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Download or read book Effective Media Analysis for Stochastic Volatility Models written by and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Lorenzo Bergomi
Publisher : CRC Press
ISBN 13 : 1482244071
Total Pages : 520 pages
Book Rating : 4.4/5 (822 download)
Download or read book Stochastic Volatility Modeling written by Lorenzo Bergomi and published by CRC Press. This book was released on 2015-12-16 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Packed with insights, Lorenzo Bergomi's Stochastic Volatility Modeling explains how stochastic volatility is used to address issues arising in the modeling of derivatives, including:Which trading issues do we tackle with stochastic volatility? How do we design models and assess their relevance? How do we tell which models are usable and when does c
Author : Mike Oliver Felpel
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)
Download or read book Effective Methods for Generalized Stochastic Volatility Models written by Mike Oliver Felpel and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Leif B. G. Andersen
Publisher :
ISBN 13 :
Total Pages : 38 pages
Book Rating : 4.:/5 (129 download)
Download or read book Efficient Simulation of the Heston Stochastic Volatility Model written by Leif B. G. Andersen and published by . This book was released on 2007 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic volatility models are increasingly important in practical derivatives pricing applications, yet relatively little work has been undertaken in the development of practical Monte Carlo simulation methods for this class of models. This paper considers several new algorithms for time-discretization and Monte Carlo simulation of Heston-type stochastic volatility models. The algorithms are based on a careful analysis of the properties of affine stochastic volatility diffusions, and are straightforward and quick to implement and execute. Tests on realistic model parameterizations reveal that the computational efficiency and robustness of the simulation schemes proposed in the paper compare very favorably to existing methods.
Author : Makoto Takahashi
Publisher : Springer Nature
ISBN 13 : 981990935X
Total Pages : 120 pages
Book Rating : 4.8/5 (199 download)
Download or read book Stochastic Volatility and Realized Stochastic Volatility Models written by Makoto Takahashi and published by Springer Nature. This book was released on 2023-04-18 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This treatise delves into the latest advancements in stochastic volatility models, highlighting the utilization of Markov chain Monte Carlo simulations for estimating model parameters and forecasting the volatility and quantiles of financial asset returns. The modeling of financial time series volatility constitutes a crucial aspect of finance, as it plays a vital role in predicting return distributions and managing risks. Among the various econometric models available, the stochastic volatility model has been a popular choice, particularly in comparison to other models, such as GARCH models, as it has demonstrated superior performance in previous empirical studies in terms of fit, forecasting volatility, and evaluating tail risk measures such as Value-at-Risk and Expected Shortfall. The book also explores an extension of the basic stochastic volatility model, incorporating a skewed return error distribution and a realized volatility measurement equation. The concept of realized volatility, a newly established estimator of volatility using intraday returns data, is introduced, and a comprehensive description of the resulting realized stochastic volatility model is provided. The text contains a thorough explanation of several efficient sampling algorithms for latent log volatilities, as well as an illustration of parameter estimation and volatility prediction through empirical studies utilizing various asset return data, including the yen/US dollar exchange rate, the Dow Jones Industrial Average, and the Nikkei 225 stock index. This publication is highly recommended for readers with an interest in the latest developments in stochastic volatility models and realized stochastic volatility models, particularly in regards to financial risk management.
Author : Giacomo Bormetti
Publisher :
ISBN 13 :
Total Pages : 86 pages
Book Rating : 4.:/5 (13 download)
Download or read book A Stochastic Volatility Model with Realized Measures for Option Pricing written by Giacomo Bormetti and published by . This book was released on 2019 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical filtering and smoothing recursions for the basic specification of the model, and an effective MCMC algorithm for its richer variants. The empirical analysis shows the effectiveness of filtering and smoothing realized measures in inflating the latent volatility persistence - the crucial parameter in pricing Standard and Poor's 500 Index options.
Author : Stefanos Dimitrakopoulos
Publisher :
ISBN 13 :
Total Pages : 28 pages
Book Rating : 4.:/5 (13 download)
Download or read book Bayesian Analysis of Moving Average Stochastic Volatility Models written by Stefanos Dimitrakopoulos and published by . This book was released on 2017 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a moving average stochastic volatility in mean model and a moving average stochastic volatility model with leverage. For parameter estimation, we develop efficient Markov chain Monte Carlo algorithms and illustrate our methods, using simulated data and a real data set. We compare the proposed specifications against several competing stochastic volatility models, using marginal likelihoods and the observed-data Deviance information criterion. We find that the moving average stochastic volatility model with leverage has better fit to our daily return series than various standard benchmarks.
