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Stochastic Volatility and Realized Stochastic Volatility Models

Stochastic Volatility and Realized Stochastic Volatility Models
Author: Makoto Takahashi
Publisher: Springer Nature
Total Pages: 120
Release: 2023-04-18
Genre: Business & Economics
ISBN: 981990935X

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This treatise delves into the latest advancements in stochastic volatility models, highlighting the utilization of Markov chain Monte Carlo simulations for estimating model parameters and forecasting the volatility and quantiles of financial asset returns. The modeling of financial time series volatility constitutes a crucial aspect of finance, as it plays a vital role in predicting return distributions and managing risks. Among the various econometric models available, the stochastic volatility model has been a popular choice, particularly in comparison to other models, such as GARCH models, as it has demonstrated superior performance in previous empirical studies in terms of fit, forecasting volatility, and evaluating tail risk measures such as Value-at-Risk and Expected Shortfall. The book also explores an extension of the basic stochastic volatility model, incorporating a skewed return error distribution and a realized volatility measurement equation. The concept of realized volatility, a newly established estimator of volatility using intraday returns data, is introduced, and a comprehensive description of the resulting realized stochastic volatility model is provided. The text contains a thorough explanation of several efficient sampling algorithms for latent log volatilities, as well as an illustration of parameter estimation and volatility prediction through empirical studies utilizing various asset return data, including the yen/US dollar exchange rate, the Dow Jones Industrial Average, and the Nikkei 225 stock index. This publication is highly recommended for readers with an interest in the latest developments in stochastic volatility models and realized stochastic volatility models, particularly in regards to financial risk management.


Stochastic Volatility

Stochastic Volatility
Author: Neil Shephard
Publisher: OUP Oxford
Total Pages: 536
Release: 2005-03-10
Genre: Business & Economics
ISBN: 0191531421

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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 book brings together some of the main papers that have influenced the field of the econometrics of stochastic volatility, and shows that the development of this subject has been highly multidisciplinary, with results drawn from financial economics, probability theory, and econometrics, blending to produce methods and models that have aided our understanding of the realistic pricing of options, efficient asset allocation, and accurate risk assessment. A lengthy introduction by the editor connects the papers with the literature.


A Regime-Switching Heston Model for VIX and S&P 500 Implied Volatilities

A Regime-Switching Heston Model for VIX and S&P 500 Implied Volatilities
Author: Andrew Papanicolaou
Publisher:
Total Pages: 27
Release: 2014
Genre:
ISBN:

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Volatility products have become popular in the past 15 years as a hedge against market uncertainty. In particular, there is growing interest in options on the VIX volatility index. A number of recent empirical studies examine whether there is significantly greater risk premium in VIX option prices compared with S&P 500 option prices. We address this issue by proposing and analyzing a stochastic volatility model with regime switching. The basic Heston model cannot capture VIX implied volatilities, as has been documented. We show that the incorporation sharp regime shifts can bridge this shortcoming. We take advantage of Fourier methods to make the extension tractable, and we present a fit to data, both in times of crisis and relative calm, which shows the effectiveness of the regime switching.


An Empirical Analysis of Stochastic Volatility Models

An Empirical Analysis of Stochastic Volatility Models
Author: Adrien-Paul Lambillon
Publisher:
Total Pages:
Release: 2014
Genre:
ISBN:

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The aim of this paper is to explain and apply the stochastic volatility models of Heston and GARCH to model the S&P 500 index volatility. The maximum likelihood estimate of the CIR process in the volatility equation of the Heston model and GARCH(1,1) with different underlying distributions are compared. It is shown that the model with strongest mean reversion, the CIR model, is the best volatility estimation for the overall period. For periods of volatility clustering, however, GARCH models capture the behaviour more accurately.


Options Pricing and Hedging in a Regime-Switching Volatility Model

Options Pricing and Hedging in a Regime-Switching Volatility Model
Author: Melissa Anne Mielkie
Publisher:
Total Pages: 320
Release: 2014
Genre:
ISBN:

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Both deterministic and stochastic volatility models have been used to price and hedge options. Observation of real market data suggests that volatility, while stochastic, is well modelled as alternating between two states. Under this two-state regime-switching framework, we derive coupled pricing partial differential equations (PDEs) with the inclusion of a state-dependent market price of volatility risk (MPVR) term. Since there is no closed-form solution for this pricing problem, we apply and compare two approaches to solving the coupled PDEs, assuming constant Poisson intensities. First we solve the problem using numerical solution techniques, through the application of the Crank- Nicolson numerical scheme. We also obtain approximate solutions in terms of known Black- Scholes formulae by reformulating our problem and applying the Cauchy-Kowalevski PDE theorem. Both our pricing equations and our approximate solutions give way to the analysis of the impact of our state-dependent MPVR on theoretical option prices. Using financially intuitive constraints on our option prices and Deltas, we prove the necessity of a negative MPVR. An exploration of the regime-switching option prices and their implied volatilities is given, as well as numerical results and intuition supporting our mathematical proofs. Given our regime-switching framework, there are several different hedging strategies to investigate. We consider using an option to hedge against a potential regime shift. Some practical problems arise with this approach, which lead us to set up portfolios containing a basket of two hedging options. To be more precise, we consider the effects of an option going too far in- and out-of-the-money on our hedging strategy, and introduce limits on the magnitude of such hedging option positions. A complementary approach, where constant volatility is assumed and investor's risk preferences are taken into account, is also analysed. Analysis of empirical data supports the hypothesis that volatility levels are a effected by upcoming financial events. Finally, we present an extension of our regime-switching framework with deterministic Poisson intensities. In particular, we investigate the impact of time and stock varying Poisson intensities on option prices and their corresponding implied volatilities, using numerical solution techniques. A discussion of some event-driven hedging strategies is given.