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Author: Jiří Witzany
Publisher:
ISBN:
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Languages : en
Pages :
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Book Description
We formulate a bivariate stochastic volatility jump-diffusion model with correlated jumps and volatilities. An MCMC Metropolis-Hastings sampling algorithm is proposed to estimate the model's parameters and latent state variables (jumps and stochastic volatilities) given observed returns. The methodology is successfully tested on several artificially generated bivariate time series and then on the two most important Czech domestic financial market time series of the FX (CZK/EUR) and stock (PX index) returns. Four bivariate models with and without jumps and/or stochastic volatility are compared using the deviance information criterion (DIC) confirming importance of incorporation of jumps and stochastic volatility into the model. -- jump-diffusion ; stochastic volatility ; MCMC ; Value at Risk ; Monte Carlo
Author: Jiří Witzany
Publisher:
ISBN:
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Languages : en
Pages :
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Book Description
We formulate a bivariate stochastic volatility jump-diffusion model with correlated jumps and volatilities. An MCMC Metropolis-Hastings sampling algorithm is proposed to estimate the model's parameters and latent state variables (jumps and stochastic volatilities) given observed returns. The methodology is successfully tested on several artificially generated bivariate time series and then on the two most important Czech domestic financial market time series of the FX (CZK/EUR) and stock (PX index) returns. Four bivariate models with and without jumps and/or stochastic volatility are compared using the deviance information criterion (DIC) confirming importance of incorporation of jumps and stochastic volatility into the model. -- jump-diffusion ; stochastic volatility ; MCMC ; Value at Risk ; Monte Carlo
Author: Mikhail Chernov
Publisher:
ISBN:
Category :
Languages : en
Pages : 37
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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: Milan Fičura
Publisher:
ISBN:
Category :
Languages : en
Pages : 25
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Book Description
We are comparing two approaches for stochastic volatility and jumps estimation in the EUR/USD time series - the non-parametric power-variation approach using high-frequency returns, and the parametric Bayesian approach (MCMC estimation of SVJD models) using daily returns. We find that both of the methods do identify continuous stochastic volatility similarly, but they do not identify similarly the jump component. Firstly - the jumps estimated using the non-parametric high-frequency estimators are much more numerous than in the case of the Bayesian method using daily data. More importantly - we find that the probabilities of jump occurrences assigned to every day by both of the methods are virtually no rank-correlated (Spearman rank correlation is 0.0148) meaning that the two methods do not identify jumps at the same days. Actually the jump probabilities inferred using the non-parametric approach are not much correlated even with the daily realized variance and the daily squared returns, indicating that the discontinuous price changes (jumps) observed on high-frequencies may not be distinguishable (from the continuous volatility) on the daily frequency. As an additional result we find strong evidence for jump size dependence and jump clustering (based on the self-exciting Hawkes process) of the jumps identified using the non-parametric method (the shrinkage estimator).
Author: Jaya P. N. Bishwal
Publisher: Springer Nature
ISBN: 3031038614
Category : Mathematics
Languages : en
Pages : 634
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Book Description
This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.
Author: Jonas Kau
Publisher:
ISBN:
Category :
Languages : en
Pages : 163
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Author: Mthuli Ncube
Publisher:
ISBN: 9783659241192
Category :
Languages : en
Pages : 124
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Book Description
Author: Christian Gouriéroux
Publisher: OUP Oxford
ISBN: 019152509X
Category : Business & Economics
Languages : en
Pages : 190
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Book Description
This book introduces a new generation of statistical econometrics. After linear models leading to analytical expressions for estimators, and non-linear models using numerical optimization algorithms, the availability of high- speed computing has enabled econometricians to consider econometric models without simple analytical expressions. The previous difficulties presented by the presence of integrals of large dimensions in the probability density functions or in the moments can be circumvented by a simulation-based approach. After a brief survey of classical parametric and semi-parametric non-linear estimation methods and a description of problems in which criterion functions contain integrals, the authors present a general form of the model where it is possible to simulate the observations. They then move to calibration problems and the simulated analogue of the method of moments, before considering simulated versions of maximum likelihood, pseudo-maximum likelihood, or non-linear least squares. The general principle of indirect inference is presented and is then applied to limited dependent variable models and to financial series.
Author: Ke Chen (Economist)
Publisher:
ISBN:
Category :
Languages : en
Pages :
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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: CIRANO.
Publisher: Montréal : CIRANO
ISBN:
Category :
Languages : en
Pages : 35
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Book Description
Author: Robert A. Meyers
Publisher: Springer Science & Business Media
ISBN: 1441977007
Category : Business & Economics
Languages : en
Pages : 919
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Book Description
Finance, Econometrics and System Dynamics presents an overview of the concepts and tools for analyzing complex systems in a wide range of fields. The text integrates complexity with deterministic equations and concepts from real world examples, and appeals to a broad audience.