Author : Matthias R. Fengler
Publisher : Springer Science & Business Media
ISBN 13 : 3540305912
Total Pages : 232 pages
Book Rating : 4.5/5 (43 download)
Download or read book Semiparametric Modeling of Implied Volatility written by Matthias R. Fengler and published by Springer Science & Business Media. This book was released on 2005-12-19 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers recent advances in the theory of implied volatility and refined semiparametric estimation strategies and dimension reduction methods for functional surfaces. The first part is devoted to smile-consistent pricing approaches. The second part covers estimation techniques that are natural candidates to meet the challenges in implied volatility surfaces. Empirical investigations, simulations, and pictures illustrate the concepts.
Author : Zaizhi Wang
Publisher :
ISBN 13 :
Total Pages : 20 pages
Book Rating : 4.:/5 (129 download)
Download or read book 'Effective' Parameters for Stochastic Volatility Models written by Zaizhi Wang and published by . This book was released on 2008 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper tackles the issue of approximated formula for stochastic model with time dependent model parameters, using an averaging principle. The idea lies in finding a similar model but with constant parameters that is the closest to our initial process, along the same lines as results proven by Gyouml;ngy (1986) for general stochastic processes. We extend previous results found by Piterbarg (2005) for the particular case of SABR model (Hagan (2002)). The resulting formula can be evaluated very quickly solving the implied Riccati equations. We compare the approximation with exact solution of the corresponding partial differential equation using an ADI method. Numerical results show that the approximation works well for short term maturities.
Author : Sangjoon Kim
Publisher :
ISBN 13 :
Total Pages : 80 pages
Book Rating : 4.:/5 (753 download)
Download or read book Stochastic Volatility written by Sangjoon Kim and published by . This book was released on 1997 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Robert A. Meyers
Publisher : Springer Science & Business Media
ISBN 13 : 1441977007
Total Pages : 919 pages
Book Rating : 4.4/5 (419 download)
Download or read book Complex Systems in Finance and Econometrics written by Robert A. Meyers and published by Springer Science & Business Media. This book was released on 2010-11-03 with total page 919 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : David Insua
Publisher : John Wiley & Sons
ISBN 13 : 1118304039
Total Pages : 315 pages
Book Rating : 4.1/5 (183 download)
Download or read book Bayesian Analysis of Stochastic Process Models written by David Insua and published by John Wiley & Sons. This book was released on 2012-04-02 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.
Author : Serda Selin Ozturk
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)
Download or read book Applications of efficient importance sampling to stochastic volatility models written by Serda Selin Ozturk and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Fangwei Shi
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (16 download)
Download or read book Asymptotic Analysis of New Stochastic Volatility Models written by Fangwei Shi and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jaya P. N. Bishwal
Publisher : Springer Nature
ISBN 13 : 3031038614
Total Pages : 634 pages
Book Rating : 4.0/5 (31 download)
Download or read book Parameter Estimation in Stochastic Volatility Models written by Jaya P. N. Bishwal and published by Springer Nature. This book was released on 2022-08-06 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Daniel B. Nelson
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (535 download)
Download or read book Comment on Jacquier, Polson and Rossi's "Bayesian Analysis of Stochastic Volatility Models written by Daniel B. Nelson and published by . This book was released on 1994 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Yizhou Fang
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (19 download)
Download or read book Computationally Efficient Multi-asset Stochastic Volatility Modeling written by Yizhou Fang and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic volatility (SV) models are popular in financial modeling, because they capture the inherent uncertainty of the asset volatility. Since assets are observed to co-move together, multi-asset SV (mSV) models are more appealing than combining single-asset SV models in portfolio analysis and risk management. However, the latent volatility process renders the observed data likelihood intractable. Therefore, parameter inference typically requires computationally intensive methods to integrate the latent volatilities out, so that it is computationally challenging to estimate the model parameters. This three-part thesis is concerned with mSV modeling that is both conceptually and computationally scalable to large financial portfolios. In Part I, we explore the potential of substituting the latent volatility by an observable market proxy. For more than 20 years of out-of-sample predictions, we find that modeling the Standard and Poor's 500 (SPX) index by a simple framework of Seemingly Unrelated Regressions (SUR) with VIX volatility proxy is comparable to the benchmark Heston model with latent volatility, at a fraction of the computational cost. In Part II, we propose a new mSV model structured around a common volatility factor, which also can be proxied by an observable process. Unlike existing mSV models, the number of parameters in ours scales linearly instead of quadratically in the number of assets -- a desirable property for parameter inference of high-dimensional portfolios. Empirical evidence suggests that the common-factor volatility structure has considerable benefits for option pricing compared to a richer class of unconstrained models. In Part III, we propose an approximate method of parameter inference for mSV models based on the Kalman filter. A large-scale simulation study indicates that the approximation is orders of magnitude faster than exact inference methods, while retaining comparable accuracy.
