugged into the Black-Scholes formula to obtain predicted option prices. Firstly we ensure that the prediction from stage one is positive by using log-linear regression. Secondly, we correct the bias that results from the transformation applied to the fitted values (i.e., the Black-Scholes formula) being a highly non-linear function of implied volatility. We apply the smearing technique in order to correct this bias. An alternative means of implementing the PBS approach is to use the market option price as the dependent variable and estimate the parameters of the IV equation by the method of non-linear least squares (NLLS). A problem we identify with this method is one of model incoherency: the IV equation that is estimated does not correspond to the set of option prices used to estimate it. We use the Monte Carlo method to verify that (1) standard PBS gives biased option values, both in-sample and out-of-sample; (2) using standard (log-linear) PBS with smearing almost completely eliminates the bias; (3) NLLS gives biased option values, but the bias is less severe than with standard PBS. We are led to conclude that, of the range of possible approaches to implementing PBS, log-linear PBS with smearing is preferred on the basis that it is the only approach that results in valuations with negligible bias.

scholarly journals Option Pricing by Probability Distortion Operator Based on the Quantile Function

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Luogen Yao ◽  
Gang Yang
Keyword(s):  
Option Pricing ◽  
Simulation Analysis ◽  
Quantile Function ◽  
Martingale Measure ◽  
Option Prices ◽  
New Class ◽  
Pricing Options ◽  
Black Scholes ◽  
Exponential Lévy Models

A new class of distortion operators based on quantile function is proposed for pricing options. It is shown that option prices obtained with our distortion operators are just the prices under mean correcting martingale measure in exponential Lévy models. In particular, Black-Scholes formula can be recuperated by our distortion operator. Simulation analysis shows that our distortion operator is superior to normal distortion operator and NIG distortion operator.

REGIME-SWITCHING RECOMBINING TREE FOR OPTION PRICING

2010 ◽  
Vol 13 (03) ◽  
pp. 479-499 ◽  
Author(s):  
R. H. LIU
Keyword(s):  
Option Pricing ◽  
Regime Switching ◽  
Asset Price ◽  
Stochastic Volatility Model ◽  
Option Prices ◽  
Regime Switching Model ◽  
Switching Model ◽  
Tree Construction ◽  
Volatility Model ◽  
Tree Approach

In this paper we develop an efficient tree approach for option pricing when the underlying asset price follows a regime-switching model. The tree grows only linearly as the number of time steps increases. Thus it enables us to use large number of time steps to compute accurate prices for both European and American options. We present conditions that guarantee the positivity of branch probabilities. We numerically test the sensitivity of option prices to the choice of a key parameter for tree construction. As an interesting application, we develop a regime-switching model to approximate the Heston's stochastic volatility model and then employ the tree approach to approximate the option prices. Numerical results are provided and compared.

scholarly journals The Impact of Liquidity on GARCH Option Pricing Error during Financial Crisis

2017 ◽  
Vol 4 (4) ◽  
pp. 160
Author(s):  
Han Ching Huang ◽  
Yong Chern Su ◽  
Wei-Shen Chen
Keyword(s):  
Financial Crisis ◽  
Option Pricing ◽  
Garch Model ◽  
Black Scholes Model ◽  
Pricing Errors ◽  
Black Scholes ◽  
Out Of Sample ◽  
Pricing Error ◽  
The Impact ◽  
The Relationship

In this paper, we explore the valuation performance of Heston and Nandi GARCH (HN GARCH) model on the pricing of options of financial stocks listed for AMEX during pre and post financial crisis periods. We find that the GARCH pricing model presents better performance than the traditional Black-Scholes model for the out-of-sample option pricing, no matter what the moneyness and the time-to-maturity. Specifically, the models show the effects of liquidity is not significant. Intuitionally, smaller liquidity tends to exhibit more pricing errors, especially for longer mature options. Unfortunately, we cannot get the expected outcomes, which is that the period of post financial crisis tend to have larger pricing errors. In sum, except more computational convenience, the HN GARCH model offers another vision of the relationship between liquidity and its effect on pricing errors.

scholarly journals The Performance of Skewness and Kurtosis Adjusted Option Pricing Model in Emerging Markets

2016 ◽  
Vol 5 (3) ◽  
pp. 70-84
Author(s):  
Özge Sezgin Alp
Keyword(s):  
Emerging Markets ◽  
Option Pricing ◽  
Option Prices ◽  
Pricing Model ◽  
European Options ◽  
Black Scholes Model ◽  
Black Scholes ◽  
Option Pricing Model ◽  
Skewness And Kurtosis ◽  
Pricing Formula

In this study, the option pricing performance of the adjusted Black-Scholes model proposed by Corrado and Su (1996) and corrected by Brown and Robinson (2002), is investigated and compared with original Black Scholes pricing model for the Turkish derivatives market. The data consist of the European options written on BIST 30 index extends from January 02, 2015 to April 24, 2015 for given exercise prices with maturity April 30, 2015. In this period, the strike prices are ranging from 86 to 124. To compare the models, the implied parameters are derived by minimizing the sum of squared deviations between the observed and theoretical option prices. The implied distribution of BIST 30 index does not significantly deviate from normal distribution. In addition, pricing performance of Black Scholes model performs better in most of the time. Black Scholes pricing Formula, Carrado-Su pricing Formula, Implied Parameters

scholarly journals Option Pricing And Monte Carlo Simulations

2011 ◽  
Vol 3 (9) ◽  
Author(s):  
George M. Jabbour ◽  
Yi-Kang Liu
Keyword(s):  
Monte Carlo ◽  
Option Pricing ◽  
Monte Carlo Simulations ◽  
Variance Reduction ◽  
Closed Form Solution ◽  
Form Solution ◽  
Price Convergence ◽  
Option Prices ◽  
Black Scholes ◽  
The Difference

The advantage of Monte Carlo simulations is attributed to the flexibility of their implementation. In spite of their prevalence in finance, we address their efficiency and accuracy in option pricing from the perspective of variance reduction and price convergence. We demonstrate that increasing the number of paths in simulations will increase computational efficiency. Moreover, using a t-test, we examine the significance of price convergence, measured as the difference between sample means of option prices. Overall, our illustrative results show that the Monte Carlo simulation prices are not statistically different from the Black-Scholes type closed-form solution prices.

Sign in / Sign up

Export Citation Format

Share Document