Stochastic Volatility Research Papers - Academia.edu (original) (raw)

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• • by Peter Spreij
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  • Stochastic Volatility, Density Estimation, Discrete time, Kernel density estimate
• • by Sujit Ghosh
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  • Statistics, Bayesian Inference, Markov Chain Monte Carlo, Stochastic Volatility
• • by Charles Bos
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  • Time Series, Bayesian Analysis, Sampling methods, Stochastic Volatility

We extend to the Heston stochastic volatility framework the parity result of McDonald and Schroder (1998) for American call and put options.

• • by Anna Battauz
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  • Stochastic Volatility

In this note, we prove that in asset price models with lognormal stochastic volatility, when the correlation coecien t between the Brownian motion driving the volatility and the one driving the actualized asset price is positive, this... more

In this note, we prove that in asset price models with lognormal stochastic volatility, when the correlation coecien t between the Brownian motion driving the volatility and the one driving the actualized asset price is positive, this price is not a martingale.

• • by Benjamin Jourdain
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  • Stochastic Volatility, Brownian Motion, Asset Prices, Asset pricing model
• • by Caio Almeida
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  • Econometrics, Time Series, Forecasting, Stochastic Volatility
• • by Rene Garcia
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  • Latent variable, Stochastic Volatility, Latent Variables, Option pricing

This paper investigates the consequences of stochastic volatility for pricing spot foreign currency options. A diffusion model for exchange rates with stochastic volatility is proposed and estimated. The parameter estimates are then used... more

This paper investigates the consequences of stochastic volatility for pricing spot foreign currency options. A diffusion model for exchange rates with stochastic volatility is proposed and estimated. The parameter estimates are then used to price foreign currency options and the predictions are compared to observed market prices. We find that allowing volatility to be stochastic results in a much better fit to the empirical distribution of the Canada-U.S. exchange rate, and that this improvement in fit results in more accurate predictions of observed option prices.

• • by Stuart Turnbull
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  • Econometrics, Prediction, Money, Parameter estimation
• • by Jean-françois Richard
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  • Econometrics, Empirical Finance, Monte Carlo, Marginal Likelihood
• • by Carol Alexander
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  • Applied Mathematics, Stochastic Process, Path Dependence, Stochastic Volatility
• • by Guanghua Lian
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  • Applied Mathematics, Economics, Mathematical Finance, Stochastic Volatility

In this paper we compare the predictive abilility of Stochastic Volatility (SV) models to that of volatility forecasts implied by option prices. We develop an SV model with implied volatility as an exogeneous var able in the variance... more

In this paper we compare the predictive abilility of Stochastic Volatility (SV) models to that of volatility forecasts implied by option prices. We develop an SV model with implied volatility as an exogeneous var able in the variance equation which facilitates the use of statistical tests for nested models; we refer to this model as the SVX model. The SVX

• • by Eugenie Hol and +1
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  • Monte Carlo, Volatility Forecasting, Maximum Likelihood, Stochastic Volatility

This paper surveys recent developments in the theory of option pricing. The emphasis is on the interplay between option prices and investors' impatience and their aversion to risk. The traditional view, steeped in the risk-neutral... more

This paper surveys recent developments in the theory of option pricing. The emphasis is on the interplay between option prices and investors' impatience and their aversion to risk. The traditional view, steeped in the risk-neutral approach to derivative pricing, has been that these preferences play no role in the determination of option prices. However, the usual lognormality assumption required to obtain preference-free option pricing formulas is at odds with the empirical properties of financial assets. The lognormality assumption is easily reconcilable with those properties by the introduction of a latent state variable whose values can be interpreted as the states of the economy. The presence of a covariance risk with the state variable makes option prices depend explicitly on preferences. Generalized option pricing formulas, in which preferences matter, can explain several well-known empirical biases associated with preference-free models such as that of Black and Scholes (...

• • by Richard Luger
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  • Stochastic Volatility, Option pricing
• • by Lie-Jane Kao
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  • Mathematical Sciences, Stochastic Volatility, Brownian Motion, Empirical Study
• • by Ioan Oancea and +1
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  • Stochastic dominance, Risk Aversion, Stochastic Volatility, Option pricing
• • by Carol Alexander
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  • Stochastic Volatility, Hedging, Implied Volatility
• • by Rafał Weron and +1
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  • Computational Finance, Monte Carlo Simulation, Stochastic Volatility, Empirical Study
• • by Gerald Cheang
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  • Density-functional theory, Quantitative Finance, Stochastic Volatility, Free boundary Problem
• • by Julie Forman
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  • Statistics, Statistical Inference, Stochastic Volatility, Spectral method

This report covers the important topic of stochastic volatility modelling with an emphasis on linear state models. The approach taken focuses on comparing models based on their ability to flt the data and their forecasting performance. To... more

This report covers the important topic of stochastic volatility modelling with an emphasis on linear state models. The approach taken focuses on comparing models based on their ability to flt the data and their forecasting performance. To this end several parsimonious stochastic volatility models are estimated using realised volatility, a volatility proxy from high frequency stock price data. The

• • by paresh date
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  • High Frequency, Linear Model, Kalman Filtering, Stochastic Volatility
• • by Eric Renault
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  • Neural Network, Long Memory, Statistical Inference, Latent variable

