Estimating Stochastic Volatility Models Through Indirect Inference 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 Estimating Stochastic Volatility Models Through Indirect Inference PDF full book. Access full book title Estimating Stochastic Volatility Models Through Indirect Inference by Chiara Monfardini. Download full books in PDF and EPUB format.
Author: Chiara Monfardini
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
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: Chiara Monfardini
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
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: Mario Philipp Rothfelder
Publisher:
ISBN:
Category :
Languages : en
Pages : 42
Get Book Here
Book Description
Author: Chiara Monfardini
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Chiara Monfardini
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 44
Get Book Here
Book Description
Author: Jaya P. N. Bishwal
Publisher: Springer Nature
ISBN: 3031038614
Category : Mathematics
Languages : en
Pages : 634
Get Book Here
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.
Author: Patrick Gagliardini
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Claudia Foroni
Publisher:
ISBN: 9783865587817
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Eduardo Rossi
Publisher:
ISBN:
Category :
Languages : en
Pages : 54
Get Book Here
Book Description
We propose the indirect inference estimator as a consistent method to estimate the parameters of a structural model when the observed series are contaminated by measurement error by considering the noise as a structural feature. We show that the indirect inference estimates are asymptotically biased if the error is neglected. Instead, if the condition for identification is satisfied, the measurement error parameters can be estimated jointly with the structural ones leading to a consistent and asymptotically Gaussian estimator. The issues of identification and misspecification of ME are discussed in detail. Based on the encompassing principle, we show that II can still be consistent for the parameters of interest when the conditional distribution of ME is misspecified, as long as the structural model for the observed series encompasses the auxiliary. We illustrate the reliability of this procedure in the estimation of stochastic volatility models based on realized volatility measures contaminated by microstructure noise. The empirical application stresses the importance of a realistic specification of the microstructure noise distribution to match the features of the observed log-returns at high frequencies.
Author: Fabio Fornari
Publisher:
ISBN:
Category : Interest rates
Languages : en
Pages : 76
Get Book Here
Book Description
Author: Vickneswary Tagore
Publisher:
ISBN:
Category : Finance
Languages : en
Pages : 220
Get Book Here
Book Description
