scholarly journals Estimation for a Second-Order Jump Diffusion Model from Discrete Observations: Application to Stock Market Returns

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Tianshun Yan ◽  
Yanyong Zhao ◽  
Shuanghua Luo
Keyword(s):  
Stock Market ◽  
Diffusion Model ◽  
Jump Diffusion ◽  
Second Order ◽  
Model Parameters ◽  
Ratio Test ◽  
Market Returns ◽  
Discrete Observations ◽  
Stock Market Returns ◽  
Jump Diffusion Model

This paper proposes a second-order jump diffusion model to study the jump dynamics of stock market returns via adding a jump term to traditional diffusion model. We develop an appropriate maximum likelihood approach to estimate model parameters. A simulation study is conducted to evaluate the performance of the estimation method in finite samples. Furthermore, we consider a likelihood ratio test to identify the statistically significant presence of jump factor. The empirical analysis of stock market data from North America, Asia, and Europe is provided for illustration.

Jump diffusion model with application to the Japanese stock market

2008 ◽  
Vol 78 (2-3) ◽  
pp. 223-236 ◽  
Author(s):  
Koichi Maekawa ◽  
Sangyeol Lee ◽  
Takayuki Morimoto ◽  
Ken-ichi Kawai
Keyword(s):  
Stock Market ◽  
Diffusion Model ◽  
Jump Diffusion ◽  
Jump Diffusion Model ◽  
Japanese Stock Market

CDS Premium and Jump Risk in Stock Market

2012 ◽  
Vol 20 (3) ◽  
pp. 347-364
Author(s):  
Kook-Hyun Chang ◽  
Byung-Jo Yoon
Keyword(s):  
Stock Market ◽  
Diffusion Model ◽  
Jump Diffusion ◽  
Conditional Volatility ◽  
Jump Risk ◽  
Premium Rate ◽  
Korean Stock Market ◽  
Jump Diffusion Model

This paper tries to empirically investigate whether the jump risk of Korean stock market may be statistically useful in explaining the Korean CDS (5Y) premium rate. This paper uses the jump-diffusion model with heteroscedasticity to estimate the conditional volatility of KOSPI from 7/2/2007 to 7/30/2010. The total volatility of Korean stock market is decomposed into a heteroscedasticity and a jump risk by using the jump-diffusion model. The finding is that the jump risk in stead of heteroscedasticity in Korean stock market can explain the Korean CDS premium rate.

scholarly journals Analysing the Nepali Stock Market with Stochastic Models

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Karan Singh Thagunna ◽  
Radal M Lochowski
Keyword(s):  
Brownian Motion ◽  
Stock Market ◽  
Diffusion Model ◽  
Stock Prices ◽  
Jump Diffusion ◽  
Efficient Market Hypothesis ◽  
Geometric Brownian Motion ◽  
Brownian Motion Model ◽  
Jump Diffusion Model ◽  
Daily Returns

In this article we analyse the behaviour of the Nepali stock market and movements of stock prices of selected companies using (i) Efficient Market Hypothesis (EMH) (ii) geometric Brownian motion model (gBm) and (iii) Merton’s jump-diffusion model. Using the daily returns of the NEPSE index and the daily returns of stock prices of selected companies we estimate the geometric Brownian motion model and Merton’s jump-diffusion model. Further, we compare both models to identify the best fit for the Nepali stock market data. Keywords: Black-Scholes model, Efficient Market Hypothesis, geometric Brownian motion, Merton’s jump-diffusion Model, Variance Ratio Test

scholarly journals Option Pricing under Two-Factor Stochastic Volatility Jump-Diffusion Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Guohe Deng
Keyword(s):  
Stochastic Volatility ◽  
Diffusion Model ◽  
Implied Volatility ◽  
Empirical Work ◽  
Jump Diffusion ◽  
Model Parameters ◽  
Single Factor ◽  
Proposed Model ◽  
Volatility Model ◽  
Jump Diffusion Model

Empirical evidence shows that single-factor stochastic volatility models are not flexible enough to account for the stochastic behavior of the skew, and certain financial assets may exhibit jumps in returns and volatility. This paper introduces a two-factor stochastic volatility jump-diffusion model in which two variance processes with jumps drive the underlying stock price and then considers the valuation on European style option. We derive a semianalytical formula for European vanilla option and develop a fast and accurate numerical algorithm for the computation of the option prices using the fast Fourier transform (FFT) technique. We compare the volatility smile and probability density of the proposed model with those of alternative models, including the normal jump diffusion model and single-factor stochastic volatility model with jumps, respectively. Finally, we provide some sensitivity analysis of the model parameters to the options and several calibration tests using option market data. Numerical examples show that the proposed model has more flexibility to capture the implied volatility term structure and is suitable for empirical work in practice.

Sign in / Sign up

Export Citation Format

Share Document