A Bayesian Analysis of Return Dynamics with Lévy Jumps

Li, Haitao; Wells, Martin T.; Yu, Cindy

doi:10.1093/rfs/hhl036

Cited by 137 publications

(130 citation statements)

References 35 publications

Supporting

Mentioning

123

Contrasting

Order By: Relevance

“…Thus, the ob- served y consists of the 5000 data points shown in Figure 2. This is almost the same sample as used in Li et al (2008) and extends the period covered by Griffin and Steel (2006).…”

Section: Application To a Stock Price Indexmentioning

confidence: 99%

Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein–Uhlenbeck processes

Griffin

Steel

2010

Computational Statistics & Data Analysis

View full text Add to dashboard Cite

Section: Application To a Stock Price Indexmentioning

confidence: 99%

Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein–Uhlenbeck processes

Griffin

Steel

2010

Computational Statistics & Data Analysis

View full text Add to dashboard Cite

“…The prior parameters are set in accordance with Eraker et al (2003), Jacquier et al (2004), and Li, Wells, and Yu (2008). In our application, we use a sampler that draws one parameter and latent state at a time.…”

Section: Estimationmentioning

confidence: 99%

Empirical Analysis of Affine vs. Non-Affine Variance Specifications in Jump-Diffusion Models for Equity Indices

2012

View full text Add to dashboard Cite

This paper investigates several crucial issues that arise when modeling equity returns with stochastic variance. (i) Does the model need to include jumps even when using a nonaffine variance specification? We find that jump models clearly outperform pure stochastic volatility models.(ii) How do affine variance specifications perform when compared to nonaffine models in a jump diffusion setup? We find that nonaffine specifications outperform affine models, even after including jumps.Key Words Bayesian inference; Deviance information criteria; Jump diffusion; Markov Chain Monte Carlo; Stochastic volatility. JEL Classifications: C11, G11, G12 Empirical Analysis of Affine vs. Nonaffine Variance Specifications in Jump-Diffusion Models for Equity Indices This version: May 4, 2014Abstract This paper investigates several crucial issues that arise when modeling equity returns with stochastic variance. (i) Does the model need to include jumps even when using a nonaffine variance specification? We find that jump models clearly outperform pure stochastic volatility models.(ii) How do affine variance specifications perform when compared to nonaffine models in a jump diffusion setup? We find that nonaffine specifications outperform affine models, even after including jumps.

show abstract

“…In our analysis, we choose to study the performance of our test on jumps from the finite moment log-stable process of Carr and Wu (2003) and Li, Wells, and Yu (2008). According to both studies, we set β = −1 to achieve negative skewness of empirical densities of equity index returns.…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

Detecting Jumps from Levy Jump Diffusion Processes

Lee

Hannig

2008

SSRN Journal

View full text Add to dashboard Cite

Recent asset-pricing models incorporate jump risk through Lévy processes in addition to diffusive risk. This paper studies how to detect stochastic arrivals of small and big Lévy jumps with new nonparametric tests. The tests allow for robust analysis of their separate characteristics and facilitate better estimation of return dynamics.Empirical evidence of both small and big jumps based on these tests suggests that models for individual equities and overall market indices require incorporating Lévy-type jumps. The evidence of small jumps also helps explain why jumps in the market index are uncorrelated with jumps in its component equities. JEL classification: G12, C12, C14

show abstract

A Bayesian Analysis of Return Dynamics with Lévy Jumps

Cited by 137 publications

References 35 publications

Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein–Uhlenbeck processes

Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein–Uhlenbeck processes

Empirical Analysis of Affine vs. Non-Affine Variance Specifications in Jump-Diffusion Models for Equity Indices

Detecting Jumps from Levy Jump Diffusion Processes

Contact Info

Product

Resources

About