We consider the pricing of options when delta hedging only takes place at discrete intervals. We show how to include transaction costs, jumps and stochastic volatility while optimally, but discretely, dynamically hedging. Copyright © 2009... more

We consider the pricing of options when delta hedging only takes place at discrete intervals. We show how to include transaction costs, jumps and stochastic volatility while optimally, but discretely, dynamically hedging. Copyright © 2009 Wilmott Magazine Ltd

• • by Hyungsok Ahn
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  • Transaction Costs, Stochastic Volatility, Poisson Process, Option pricing
• • by Gerald Cheang
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  • Economics, Density-functional theory, Quantitative Finance, Mathematical Sciences
• • by Alexander Tsyplakov
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  • Stochastic Volatility, Importance Sampling, Bayesian framework
• • by Tim Bollerslev
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  • Finance, Econometrics, Long Memory, High Frequency
• • by Donald Lien
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  • Applied Economics, Option Valuation, Stochastic Volatility, Economics Finance
• • by Fulvio Ortu
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  • Econometrics, Statistics, Stochastic Volatility, Performance Model
• • by Boda Kang and +2
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  • Mechanical Engineering, Energy Economics, Applied Economics, Stochastic Volatility
• • by Archil Gulisashvili
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  • Economics, Quantitative analysis, Derivative Pricing, Quantitative Finance
• • by Martin Keller-Ressel
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  • Applied Mathematics, Mathematical Finance, Capital Asset Pricing Model, Stochastic Volatility
• • by Fabienne Comte
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  • Model Selection, Stochastic Volatility, Laplace Transform, Density Estimation
• • by Michael McAleer
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  • Approximation Theory, Econometrics, Data Analysis, Portfolio Management
• • by Carol Alexander
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  • Stochastic Volatility
• • by Gareth Peters and +1
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  • Applied Mathematics, Statistics, Statistical Computing, Computational Finance
• • by Elisa Luciano
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  • Economics, Derivative Pricing, Quantitative Finance, Mathematical Sciences
• • by Jonathan Ziveyi
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  • Stochastic Process, Stochastic Volatility, Method of Lines, Laplace Transform
• • by Andrea Pascucci
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  • Path Dependence, Mathematical Sciences, Stochastic Volatility, Minimum variance
• • by Harald Uhlig and +1
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  • Econometrics, Stochastic Volatility, Monte Carlo Markov Chain, State Space
• • by A. Pelsser
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  • Real Estate, Seasonality, Price Discovery, Stochastic Volatility
• • by Michael McAleer
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  • Econometrics, Numerical Simulation, Financial time series, Stochastic Volatility

In this paper we compare the forecast performance of continuous and discrete-time volatility models. In discrete time, we consider more than ten GARCH-type models and an asymmetric autoregressive stochastic volatility model. In... more

In this paper we compare the forecast performance of continuous and discrete-time volatility models. In discrete time, we consider more than ten GARCH-type models and an asymmetric autoregressive stochastic volatility model. In continuous-time, a stochastic ...

• • by Helena Veiga
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  • Volatility Forecasting, Maximum Likelihood, Stochastic Volatility, Particle Filter
• • by A. Pelsser and +1
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  • Economics, Risk Management, Mathematical Sciences, Stochastic Volatility
• • by Tim Bollerslev
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  • Time Series, Stochastic Volatility, Political processes modeling and forecasting, Portfolio allocation
• • by Huyen Pham
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  • Optimization Problem, Portfolio Optimization, Stochastic Volatility, Rate of return
• • by Monique Jeanblanc
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  • Applied Mathematics, Mathematical Finance, Stochastic Volatility, Mathematical
• • by Tim Bollerslev
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  • Econometrics, Extreme Value Theory, Stochastic Volatility, Systematic Risk
• • by Norman Swanson
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  • High Frequency, Stochastic Volatility, Empirical evidence, Continuous Time Models

This paper presents a Markov chain Monte Carlo (MCMC) algorithm to estimate parameters and latent stochastic processes in the asymmetric stochastic volatility (SV) model, in which the Box-Cox transformation of the squared volatility... more

This paper presents a Markov chain Monte Carlo (MCMC) algorithm to estimate parameters and latent stochastic processes in the asymmetric stochastic volatility (SV) model, in which the Box-Cox transformation of the squared volatility follows an autoregressive Gaussian ...

• • by Maxwell King
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  • Stochastic Process, Econometrics, Empirical Finance, Markov Chain Monte Carlo

This study compares the performance of several methods to calculate the Value-at-Risk of the six main ASEAN stock markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample... more

This study compares the performance of several methods to calculate the Value-at-Risk of the six main ASEAN stock markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance is analyzed by various backtesting procedures. We find that simpler models fail to produce sufficient Value-at-Risk forecasts, which appears to stem from several econometric properties of the return distributions. With stochastic volatility models, we obtain better Value-at-Risk forecasts compared to GARCH. The quality varies over forecasting horizons and across markets. This indicates that, despite a regional proximity and homogeneity of the markets, index volatilities are driven by different factors.

• • by Thomas Walther and +1
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  • ASEAN, Stochastic Volatility, GARCH, Value at Risk
• • by Emese Lazar and +1
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  • Stochastic Volatility, GARCH, Markov switching, Levy Process

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