Results 1  10
of
470
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 392 (41 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 are approximately Gaussian. Third, the longrun dynamics of realized logarithmic volatilities are well approximated by a fractionallyintegrated longmemory process. Motivated by the three ABDL empirical regularities, we proceed to estimate and evaluate a multivariate model for the logarithmic realized volatilities: a fractionallyintegrated Gaussian vector autoregression (VAR) . Importantly, our approach explicitly permits measurement errors in the realized volatilities. Comparing the resulting volatility forecasts to those obtained from currently popular daily volatility models and more complicated highfrequency models, we find that our simple Gaussian VAR forecasts generally produce superior forecasts. Furthermore, we show that, given the theoretically motivated and empirically plausible assumption of normally distributed returns conditional on the realized volatilities, the resulting lognormalnormal mixture forecast distribution provides conditionally wellcalibrated density forecasts of returns, from which we obtain accurate estimates of conditional return quantiles. In the remainder of this paper, we proceed as follows. We begin in section 2 by formally developing the relevant quadratic variation theory within a standard frictionless arbitragefree multivariate pricing environment. In section 3 we discuss the practical construction of realized volatilities from highfrequency foreign exchange returns. Next, in section 4 we summarize the salient distributional features of r...
The Distribution of Realized Exchange Rate Volatility
 Journal of the American Statistical Association
, 2001
"... Using highfrequency data on deutschemark and yen returns against the dollar, we construct modelfree estimates of daily exchange rate volatility and correlation that cover an entire decade. Our estimates, termed realized volatilities and correlations, are not only modelfree, but also approximately ..."
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Cited by 231 (22 self)
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Using highfrequency data on deutschemark and yen returns against the dollar, we construct modelfree estimates of daily exchange rate volatility and correlation that cover an entire decade. Our estimates, termed realized volatilities and correlations, are not only modelfree, but also approximately free of measurement error under general conditions, which we discuss in detail. Hence, for practical purposes, we may treat the exchange rate volatilities and correlations as observed rather than latent. We do so, and we characterize their joint distribution, both unconditionally and conditionally. Noteworthy results include a simple normalityinducing volatility transformation, high contemporaneous correlation across volatilities, high correlation between correlation and volatilities, pronounced and persistent dynamics in volatilities and correlations, evidence of longmemory dynamics in volatilities and correlations, and remarkably precise scaling laws under temporal aggregation.
The distribution of realized stock return volatility
, 2001
"... We examine "realized" daily equity return volatilities and correlations obtained from highfrequency intraday transaction prices on individual stocks in the Dow Jones ..."
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Cited by 172 (14 self)
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We examine "realized" daily equity return volatilities and correlations obtained from highfrequency intraday transaction prices on individual stocks in the Dow Jones
On the Detection and Estimation of Long Memory in Stochastic Volatility
, 1995
"... Recent studies have suggested that stock markets' volatility has a type of longrange dependence that is not appropriately described by the usual Generalized Autoregressive Conditional Heteroskedastic (GARCH) and Exponential GARCH (EGARCH) models. In this paper, different models for describing ..."
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Cited by 169 (6 self)
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Recent studies have suggested that stock markets' volatility has a type of longrange dependence that is not appropriately described by the usual Generalized Autoregressive Conditional Heteroskedastic (GARCH) and Exponential GARCH (EGARCH) models. In this paper, different models for describing this longrange dependence are examined and the properties of a LongMemory Stochastic Volatility (LMSV) model, constructed by incorporating an Autoregressive Fractionally Integrated Moving Average (ARFIMA) process in a stochastic volatility scheme, are discussed. Strongly consistent estimators for the parameters of this LMSV model are obtained by maximizing the spectral likelihood. The distribution of the estimators is analyzed by means of a Monte Carlo study. The LMSV is applied to daily stock market returns providing an improved description of the volatility behavior. In order to assess the empirical relevance of this approach, tests for longmemory volatility are described and applied to an e...
