Nonparametric Estimation In Models With L Vy Type Jumps And Stochastic Volatility
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Nonparametric Estimation In Models With L Vy Type Jumps And Stochastic Volatility
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Author : Cecilia Mancini
language : en
Publisher:
Release Date : 2005
Nonparametric Estimation In Models With L Vy Type Jumps And Stochastic Volatility written by Cecilia Mancini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Nonparametric Estimation In Models With Levy Type Jumps And Stochastic Volatility
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Author : Cecilia Mancini
language : it
Publisher:
Release Date : 2005
Nonparametric Estimation In Models With Levy Type Jumps And Stochastic Volatility written by Cecilia Mancini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Nonparametric Estimation In Model With Levy Type Jumps And Stochastic Volatility
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Author : Cecilia Mancini
language : en
Publisher:
Release Date : 2005
Nonparametric Estimation In Model With Levy Type Jumps And Stochastic Volatility written by Cecilia Mancini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Nonparametric Estimation In A Stochastic Volatility Model
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Author : Jürgen Franke
language : en
Publisher:
Release Date : 1998
Nonparametric Estimation In A Stochastic Volatility Model written by Jürgen Franke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Nonparametric Estimation Of Stochastic Volatility Models
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Author : Steven Cannon Hogan
language : en
Publisher:
Release Date : 2000
Nonparametric Estimation Of Stochastic Volatility Models written by Steven Cannon Hogan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.
Estimating Stochastic Volatility And Jumps Using High Frequency Data And Bayesian Methods
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Author : Milan Fičura
language : en
Publisher:
Release Date : 2015
Estimating Stochastic Volatility And Jumps Using High Frequency Data And Bayesian Methods written by Milan Fičura and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
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).
Parameter Estimation In Stochastic Volatility Models
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Author : Jaya P. N. Bishwal
language : en
Publisher: Springer Nature
Release Date : 2022-08-06
Parameter Estimation In Stochastic Volatility Models written by Jaya P. N. Bishwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-06 with Mathematics categories.
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.
Identifying Price Jumps From Daily Data With Bayesian Vs Non Parametric Methods
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Author : Milan Fičura
language : en
Publisher:
Release Date : 2017
Identifying Price Jumps From Daily Data With Bayesian Vs Non Parametric Methods written by Milan Fičura and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Non-parametric approach to financial time series jump estimation, using the L-Estimator, is compared with the parametric approach utilizing a Stochastic-Volatility-Jump-Diffusion (SVJD) model, estimated with MCMC and extended with Particle Filters to estimate the out-sample evolution of its latent state variables, such as the jump occurrences. The comparison is performed on simulated time series with different kinds of dynamics, including Poisson jumps, self-exciting Hawkes jumps with long-term clustering, as well as co-jumps. In addition to that, a comparison is performed on the real world daily time series of 4 major currency exchange rates. The results from the simulation study show that for the purposes of in-sample estimation does the MCMC based parametric approach significantly outperform the L-Estimator. In the case of the out-sample estimates, based on a combination of MCMC an Particle Filters, used to sequentially estimate the jump occurrences immediately at the times at which the jumps occur, does the parametric approach achieve a similar accuracy as the non-parametric one in the case of the simulations with Poisson jumps that are relatively large, and it outperforms the non-parametric approach in the case of Hawkes jumps when the jumps are large. On the other hand, the L-Estimator provides better results than the parametric approach in all of the cases when the simulated jumps are small (1% or less), regardless of the jump process dynamics. The application of the methods to foreign exchange rate time series further shows that the estimates of the parametric method may be biased in the case when large outlier jumps occur in the time series as well as when the stochastic volatility grows too high (as happened during the crisis). In both of these cases, the non-parametric L-Estimator based approach seems to provide more robust jump estimates, less influenced by the mentioned issues.
Nonparametric Modelling And Estimation Of Stochastic Volatility
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Author : Andreas Dürkes
language : en
Publisher:
Release Date : 2006
Nonparametric Modelling And Estimation Of Stochastic Volatility written by Andreas Dürkes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
A New Class Of Stochastic Volatility Models With Jumps
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Author : Mikhail Chernov
language : en
Publisher:
Release Date : 2012
A New Class Of Stochastic Volatility Models With Jumps written by Mikhail Chernov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
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.