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212
Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility
 REVIEW OF ECONOMICS AND STATISTICS, FORTHCOMING
, 2006
"... A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from highfrequency returns coupled with simple modeling procedures. Building on recent theoretical results in BarndorffNi ..."
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Cited by 154 (10 self)
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A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from highfrequency returns coupled with simple modeling procedures. Building on recent theoretical results in BarndorffNielsen and Shephard (2004a, 2005) for related bipower variation measures, the present paper provides a practical and robust framework for nonparametrically measuring the jump component in asset return volatility. In an application to the DM/ $ exchange rate, the S&P500 market index, and the 30year U.S. Treasury bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from nonjump movements in a simple but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the nonjump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling, forecasting, and pricing the continuous and jump components of the total return variation process.
A central limit theorem for realised power and bipower variations of continuous semimartingales
 In
, 2006
"... Summary. Consider a semimartingale of the form Yt = Y0 + ∫ t 0 asds + ∫ t σs − dWs, 0 where a is a locally bounded predictable process and σ (the “volatility”) is an adapted right–continuous process with left limits and W is a Brownian motion. We consider the realised bipower variation process V (Y; ..."
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Cited by 91 (41 self)
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Summary. Consider a semimartingale of the form Yt = Y0 + ∫ t 0 asds + ∫ t σs − dWs, 0 where a is a locally bounded predictable process and σ (the “volatility”) is an adapted right–continuous process with left limits and W is a Brownian motion. We consider the realised bipower variation process V (Y; r, s) n t = n r+s
Jumps in financial markets: A new nonparametric test and jump clustering
, 2007
"... This article introduces a new nonparametric test to detect jump arrival times and realized jump sizes in asset prices up to the intraday level. We demonstrate that the likelihood of misclassification of jumps becomes negligible when we use highfrequency returns. Using our test, we examine jump dyn ..."
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Cited by 64 (3 self)
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This article introduces a new nonparametric test to detect jump arrival times and realized jump sizes in asset prices up to the intraday level. We demonstrate that the likelihood of misclassification of jumps becomes negligible when we use highfrequency returns. Using our test, we examine jump dynamics and their distributions in the U.S. equity markets. The results show that individual stock jumps are associated with prescheduled earnings announcements and other companyspecific news events. Additionally, S&P 500 Index jumps are associated with general market news announcements. This suggests different pricing models for individual equity options versus index options. (JEL G12, G22, G14) Financial markets sometimes generate significant discontinuities, socalled jumps, in financial variables. A number of recent empirical and theoretical studies proved the existence of jumps and their substantial impact on financial management, from portfolio and risk management to option and bond pricing
Estimation of volatility functionals in the simultaneous presence of microstructure noise and jumps
, 2009
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Variation, jumps, market frictions and high frequency data in financial econometrics
, 2005
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Noarbitrage semimartingale restrictions for continuoustime volatility models subject to leveral effects, jumps and i.i.d. noise: Theory and testable distributional implications
 JOURNAL OF ECONOMETRICS
, 2007
"... We develop a sequential procedure to test the adequacy of jumpdiffusion models for return distributions. We rely on intraday data and nonparametric volatility measures, along with a new jump detection technique and appropriate conditional moment tests, for assessing the import of jumps and levera ..."
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Cited by 52 (10 self)
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We develop a sequential procedure to test the adequacy of jumpdiffusion models for return distributions. We rely on intraday data and nonparametric volatility measures, along with a new jump detection technique and appropriate conditional moment tests, for assessing the import of jumps and leverage effects. A novel robusttojumps approach is utilized to alleviate microstructure frictions for realized volatility estimation. Size and power of the procedure are explored through Monte Carlo methods. Our empirical findings support the jumpdiffusive representation for S&P500 futures returns but reveal
Multiscale jump and volatility analysis for highfrequency financial data
 Journal of the American Statistical Association
, 2007
"... The wide availability of highfrequency data for many financial instruments stimulates an upsurge interest in statistical research on the estimation of volatility. Jumpdiffusion processes observed with market microstructure noise are frequently used to model highfrequency financial data. Yet, exis ..."
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Cited by 49 (5 self)
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The wide availability of highfrequency data for many financial instruments stimulates an upsurge interest in statistical research on the estimation of volatility. Jumpdiffusion processes observed with market microstructure noise are frequently used to model highfrequency financial data. Yet, existing methods are developed for either noisy data from a continuous diffusion price model or data from a jumpdiffusion price model without noise. We propose methods to cope with both jumps in the price and market microstructure noise in the observed data. They allow us to estimate both integrated volatility and jump variation from the data sampled from jumpdiffusion price processes, contaminated with the market microstructure noise. Our approach is to first remove
Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility
, 2003
"... A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from highfrequency returns coupled with relatively simple reduced form time series modeling procedures. Building on recent th ..."
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Cited by 41 (4 self)
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A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from highfrequency returns coupled with relatively simple reduced form time series modeling procedures. Building on recent theoretical results from BarndorffNielsen and Shephard (2003c) for related bipower variation measures involving the sum of highfrequency absolute returns, the present paper provides a practical framework for nonparametrically measuring the jump component in the realized volatility measurements. Exploiting these ideas for a decade of highfrequency fiveminute returns for the DM/ $ exchange rate, the S&P500 aggregate market index, and the 30year U.S. Treasury Bond, we find the jump components to be distinctly less persistent than the contribution to the overall return variability originating from the continuous sample path component of the price process. Explicitly including the jump measure as an additional explanatory variable in an easytoimplement reduced form model for the realized volatilities results in highly significant jump coefficient estimates at the daily, weekly and quarterly forecasts horizons. As such, our results hold promise for improved financial asset allocation, risk management, and derivatives pricing, by separate modeling, forecasting and pricing of the continuous and jump components of the total return variability.
Optimal filtering of jump diffusions: extracting latent states from asset prices
, 2007
"... This paper provides a methodology for computing optimal filtering distributions in discretely observed continuoustime jumpdiffusion models. Although it has received little attention, the filtering distribution is useful for estimating latent states, forecasting volatility and returns, computing mo ..."
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Cited by 38 (7 self)
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This paper provides a methodology for computing optimal filtering distributions in discretely observed continuoustime jumpdiffusion models. Although it has received little attention, the filtering distribution is useful for estimating latent states, forecasting volatility and returns, computing model diagnostics such as likelihood ratios, and parameter estimation. Our approach combines timediscretization schemes with Monte Carlo methods to compute the optimal filtering distribution. Our approach is very general, applying in multivariate jumpdiffusion models with nonlinear characteristics and even nonanalytic observation equations, such as those that arise when option prices are available. We provide a detailed analysis of the performance of the filter, and analyze four applications: disentangling jumps from stochastic volatility, forecasting realized volatility, likelihood based model comparison, and filtering using both option prices and underlying returns.