Estimating A Stochastic Volatility Model For Dax Index Options
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Release | : 2003 |
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Download Estimating a Stochastic Volatility Model for DAX-Index Options Book in PDF, Epub and Kindle
The paper examines alternative strategies for pricing and hedging options on German DAX-index. To this purpose an affine stochastic volatility model is estimated directly on objective probability system through a three step approach. Errors obtained by the implementation of the stochastic volatility model and Black and Scholes with different historical and implied volatility measures are compared and the performance is evaluated in terms of out-of-sample pricing and hedging. The results for DAX-index options market support the estimation on the affine stochastic volatility model in pricing as well as in hedging procedures.
Author | : Marzia Freo |
Publisher | : |
Total Pages | : 17 |
Release | : 2003 |
Genre | : |
ISBN | : |
Download Estimating a Stochastic Volatility Model for DAX-Index Options Book in PDF, Epub and Kindle
Author | : Marzia Freo |
Publisher | : |
Total Pages | : 17 |
Release | : 2003 |
Genre | : |
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Download Estimating a Stochastic Volatily Model for DAX-index Options Book in PDF, Epub and Kindle
Author | : Matthias R. Fengler |
Publisher | : Springer Science & Business Media |
Total Pages | : 232 |
Release | : 2005-12-19 |
Genre | : Business & Economics |
ISBN | : 3540305912 |
Download Semiparametric Modeling of Implied Volatility Book in PDF, Epub and Kindle
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 | : Yacine Ait-Sahalia |
Publisher | : |
Total Pages | : 44 |
Release | : 2009 |
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Download Maximum Likelihood Estimation of Stochastic Volatility Models Book in PDF, Epub and Kindle
We develop and implement a new method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by the implied volatility of a short dated at-the-money option. We find that the approximation results in a negligible loss of accuracy. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine model of Heston (1993) and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.
Author | : Jaya P. N. Bishwal |
Publisher | : Springer Nature |
Total Pages | : 634 |
Release | : 2022-08-06 |
Genre | : Mathematics |
ISBN | : 3031038614 |
Download Parameter Estimation in Stochastic Volatility Models Book in PDF, Epub and Kindle
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 | : Nazibrola Lordkipanidze |
Publisher | : |
Total Pages | : 296 |
Release | : 2004 |
Genre | : |
ISBN | : |
Download Modeling and Estimation of Long-memory in Stochastic Volatility Book in PDF, Epub and Kindle
Author | : Robert A. Jarrow |
Publisher | : |
Total Pages | : 472 |
Release | : 1998 |
Genre | : Derivative securities |
ISBN | : |
Download Volatility Book in PDF, Epub and Kindle
Written by a number of authors, this text is aimed at market practitioners and applies the latest stochastic volatility research findings to the analysis of stock prices. It includes commentary and analysis based on real-life situations.
Author | : Yacine Aït-Sahalia |
Publisher | : |
Total Pages | : 42 |
Release | : 2004 |
Genre | : Options (Finance) |
ISBN | : |
Download Maximum Likelihood Estimation of Stochastic Volatility Models Book in PDF, Epub and Kindle
We develop and implement a new method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by the implied volatility of a short dated at-the-money option. We find that the approximation results in a negligible loss of accuracy. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine model of Heston (1993) and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.
Author | : Mr.Noureddine Krichene |
Publisher | : International Monetary Fund |
Total Pages | : 30 |
Release | : 2003-06-01 |
Genre | : Business & Economics |
ISBN | : 1451854846 |
Download Modeling Stochastic Volatility with Application to Stock Returns Book in PDF, Epub and Kindle
A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was consistent with both volatility clustering and mean reversion. Filtering showed highly volatile markets, reflecting frequent pertinent news. Diagnostics showed no model failure, although specification improvements were always possible. The model corroborated stylized findings in volatility modeling and has potential value for market participants in asset pricing and risk management, as well as for policymakers in the design of macroeconomic policies conducive to less volatile financial markets.