Parameter Estimation in Stochastic Volatility Models

Parameter Estimation in Stochastic Volatility Models

Author: Jaya P. N. Bishwal

Publisher: Springer Nature

Published: 2022-08-06

Total Pages: 634

ISBN-13: 3031038614

DOWNLOAD EBOOK

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.


Maximum Likelihood Estimation of Stochastic Volatility Models

Maximum Likelihood Estimation of Stochastic Volatility Models

Author: Yacine Aït-Sahalia

Publisher:

Published: 2004

Total Pages: 42

ISBN-13:

DOWNLOAD EBOOK

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.


Maximum Likelihood Estimation of Stochastic Volatility Models

Maximum Likelihood Estimation of Stochastic Volatility Models

Author: Yacine Ait-Sahalia

Publisher:

Published: 2009

Total Pages: 44

ISBN-13:

DOWNLOAD EBOOK

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.


Application of Stochastic Volatility Models in Option Pricing

Application of Stochastic Volatility Models in Option Pricing

Author: Pascal Debus

Publisher: GRIN Verlag

Published: 2013-09-09

Total Pages: 59

ISBN-13: 3656491941

DOWNLOAD EBOOK

Bachelorarbeit aus dem Jahr 2010 im Fachbereich BWL - Investition und Finanzierung, Note: 1,2, EBS Universität für Wirtschaft und Recht, Sprache: Deutsch, Abstract: The Black-Scholes (or Black-Scholes-Merton) Model has become the standard model for the pricing of options and can surely be seen as one of the main reasons for the growth of the derivative market after the model ́s introduction in 1973. As a consequence, the inventors of the model, Robert Merton, Myron Scholes, and without doubt also Fischer Black, if he had not died in 1995, were awarded the Nobel prize for economics in 1997. The model, however, makes some strict assumptions that must hold true for accurate pricing of an option. The most important one is constant volatility, whereas empirical evidence shows that volatility is heteroscedastic. This leads to increased mispricing of options especially in the case of out of the money options as well as to a phenomenon known as volatility smile. As a consequence, researchers introduced various approaches to expand the model by allowing the volatility to be non-constant and to follow a sto-chastic process. It is the objective of this thesis to investigate if the pricing accuracy of the Black-Scholes model can be significantly improved by applying a stochastic volatility model.


Small-time Asymptotics and Expansions of Option Prices Under Levy-based Models

Small-time Asymptotics and Expansions of Option Prices Under Levy-based Models

Author: Ruoting Gong

Publisher:

Published: 2012

Total Pages:

ISBN-13:

DOWNLOAD EBOOK

This thesis is concerned with the small-time asymptotics and expansions of call option prices, when the log return processes of the underlying stock prices follow several Levy-based models. To be specific, we derive the time-to-maturity asymptotoc behavior for both at-the-money (ATM, out-of-the-money (OTM) and in-the-money (ITM) call option prices under several jump diffusion models and stochastic volatility models with Levy jumps. In the OTM and ITM cases, we consider a general stochastic volatility model with independent Levy jumps, while in the ATM case, we consider the pure-jump CGMY model with or without an independent Brownian component. An accurate modeling of the option market and asset prices requires a mixture of a continuous diffusive component and a jump component. In this thesis, we first model the log-return process of a fisk asset with a jump diffusion model by combining a stochastic volatility model with an independent pure-jump Levy process. By assuming smoothness conditions on the Levy density away from the origin and a small-time large deviation principle on the stochastic volatility model, we derive the small-time expansions, of arbitrary polynomial order, in time-t, for the tail distribution of the log-return process, and for the call-option price which is not at-the-money. Moreover, our approach allows for a unified treatment of more general payoff functions. As a consequence of our tail expansions, the polynomial expansion in t of the transition density is also obtained under mild conditions. The asymptotic behavior of the ATM call-option prices is more complicated to obtain, and, in general, is given by fractional powers of t, which depends on different choices of the underlying log-return models. Here, we focus on the CGMY model, one of the most popular tempered stable models used in financial modeling. A novel second-order approximation for ATM option prices under the pure-jump CGMY Levy model is derived, and then extended to a model with an additional independent Brownian component. The third-order asymptotic behavior of the ATM option prices as well as the asymptotic behavior of the corresponding Black-Scholes implied volatilities are also addressed.


An Asymptotic Expansion Formula for Up-and-Out Barrier Option Price Under Stochastic Volatility Model

An Asymptotic Expansion Formula for Up-and-Out Barrier Option Price Under Stochastic Volatility Model

Author: Takashi Kato

Publisher:

Published: 2014

Total Pages: 0

ISBN-13:

DOWNLOAD EBOOK

This paper derives a new semi closed-form approximation formula for pricing an up-and-out barrier option under a certain type of stochastic volatility model including SABR model by applying a rigorous asymptotic expansion method developed by Kato, Takahashi and Yamada (2012). We also demonstrate the validity of our approximation method through numerical examples.


Stochastic Volatility

Stochastic Volatility

Author: Neil Shephard

Publisher: Oxford University Press, USA

Published: 2005

Total Pages: 534

ISBN-13: 0199257205

DOWNLOAD EBOOK

Stochastic volatility is the main concept used in the fields of financial economics and mathematical finance to deal with time-varying volatility in financial markets. This work brings together some of the main papers that have influenced this field, andshows that the development of this subject has been highly multidisciplinary.


Modeling Stochastic Volatility with Application to Stock Returns

Modeling Stochastic Volatility with Application to Stock Returns

Author: Mr.Noureddine Krichene

Publisher: International Monetary Fund

Published: 2003-06-01

Total Pages: 30

ISBN-13: 1451854846

DOWNLOAD EBOOK

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.