Estimation and identification in long-memory stochastic volatility models
Author : Ana Perez Espartero
Publisher :
Page : 0 pages
File Size : 11,9 MB
Release : 2000
Category :
ISBN :
Long-memory Stochastic Volatility Models
Author : Libo Xie
Publisher :
Page : 0 pages
File Size : 41,97 MB
Release : 2008
Category :
ISBN :
Book Description
Option Pricing with Long Memory Stochastic Volatility Models
Author : Zhigang Tong
Publisher : LAP Lambert Academic Publishing
Page : 184 pages
File Size : 37,82 MB
Release : 2013
Category :
ISBN : 9783659346279
Book Description
It is now known that long memory stochastic volatility models can capture the well-documented evidence of volatility persistence. However, due to the complex structures of the long memory processes, the analytical formulas for option prices are not available yet. In this book, we propose two fractional continuous time stochastic volatility models which are built on the popular short memory stochastic volatility models. Using the tools from stochastic calculus, fractional calculus and Fourier transform, we derive the (approximate) analytical solutions for option prices. We also numerically study the effects of long memory on option prices. We show that the fractional integration parameter has the opposite effect to that of volatility of volatility parameter. We also find that long memory models can accommodate the short term options and the decay of volatility skew better than the corresponding short memory models. These findings would appeal to the researchers and practitioners in the areas of quantitative finance.
On Estimation, Diagnostic Testing and Smoothing of Long Memory Stochastic Volatility Models
Author : Kai Li
Publisher :
Page : 35 pages
File Size : 43,81 MB
Release : 2000
Category : Rate of return
ISBN :
Book Description
Long Memory in Continuous Time
Author : Centre de recherche en économie et statistique (Paris, France)
Publisher :
Page : 54 pages
File Size : 47,62 MB
Release : 1996
Category :
ISBN :
Book Description
Option Pricing with Long Memory Stochastic Volatility Models
Author : Zhigang Tong
Publisher :
Page : pages
File Size : 27,86 MB
Release : 2012
Category : Options (Finance)
ISBN : 9780494862469
Book Description
In this thesis, we propose two continuous time stochastic volatility models with long memory that generalize two existing models. More importantly, we provide analytical formulae that allow us to study option prices numerically, rather than by means of simulation. We are not aware about analytical results in continuous time long memory case. In both models, we allow for the non-zero correlation between the stochastic volatility and stock price processes. We numerically study the effects of long memory on the option prices. We show that the fractional integration parameter has the opposite effect to that of volatility of volatility parameter in short memory models. We also find that long memory models have the potential to accommodate the short term options and the decay of volatility skew better than the corresponding short memory stochastic volatility models.
Parameter Estimation in Stochastic Volatility Models
Author : Jaya P. N. Bishwal
Publisher : Springer Nature
Page : 634 pages
File Size : 32,77 MB
Release : 2022-08-06
Category : Mathematics
ISBN : 3031038614
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.
Long Memory Stochastic Volatility Models of Latin American Stock Markets
Author : Alejandro Islas Camargo
Publisher :
Page : 56 pages
File Size : 15,22 MB
Release : 2000
Category : Stock price indexes
ISBN :
Book Description
Semiparametric Bayesian Inference of Long-Memory Stochastic Volatility Models
Author : Mark J. Jensen
Publisher :
Page : 0 pages
File Size : 28,53 MB
Release : 2004
Category :
ISBN :
Book Description
In this paper, a semiparametric, Bayesian estimator of the long-memory stochastic volatility model's fractional order of integration is presented. This new estimator relies on a highly efficient, Markov chain Monte Carlo (MCMC) sampler of the model's posterior distribution. The MCMC algorithm is set forth in the time-scale domain of the stochastic volatility model's wavelet representation. The key to and centerpiece of this new algorithm is the quick and efficient multi-state sampler of the latent volatility's wavelet coefficients. A multi-state sampler of the latent wavelet coefficients is only possible because of the near-independent multivariate distribution of the long-memory process's wavelet coefficients. Using simulated and empirical stock return data, we find that our algorithm produces uncorrelated draws of the posterior distribution and point estimates that rival existing long-memory stochastic volatility estimators.
Handbook of Modeling High-Frequency Data in Finance
Author : Frederi G. Viens
Publisher : John Wiley & Sons
Page : 468 pages
File Size : 43,23 MB
Release : 2011-12-20
Category : Business & Economics
ISBN : 0470876883
Book Description
CUTTING-EDGE DEVELOPMENTS IN HIGH-FREQUENCY FINANCIAL ECONOMETRICS In recent years, the availability of high-frequency data and advances in computing have allowed financial practitioners to design systems that can handle and analyze this information. Handbook of Modeling High-Frequency Data in Finance addresses the many theoretical and practical questions raised by the nature and intrinsic properties of this data. A one-stop compilation of empirical and analytical research, this handbook explores data sampled with high-frequency finance in financial engineering, statistics, and the modern financial business arena. Every chapter uses real-world examples to present new, original, and relevant topics that relate to newly evolving discoveries in high-frequency finance, such as: Designing new methodology to discover elasticity and plasticity of price evolution Constructing microstructure simulation models Calculation of option prices in the presence of jumps and transaction costs Using boosting for financial analysis and trading The handbook motivates practitioners to apply high-frequency finance to real-world situations by including exclusive topics such as risk measurement and management, UHF data, microstructure, dynamic multi-period optimization, mortgage data models, hybrid Monte Carlo, retirement, trading systems and forecasting, pricing, and boosting. The diverse topics and viewpoints presented in each chapter ensure that readers are supplied with a wide treatment of practical methods. Handbook of Modeling High-Frequency Data in Finance is an essential reference for academics and practitioners in finance, business, and econometrics who work with high-frequency data in their everyday work. It also serves as a supplement for risk management and high-frequency finance courses at the upper-undergraduate and graduate levels.
