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Author: Jun Pan Publisher: ISBN: Category : Languages : en Pages : 26
Book Description
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: Jun Pan Publisher: ISBN: Category : Languages : en Pages : 26
Book Description
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: Mikhail Chernov Publisher: ISBN: Category : Languages : en Pages : 37
Book Description
The purpose of this paper is to propose a new class of jump diffusions which feature both stochastic volatility and random intensity jumps. Previous studies have focused primarily on pure jump processes with constant intensity and log-normal jumps or constant jump intensity combined with a one factor stochastic volatility model. We introduce several generalizations which can better accommodate several empirical features of returns data. In their most general form we introduce a class of processes which nests jump-diffusions previously considered in empirical work and includes the affine class of random intensity models studied by Bates (1998) and Duffie, Pan and Singleton (1998) but also allows for non-affine random intensity jump components. We attain the generality of our specification through a generic Levy process characterization of the jump component. The processes we introduce share the desirable feature with the affine class that they yield analytically tractable and explicit option pricing formula. The non-affine class of processes we study include specifications where the random intensity jump component depends on the size of the previous jump which represent an alternative to affine random intensity jump processes which feature correlation between the stochastic volatility and jump component. We also allow for and experiment with different empirical specifications of the jump size distributions. We use two types of data sets. One involves the Samp;P500 and the other comprises of 100 years of daily Dow Jones index. The former is a return series often used in the literature and allows us to compare our results with previous studies. The latter has the advantage to provide a long time series and enhances the possibility of estimating the jump component more precisely. The non-affine random intensity jump processes are more parsimonious than the affine class and appear to fit the data much better.
Author: Qi Li Publisher: Emerald Group Publishing ISBN: 1849506248 Category : Business & Economics Languages : en Pages : 570
Book Description
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: Ke Chen (Economist) Publisher: ISBN: Category : Languages : en Pages :
Book Description
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: Jun Pan
Author: Jun Pan
Author: CIRANO.
Author: Adjoa K. Numatsi
Author: Mikhail Chernov
Author: Qi Li
Author: Jonas Kau
Author: Jian Chen
Author: Ke Chen (Economist)
Author: Alexey Medvedev
Author: Kyriakos M. Chourdakis