Results 1  10
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138
Modeling and Forecasting Realized Volatility
, 2002
"... this paper is built. First, although raw returns are clearly leptokurtic, returns standardized by realized volatilities are approximately Gaussian. Second, although the distributions of realized volatilities are clearly rightskewed, the distributions of the logarithms of realized volatilities are a ..."
Abstract

Cited by 549 (50 self)
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this paper is built. First, although raw returns are clearly leptokurtic, returns standardized by realized volatilities are approximately Gaussian. Second, although the distributions of realized volatilities are clearly rightskewed, the distributions of the logarithms of realized volatilities
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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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
Stochastic Volatility
 Statistics in Finance. Applications of Statistics Series
, 1996
"... The volatility of a financial asset is the variance per unit time of the logarithm of the price of the asset. Volatility has a key role to play in the determination of risk and in the valuation of options and other derivative securities. The widespread BlackScholes model for asset prices assumes co ..."
Abstract

Cited by 7 (0 self)
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The volatility of a financial asset is the variance per unit time of the logarithm of the price of the asset. Volatility has a key role to play in the determination of risk and in the valuation of options and other derivative securities. The widespread BlackScholes model for asset prices assumes
Physica A 355 ( Volatility of power markets
"... Abstract Volatility features of the Nordic day ahead power spot market for a 12year period up till May 2004 are studied. The daily logarithmic volatility was measured for this period to be about 16%. This level is well above what is observed for most other wellstudied financial markets. Volatilit ..."
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Abstract Volatility features of the Nordic day ahead power spot market for a 12year period up till May 2004 are studied. The daily logarithmic volatility was measured for this period to be about 16%. This level is well above what is observed for most other wellstudied financial markets
BOOTSTRAPPING REALIZED VOLATILITY
 SUBMITTED TO ECONOMETRICA
"... We propose bootstrap methods for a general class of nonlinear transformations of realized volatility which includes the raw version of realized volatility and its logarithmic transformation as special cases. We consider the i.i.d. bootstrap and the wild bootstrap (WB) and prove their firstorder asy ..."
Abstract

Cited by 30 (5 self)
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We propose bootstrap methods for a general class of nonlinear transformations of realized volatility which includes the raw version of realized volatility and its logarithmic transformation as special cases. We consider the i.i.d. bootstrap and the wild bootstrap (WB) and prove their first
Logarithmic Differential Evolution (LDE) for Optimization of Kinetic Parameters
 in Pyrolysis of Biomass”, Proceedings of Genetic and Evolutionary Computation Conference (GECCO2008
, 2008
"... Pyrolysis is the thermal decomposition of organic matter under inert atmospheric conditions, leading to the release of volatiles and formation of char. It is also a first step in the biomass gasification. Understanding of kinetic parameters is essential for the design of a suitable pyrolysis reactor ..."
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Cited by 2 (0 self)
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Pyrolysis is the thermal decomposition of organic matter under inert atmospheric conditions, leading to the release of volatiles and formation of char. It is also a first step in the biomass gasification. Understanding of kinetic parameters is essential for the design of a suitable pyrolysis
Nonparametric Volatility Density Estimation
, 2001
"... We consider two kinds of stochastic volatility models. Both kinds of models contain a stationary volatility process, the density of which, at a xed instant in time, we aim to estimate. We discuss discrete time models where for instance a log price process is modeled as the product of a volatility pr ..."
Abstract

Cited by 14 (2 self)
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We consider two kinds of stochastic volatility models. Both kinds of models contain a stationary volatility process, the density of which, at a xed instant in time, we aim to estimate. We discuss discrete time models where for instance a log price process is modeled as the product of a volatility
Extremes of random volatility models
, 2008
"... Extreme value theory for financial models mostly concerns the martingale part of the logarithm of a price process, since random volatility determines the extreme risk in price fluctuations. The increments (Yn)n∈Z and (Yt)t∈R of length 1 of this martingale part often have the structure ..."
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Cited by 1 (0 self)
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Extreme value theory for financial models mostly concerns the martingale part of the logarithm of a price process, since random volatility determines the extreme risk in price fluctuations. The increments (Yn)n∈Z and (Yt)t∈R of length 1 of this martingale part often have the structure
Realized Stock Volatility
, 1999
"... Using intradaily highfrequency returns on the Dow Jones Industrial Average portfolio over the January 1993 to May 1998 period, we document the properties of interdaily `realized' volatility and fit a fractionally integrated model that accounts for the leverage e#ect directly to logarithmic ..."
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Cited by 5 (1 self)
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Using intradaily highfrequency returns on the Dow Jones Industrial Average portfolio over the January 1993 to May 1998 period, we document the properties of interdaily `realized' volatility and fit a fractionally integrated model that accounts for the leverage e#ect directly to logarithmic
The Distribution of Stock Return Volatility*
, 1999
"... We exploit direct modelfree measures of daily equity return volatility and correlation obtained from highfrequency intraday transaction prices on individual stocks in the Dow Jones Industrial Average over a fiveyear period to confirm, solidify and extend existing characterizations of stock return ..."
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return volatility and correlation. We find that the unconditional distributions of the variances and covariances for all thirty stocks are leptokurtic and highly skewed to the right, while the logarithmic standard deviations and correlations all appear approximately Gaussian. Moreover, the distributions
Results 1  10
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138