Stochastic Volatility With Reset At Jumps
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Author | : Jun Pan |
Publisher | : |
Total Pages | : 26 |
Release | : 2009 |
Genre | : |
ISBN | : |
This paper presents a model for asset returns incorporating both stochastic volatility and jump effects. The return process is driven by two types of randomness: small random shocks and large jumps. The stochastic volatility process is affected by both types of randomness in returns. Specifically, in the absence of large jumps, volatility is driven by the small random shocks in returns through a GARCH(1,1) model, while the occurrence of a jump event breaks the persistence in the volatility process, and resets it to an unknown deterministic level. Model estimation is performed on daily returns of Samp;P~500 index using the maximum-likelihood method. The empirical results are discussed.
Author | : Adjoa K. Numatsi |
Publisher | : |
Total Pages | : 258 |
Release | : 2010 |
Genre | : Stochastic analysis |
ISBN | : |
Author | : CIRANO. |
Publisher | : Montréal : CIRANO |
Total Pages | : 35 |
Release | : 1999 |
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Author | : Qi Li |
Publisher | : Emerald Group Publishing |
Total Pages | : 570 |
Release | : 2009-12-04 |
Genre | : Business & Economics |
ISBN | : 1849506248 |
Contains a selection of papers presented initially at the 7th Annual Advances in Econometrics Conference held on the LSU campus in Baton Rouge, Louisiana during November 14-16, 2008. This work is suitable for those who wish to familiarize themselves with nonparametric methodology.
Author | : Jian Chen |
Publisher | : |
Total Pages | : 0 |
Release | : 2022 |
Genre | : |
ISBN | : |
Author | : Ke Chen (Economist) |
Publisher | : |
Total Pages | : |
Release | : 2013 |
Genre | : |
ISBN | : |
This thesis studies a few different finance topics on the application and modelling of jump and stochastic volatility process. First, the thesis proposed a non-parametric method to estimate the impact of jump dependence, which is important for portfolio selection problem. Comparing with existing literature, the new approach requires much less restricted assumption on the jump process, and estimation results suggest that the economical significance of jumps is largely mis-estimated in portfolio optimization problem. Second, this thesis investigates the time varying variance risk premium, in a framework of stochastic volatility with stochastic jump intensity. The proposed model considers jump intensity as an extra factor which is driven by realized jumps, in addition to a stochastic volatility model. The results provide strong evidence of multiple factors in the market and show how they drive the variance risk premium. Thirdly, the thesis uses the proposed models to price options on equity and VIX consistently. Based on calibrated model parameters, the thesis shows how to calculate the unconditional correlation of VIX future between different maturities.
Author | : Alexey Medvedev |
Publisher | : |
Total Pages | : 40 |
Release | : 2003 |
Genre | : |
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Author | : Kyriakos M. Chourdakis |
Publisher | : |
Total Pages | : 45 |
Release | : 2000 |
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Author | : Mthuli Ncube |
Publisher | : |
Total Pages | : 124 |
Release | : 2012 |
Genre | : |
ISBN | : 9783659241192 |
Author | : Katja Ignatieva |
Publisher | : |
Total Pages | : 42 |
Release | : 2009 |
Genre | : |
ISBN | : |
This paper analyzes exponentially affine and non-affine stochastic volatility models with jumps in returns and volatility. Markov Chain Monte Carlo (MCMC) technique is applied within a Bayesian inference to estimate model parameters and latent variables using daily returns from the Samp;P 500 stock index. There are two approaches to overcome the problem of misspecification of the square root stochastic volatility model. The first approach proposed by Christo ersen, Jacobs and Mimouni (2008) suggests to investigate some non-affine alternatives of the volatility process. The second approach consists in examining more heavily parametrized models by adding jumps to the return and possibly to the volatility process. The aim of this paper is to combine both model frameworks and to test whether the class of affine models is outperformed by the class of non-affine models if we include jumps into the stochastic processes. We conclude that the non-affine model structure have promising statistical properties and are worth further investigations. Further, we find affine models with jump components that perform similar to the non affine models without jump components. Since non affine models yield economically unrealistic parameter estimates, and research is rather developed for the affine model structures we have a tendency to prefer the affine jump diffusion models.