Estimation of Jump-diffusion Processes Via Empirical Characteristic Functions PDF Download

Author: Maria Semenova
Publisher:
ISBN:
Category :
Languages : en
Pages : 135

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Author: Maria Semenova
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Thèse. HEC. 2006

Author: Alex Levin
Publisher:
ISBN:
Category :
Languages : en
Pages : 40

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Extensions of Empirical Characteristic Function (ECF) method for estimating parameters of affine jump-diffusions with unobserved stochastic volatility (SV) are considered. We develop a new approach based on a bias-corrected ECF for the Realized Variance (in the case of diffusions) and Bipower Variation or second generation jump-robust estimators of integrated stochastic variance (in the case of jumps in the underlying). Effective numerical implementation of Unconditional and Conditional ECF methods through a special configuration of grid points in the frequency domain is proposed. The method is illustrated based on a multifactor jump-diffusion SV model with exponential Poisson jumps in the volatility and underlying correlated by a new ''Gamma-factor copula'' that allows for analytically tractable joint characteristic function. A closed form Lauricella-Kummer-type density is derived for the stationary SV distribution. This distribution extends in a certain way a Generalized Gamma Convolution family of Thorin, and it is proven to be infinitely divisible, but not always self-decomposable. Numerical results for S&P 500 Index, VIX Index and rigorous Monte-Carlo study for a number of SV models are presented.

Author: Jun Yu
Publisher:
ISBN:
Category :
Languages : en
Pages : 39

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This paper reviews the method of model-fitting via the empirical characteristic function. The advantage of using this procedure is that one can avoid difficulties inherent in calculating or maximizing the likelihood function. Thus it is a desirable estimation method when the maximum likelihood approach encounters difficulties but the characteristic function has a tractable expression. The basic idea of the empirical characteristic function method is to match the characteristic function derived from the model and the empirical characteristic function obtained from data. Ideas are illustrated by using the methodology to estimate a diffusion model that includes a self-exciting jump component. A Monte Carlo study shows that the finite sample performance of the proposed procedure offers an improvement over a GMM procedure. An application using over 72 years of DJIA daily returns reveals evidence of jump clustering.

Author: Daniel Mahoney
Publisher: Springer
ISBN: 1137560150
Category : Business & Economics
Languages : en
Pages : 547

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Commodity markets present several challenges for quantitative modeling. These include high volatilities, small sample data sets, and physical, operational complexity. In addition, the set of traded products in commodity markets is more limited than in financial or equity markets, making value extraction through trading more difficult. These facts make it very easy for modeling efforts to run into serious problems, as many models are very sensitive to noise and hence can easily fail in practice. Modeling and Valuation of Energy Structures is a comprehensive guide to quantitative and statistical approaches that have been successfully employed in support of trading operations, reflecting the author's 17 years of experience as a front-office 'quant'. The major theme of the book is that simpler is usually better, a message that is drawn out through the reality of incomplete markets, small samples, and informational constraints. The necessary mathematical tools for understanding these issues are thoroughly developed, with many techniques (analytical, econometric, and numerical) collected in a single volume for the first time. A particular emphasis is placed on the central role that the underlying market resolution plays in valuation. Examples are provided to illustrate that robust, approximate valuations are to be preferred to overly ambitious attempts at detailed qualitative modeling.

Author: Eric Jondeau
Publisher: Springer Science & Business Media
ISBN: 1846286964
Category : Mathematics
Languages : en
Pages : 541

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This book examines non-Gaussian distributions. It addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series.

Author: Masaaki Kijima
Publisher: World Scientific
ISBN: 9814366021
Category : Mathematics
Languages : en
Pages : 258

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This book contains the proceedings of the KIER-TMU International Workshop on Financial Engineering 2010, which was held in Tokyo, in order to exchange new ideas in financial engineering among industry professionals and researchers from various countries. It has been held for two consecutive years since 2009, as a successor to the Daiwa International Workshop, which was held from 2004 to 2008, and is organized by the Institute of Economic Research of Kyoto University (KIER) and the Graduate School of Social Sciences of Tokyo Metropolitan University (TMU).The workshop serves as a bridge between academic researchers and practitioners. This book consists of eleven papers ? all refereed ? representing or related to the presentations at the workshop. The papers address state-of-the-art techniques in financial engineering. The Proceedings of the 2009 workshop was also published by World Scientific Publishing.

Author:
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ISBN:
Category :
Languages : en
Pages :

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Author: Qi Li
Publisher: Emerald Group Publishing
ISBN: 1849506248
Category : Business & Economics
Languages : en
Pages : 570

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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: Rafal Weron
Publisher: John Wiley & Sons
ISBN: 0470059990
Category : Business & Economics
Languages : en
Pages : 192

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This book offers an in-depth and up-to-date review of different statistical tools that can be used to analyze and forecast the dynamics of two crucial for every energy company processes—electricity prices and loads. It provides coverage of seasonal decomposition, mean reversion, heavy-tailed distributions, exponential smoothing, spike preprocessing, autoregressive time series including models with exogenous variables and heteroskedastic (GARCH) components, regime-switching models, interval forecasts, jump-diffusion models, derivatives pricing and the market price of risk. Modeling and Forecasting Electricity Loads and Prices is packaged with a CD containing both the data and detailed examples of implementation of different techniques in Matlab, with additional examples in SAS. A reader can retrace all the intermediate steps of a practical implementation of a model and test his understanding of the method and correctness of the computer code using the same input data. The book will be of particular interest to the quants employed by the utilities, independent power generators and marketers, energy trading desks of the hedge funds and financial institutions, and the executives attending courses designed to help them to brush up on their technical skills. The text will be also of use to graduate students in electrical engineering, econometrics and finance wanting to get a grip on advanced statistical tools applied in this hot area. In fact, there are sixteen Case Studies in the book making it a self-contained tutorial to electricity load and price modeling and forecasting.