Gaussian Stochastic Volatility Models PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Gaussian Stochastic Volatility Models PDF full book. Access full book title Gaussian Stochastic Volatility Models by Archil Gulisashvili. Download full books in PDF and EPUB format.

Gaussian Stochastic Volatility Models

Gaussian Stochastic Volatility Models PDF Author: Archil Gulisashvili
Publisher:
ISBN:
Category :
Languages : en
Pages : 40

Book Description
In this paper, we establish sample path large and moderate deviation principles for log-price processes in Gaussian stochastic volatility models, and study the asymptotic behavior of exit probabilities, call pricing functions, and the implied volatility. In addition, we prove that if the volatility function in an uncorrelated Gaussian model grows faster than linearly, then, for the asset price process, all the moments of order greater than one are infinite. Similar moment explosion results are obtained for correlated models.

Gaussian Stochastic Volatility Models

Gaussian Stochastic Volatility Models PDF Author: Archil Gulisashvili
Publisher:
ISBN:
Category :
Languages : en
Pages : 40

Book Description
In this paper, we establish sample path large and moderate deviation principles for log-price processes in Gaussian stochastic volatility models, and study the asymptotic behavior of exit probabilities, call pricing functions, and the implied volatility. In addition, we prove that if the volatility function in an uncorrelated Gaussian model grows faster than linearly, then, for the asset price process, all the moments of order greater than one are infinite. Similar moment explosion results are obtained for correlated models.

Inference in Stochastic Volatility Models for Gaussian and T Data

Inference in Stochastic Volatility Models for Gaussian and T Data PDF Author: Nan Zheng
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 182

Book Description


Handbook of Volatility Models and Their Applications

Handbook of Volatility Models and Their Applications PDF Author: Luc Bauwens
Publisher: John Wiley & Sons
ISBN: 1118272056
Category : Business & Economics
Languages : en
Pages : 566

Book Description
A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.

The Memory of Stochastic Volatility Models

The Memory of Stochastic Volatility Models PDF Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 34

Book Description
A valid asymptotic expansion for the covariance of functions of multivariate normal vectors is applied to approximate autovariances of time series generated by nonlinear transformation of Gaussian latent variates, and nonlinear functions of these, with special reference to long memory stochastic volatility models, serving to identify the roles played by the underlying Gaussian processes and the nonlinear transformation. Implications for simple stochastic volatility models are examined in detail, with numerical and Monte Carlo calculations, and applications to cyclic behaviour, cross-sectional and temporal aggregation, and multivariate models are discussed.

Inference for Adaptive Time Series Models

Inference for Adaptive Time Series Models PDF Author: Charles Steven Bos
Publisher:
ISBN:
Category :
Languages : en
Pages : 30

Book Description


Interest Rate Models with Non-Gaussian Driven Stochastic Volatility

Interest Rate Models with Non-Gaussian Driven Stochastic Volatility PDF Author: Jiangchun Bi
Publisher:
ISBN:
Category :
Languages : en
Pages :

Book Description


Indirect Inference Methods for Stochastic Volatility Models Based on Non-Gaussian Ornstein-Uhlenbeck Processes

Indirect Inference Methods for Stochastic Volatility Models Based on Non-Gaussian Ornstein-Uhlenbeck Processes PDF Author: Arvid Raknerud
Publisher:
ISBN:
Category :
Languages : en
Pages :

Book Description


Integrated OU Processes and Non-Gaussian OU-based Stochastic Volatility Models

Integrated OU Processes and Non-Gaussian OU-based Stochastic Volatility Models PDF Author: Ole Eiler Barndorff-Nielsen
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

Book Description


Parameter Estimation in Stochastic Volatility Models

Parameter Estimation in Stochastic Volatility Models PDF Author: Jaya P. N. Bishwal
Publisher: Springer Nature
ISBN: 3031038614
Category : Mathematics
Languages : en
Pages : 634

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.

A Multivariate Non-Gaussian Stochastic Volatility Model with Leverage for Energy Markets

A Multivariate Non-Gaussian Stochastic Volatility Model with Leverage for Energy Markets PDF Author: Linda Vos
Publisher:
ISBN:
Category :
Languages : en
Pages : 31

Book Description
Spot prices in energy markets exhibit special features like price spikes, mean-reversion inverse, stochastic volatility, inverse leverage effect and co-integration between the different commodities. In this paper a multivariate stochastic volatility model is introduced which captures these features. Second order structure and stationary issues of the model are analysed. Moreover the implied multivariate forward model is derived. Due to the flexibility of the model stylized facts of the forward curve as contango, backwardation and humps are explained. Moreover, a transformed-based method to price options on the forward is described, where fast and precise algorithms for price computations can be implemented. A simulation method for Monte Carlo generation of price paths is introduced.