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633
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 ..."
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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 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...
Long memory processes and fractional integration in Econometrics
 JOURNAL OF NOMETRI ELSEVIER JOURNAL OF ECONOMETRICS 73{1996) 5 59
, 1996
"... This paper provides a survey and review of the major econometric work on long memory processes, fractional integration, and their applications in economics and finance. Some of the definitions of long memory are reviewed, together with previous work in other disciplines. Section 3 describes the popu ..."
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Cited by 377 (0 self)
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This paper provides a survey and review of the major econometric work on long memory processes, fractional integration, and their applications in economics and finance. Some of the definitions of long memory are reviewed, together with previous work in other disciplines. Section 3 describes the population characteristics of various long memory processes in the mean, including ARFIMA. Section 4 is concerned with estimation and examines emiparametric procedures in both *he frequency and time domain, and also the properties of various regression based and maximum likelihood techniques. Long memory volatility processes are discussed in Section 5, while Section 6 discusses applications in economics and finance. The paper also has a concluding section.
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 approximatel ..."
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Cited by 333 (29 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 243 (22 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 214 (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 165 (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...
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 164 (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)
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 130 (9 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.
Long Memory and Persistence in Aggregate Output
 Journal of Monetary Economics
, 1989
"... We examine persistence in U.S. aggregate output by estimating fractionally integrated ARIMA models. Thex models provide better lowfrequency approximations to the Wold representation than previous stochastic specifications. and earlier resulrs on the importance of a permanent component emerge as spe ..."
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Cited by 129 (8 self)
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We examine persistence in U.S. aggregate output by estimating fractionally integrated ARIMA models. Thex models provide better lowfrequency approximations to the Wold representation than previous stochastic specifications. and earlier resulrs on the importance of a permanent component emerge as special cases. We find evidence of long memory. whch induces persistence. though t h s long memory need not be associated uith a unit root. Our point estimates indicate that macroeconomic shocks. while persistent, are distinctly less persistent than many earlier studies suggest: however. confidence intervals associated with the longrun responx are quite wide. 1.