Occasional Structural Breaks And Long Memory
 JOURNAL OF EMPIRICAL FINANCE
, 1999
"... This paper shows that a linear process with breaks can mimic autocorrelations and other properties of I(d) processes, where d can be a fraction. Simulation results show that S&P 500 absolute stock returns are more likely to show the "long memory" property because of the presence of bre ..."
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Cited by 114 (2 self)
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This paper shows that a linear process with breaks can mimic autocorrelations and other properties of I(d) processes, where d can be a fraction. Simulation results show that S&P 500 absolute stock returns are more likely to show the "long memory" property because of the presence of breaks in the series rather than an I(d) process. KEY WORDS: Occasional Structural Breaks; Long Memory; Autocorrelation JEL classification: C22 Preliminary, Comments Welcome 2 1. Introduction There have been several works analyzing the longrun properties of stock returns. Granger and Ding (1995a,b) considered long return series, using the wellknown Standard and Poor's (S&P) 500 index of about 17,000 daily observations, and established a set of temporal and distributional properties for such series. They suggested that the absolute returns are well characterized by long memory process, but the parameter estimates of the longmemory model sometimes vary considerably from one subseries to the next as show...
Separating microstructure noise from volatility
, 2006
"... There are two variance components embedded in the returns constructed using high frequency asset prices: the timevarying variance of the unobservable efficient returns that would prevail in a frictionless economy and the variance of the equally unobservable microstructure noise. Using sample moment ..."
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Cited by 95 (6 self)
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There are two variance components embedded in the returns constructed using high frequency asset prices: the timevarying variance of the unobservable efficient returns that would prevail in a frictionless economy and the variance of the equally unobservable microstructure noise. Using sample moments of high frequency return data recorded at different frequencies, we provide a simple and robust technique to identify both variance components. In the context of a volatilitytiming trading strategy, we show that careful (optimal) separation of the two volatility components of the observed stock returns yields substantial utility gains.
Is All That Talk Just Noise ? The Information Content of Internet Stock Message Boards
 Journal of Finance
, 2004
"... Financial press reports claim that internet stock message boards can move markets. We study the effect of more than 1.5 million messages posted on Yahoo! Finance and Raging Bull about the 45 companies in the Dow Jones Industrial Average, and the Dow Jones Internet Index. The bullishness of the messa ..."
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Cited by 90 (2 self)
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Financial press reports claim that internet stock message boards can move markets. We study the effect of more than 1.5 million messages posted on Yahoo! Finance and Raging Bull about the 45 companies in the Dow Jones Industrial Average, and the Dow Jones Internet Index. The bullishness of the messages is measured using computational linguistics methods. News stories reported in the Wall Street Journal are used as controls. We find significant evidence that the stock messages help predict market volatility, but not stock returns. Consistent with Harris and Raviv (1993), agreement among the posted messages is associated with decreased trading volume. (JEL: G12, G14)
Exact local Whittle estimation of fractional integration
, 2005
"... An exact form of the local Whittle likelihood is studied with the intent of developing a generalpurpose estimation procedure for the memory parameter (d) that does not rely on tapering or differencing prefilters. The resulting exact local Whittle estimator is shown to be consistent and to have the ..."
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Cited by 78 (13 self)
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An exact form of the local Whittle likelihood is studied with the intent of developing a generalpurpose estimation procedure for the memory parameter (d) that does not rely on tapering or differencing prefilters. The resulting exact local Whittle estimator is shown to be consistent and to have the same N(0, 1/4) limit distribution for all values of d if the optimization covers an interval of width less than 9/2 and the initial value of the process is known.
NonStationarities in Financial Time Series, the Long Range Dependence and the IGARCH Effects
 Review of Economics and Statistics
, 2002
"... In this paper we give the theoretical basis of a possible explanation for two stylized facts observed in long logreturn series: the long range dependence (LRD) in volatility and the integrated GARCH (IGARCH). Both these eects can be theoretically explained if one assumes that the data is nonsta ..."
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Cited by 57 (6 self)
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In this paper we give the theoretical basis of a possible explanation for two stylized facts observed in long logreturn series: the long range dependence (LRD) in volatility and the integrated GARCH (IGARCH). Both these eects can be theoretically explained if one assumes that the data is nonstationary.