Author : Arvid Raknerud
Publisher :
Page : pages
File Size : 28,76 MB
Release : 2009
Category :
ISBN :
Author : Chiara Monfardini
Publisher :
Page : pages
File Size : 27,39 MB
Release : 1999
Category :
ISBN :
We propose as a tool for the estimation of stochastic volatility models two indirect inference estimators based on the choice of an autoregressive auxiliary model and an ARMA auxiliary model, respectively. These choices make the auxiliary parameter easy to estimate and at the same time allow the derivation of optimal indirect inference estimators. The results of some Monte Carlo experiments provide evidence that the indirect inference estimators perform well in finite sample, although less efficiently than Bayes and Simulated EM algorithms.
Author : S.T Rachev
Publisher : Elsevier
Page : 707 pages
File Size : 36,15 MB
Release : 2003-03-05
Category : Business & Economics
ISBN : 0080557732
The Handbooks in Finance are intended to be a definitive source for comprehensive and accessible information in the field of finance. Each individual volume in the series should present an accurate self-contained survey of a sub-field of finance, suitable for use by finance and economics professors and lecturers, professional researchers, graduate students and as a teaching supplement. The goal is to have a broad group of outstanding volumes in various areas of finance. The Handbook of Heavy Tailed Distributions in Finance is the first handbook to be published in this series.This volume presents current research focusing on heavy tailed distributions in finance. The contributions cover methodological issues, i.e., probabilistic, statistical and econometric modelling under non- Gaussian assumptions, as well as the applications of the stable and other non -Gaussian models in finance and risk management.
Author : Jaya P. N. Bishwal
Publisher : Springer Nature
Page : 634 pages
File Size : 36,13 MB
Release : 2022-08-06
Category : Mathematics
ISBN : 3031038614
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 : Chiara Monfardini
Publisher :
Page : pages
File Size : 45,56 MB
Release : 1996
Category :
ISBN :
Author : Mario Philipp Rothfelder
Publisher :
Page : 42 pages
File Size : 13,62 MB
Release : 2010
Category :
ISBN :
Author : Rainer Schöbel
Publisher :
Page : 34 pages
File Size : 16,25 MB
Release : 1998
Category :
ISBN :
Author : Nan Zheng
Publisher :
Page : 182 pages
File Size : 35,11 MB
Release : 2013
Category : Estimation theory
ISBN :
Author : Matthew Peter Sandford Gander
Publisher :
Page : pages
File Size : 35,14 MB
Release : 2004
Category :
ISBN :
Author : Francisco Blasques
Publisher :
Page : 0 pages
File Size : 24,99 MB
Release : 2023
Category :
ISBN :
This paper considers a stochastic volatility model featuring an asymmetric stable error distribution and a novel way of accounting for the leverage effect. We adopt simulation-based methods to address key challenges in parameter estimation, the filtering of time-varying volatility, and volatility forecasting. Specifically, we make use of the indirect inference method to estimate the static parameters, and the extremum Monte Carlo method to extract latent volatility. Both methods can be easily adapted to modifications of the model, such as having other distributions for the errors and other dynamic specifications for the volatility process. Illustrations are presented for a simulated dataset and for an empirical application to a time series of Bitcoin returns.
