Results 1  10
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74
Bayesian Analysis of Stochastic Volatility Models
, 1994
"... this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener alized ARCH ..."
Abstract

Cited by 601 (26 self)
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ARCH (GARCH) models [see Bollerslev, Chou, and Kroner (1992) for a survey of ARCH modeling], both the mean and logvolatility equations have separate error terms. The ease of evaluating the ARCH likelihood function and the ability of the ARCH specification to accommodate the timevarying volatility
Bayesian Inference for a Periodic Stochastic Volatility Model of Intraday Electricity Prices
, 2010
"... Abstract The Gaussian stochastic volatility model is extended to allow for periodic autoregressions (PAR) in both the level and logvolatility process. Each PAR is represented as a first order vector autoregression for a longitudinal vector of length equal to the period. The periodic stochastic vola ..."
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Cited by 1 (1 self)
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. The approach is applied to estimate a periodic stochastic volatility model for halfhourly electricity prices with period m = 48. Demand and day types are included in both the mean and logvolatility equations as exogenous effects. A nonlinear relationship between demand and mean prices is uncovered which
Stochastic Volatility for Lévy Processes
, 2001
"... Three processes re°ecting persistence of volatility are initially formulated by evaluating three L¶evy processes at a time change given by the integral of a mean reverting square root process. The model for the mean reverting time change is then generalized to include NonGaussian models that are so ..."
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Cited by 209 (12 self)
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Three processes re°ecting persistence of volatility are initially formulated by evaluating three L¶evy processes at a time change given by the integral of a mean reverting square root process. The model for the mean reverting time change is then generalized to include NonGaussian models
Riemann manifold Langevin and Hamiltonian Monte Carlo methods
 J. of the Royal Statistical Society, Series B (Methodological
"... sampling methods defined on the Riemann manifold to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The methods provide fully automated adaptation mechanisms that circumvent the costly pilot ..."
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Cited by 150 (14 self)
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. The performance of these Riemannian Manifold Monte Carlo methods is rigorously assessed by performing inference on logistic regression models, logGaussian Cox point processes, stochastic volatility models, and Bayesian estimation of dynamical systems described by nonlinear differential equations. Substantial
Complete Models with Stochastic Volatility
, 1996
"... The paper proposes an original class of models for the continuous time price process of a financial security with nonconstant volatility. The idea is to define instantaneous volatility in terms of exponentiallyweighted moments of historic logprice. The instantaneous volatility is therefore driven ..."
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Cited by 76 (4 self)
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The paper proposes an original class of models for the continuous time price process of a financial security with nonconstant volatility. The idea is to define instantaneous volatility in terms of exponentiallyweighted moments of historic logprice. The instantaneous volatility is therefore
CHAPTER 4. STOCHASTIC VOLATILITY MODELS
"... ARCHtype models assume that the volatility can be observed onestepahead. However, a more realistic model for volatility can be based on modelling it having a predictable component that depends on past information and an unexpected noise. In this case, the volatility is a latent unobserved variabl ..."
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* 12* where tε is a strict white noise with variance 1. The noise of the volatility equation, tη, is assumed to be a Gaussian white noise with variance 2ησ independent of the noise of the level, tε. The Gaussianity of tη, which may seem rather ad hoc, means that the logvolatility process has a Normal
The ScaleInvariant Brownian Motion Equation and the Lognormal Cascade
 in the Stock Market. Available at SSRN: http://ssrn.com/abstract=1149142
, 2008
"... A continuoustime scaleinvariant Brownian motion (SIBM) stochastic equation is developed to investigate the dynamics of the stock market. The equation is used to solve the fat tail distribution of the stock universe and the DJIA time series. It is also used to model the volatility clustering in the ..."
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Cited by 2 (2 self)
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A continuoustime scaleinvariant Brownian motion (SIBM) stochastic equation is developed to investigate the dynamics of the stock market. The equation is used to solve the fat tail distribution of the stock universe and the DJIA time series. It is also used to model the volatility clustering
Particle Filtering of Stochastic Volatility Modeled With Leverage
"... AbstractIn this paper, we address univariate stochastic volatility models that allow for correlation of the perturbations in the state and observation equations, i.e., models with leverage. We propose a particle filtering method for estimating the posterior distributions of the logvolatility, whe ..."
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AbstractIn this paper, we address univariate stochastic volatility models that allow for correlation of the perturbations in the state and observation equations, i.e., models with leverage. We propose a particle filtering method for estimating the posterior distributions of the logvolatility
Asymptotics and calibration of local volatility models
 Quant. Finance
"... We derive a direct link between local and implied volatilities in the form of a quasilinear degenerate parabolic partial differential equation. Using this equation we establish closedform asymptotic formulae for the implied volatility near expiry as well as for deep in and outofthemoney options ..."
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Cited by 50 (1 self)
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We derive a direct link between local and implied volatilities in the form of a quasilinear degenerate parabolic partial differential equation. Using this equation we establish closedform asymptotic formulae for the implied volatility near expiry as well as for deep in and out
Bayesian analysis of a Markov switching stochastic volatility model
 Journal of Japan Statistical Society
, 2005
"... This article analyzes a Markov switching stochastic volatility (MSSV) model to accommodate the shift in the mean of logvolatility. Since it is difficult to estimate the parameters in this model based on the maximum likelihood method, a Bayesian Markovchain Monte Carlo (MCMC) approach is adopted. A ..."
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Cited by 3 (0 self)
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This article analyzes a Markov switching stochastic volatility (MSSV) model to accommodate the shift in the mean of logvolatility. Since it is difficult to estimate the parameters in this model based on the maximum likelihood method, a Bayesian Markovchain Monte Carlo (MCMC) approach is adopted
Results 1  10
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74