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1. Dobrislav Dobrev: Federal Reserve Board of Governors, [email protected] Pawel Szerszen: Federal Reserve Board of Governors, [email protected] are grateful to Torben Andersen, Federico Bandi, Luca Benzoni, Neil Ericsson, Michael Gordy, Erik Hjalmarsson, Michael Johannes, Matthew Pritsker, and Viktor Todorov for helpful discussions and comments. We also thank conference participants at the 2010 International Risk Management Conference, the 30th International Symposium on Forecasting, the 16th International Conference on Computing in Economics and Finance, as well as seminar participants at the Federal Reserve Board, Johns Hopkins University, the Office of the Comptroller of the Currency, and University of Maryland for their feedback. Excellent research assistance has been provided by Patrick Mason as well as Erica Reisman and Raymond Zhong. Any errors or omissions are our sole responsibility. The views in this paper are solely those of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System. Return to text 2.See for example, Chernov and Ghysels (2000), [#!Pan-JumpRiskPremia::2002!#], and Eraker (2004) among others.Return to text 3.Recent surveys of the realized volatility literature include Andersen, Bollserslev, and Diebold (2009), Bandi and Russell (2007), Barndorff-Nielsen and Shephard (2007), McAleer and Medeiros (2008).Return to text 4.Thorough empirical evidence pointing more broadly towards the value of realized volatility for modeling equity returns can be found in Andersen, Bollerslev, Diebold, and Ebens (2001).Return to text 5.Results in the same spirit have been obtained also in studies based solely on daily returns or in combination with options data such as Broadie, Chernov, and Johannes (2007). Other non-parametric studies based on high-frequency data include Andersen, Bollerslev, and Dobrev (2007), Bandi and Reno (2009).Return to text 6.Cf. Andersen, Benzoni, and Lund (2002) and Chernov, Gallant, Ghysels, and Tauchen (2003).Return to text 7.As such, our results add to the growing body of evidence showing the economic value of high-frequency realized volatility measures in finance applications, e.g. Fleming, Kirby, and Ostdiek (2003) among others.Return to text 8.The rate of convergence is typically square-root. It is slower for high-frequency volatility measures that are robust also to market microstructure noise, empirically found to matter at sample frequencies higher than a few minutes.Return to text 9.Our subsequent analysis remains valid to the extent that the utilized asymptotic results are unaffected by jumps of possibly infinite activity (but still finite variation).Return to text 10.For recent surveys of the realized volatility literature see, e.g., Andersen, Bollserslev, and Diebold (2009), Bandi and Russell (2007), Barndorff-Nielsen and Shephard (2007), McAleer and Medeiros (2008).Return to text 11.Other approaches that involve potentially delicate threshold or bandwidth choices include the truncated RV of Mancini (2006) and At-Sahalia and Jacod (2007), the truncated bipower variation of Corsi, Pirino, and Ren (2008), as well as the quantile RV estimator of Christensen, Oomen, and Podolskij (2008).Return to text 12.The scaling factor is defined as , . Return to text 13.The asymptotic variance factor for TV is 3.06 as opposed to 2.96 for MedRV. Also by design, MedRV is somewhat more robust than TV not only to jumps but also to the occurrence of "zero" returns in finite samples.Return to text 14.Without loss of generality, in our empirical analysis we focus on the popular realized quad-power quarticity estimator QQ of Barndorff-Nielsen and Shephard (2004) as well as the slightly more efficient (and robust to both jumps and zero returns) median realized quarticity estimator MedRQMmed , of Andersen, Dobrev, and Schaumburg (2009).Return to text 15.Volatility measures obtained at higher frequencies can incur biases due to market imperfections such as bid-ask bounce effects, stale quotes, price discreteness, and intraday patterns.Return to text 16.The extra robustness comes at the cost of lower convergence rate that can be easily accommodated by our generic volatility measurement equation (9) by changing the power of M accordingly.Return to text 17.As usual, we restrict to satisfy standard stationarity conditions for .Return to text 18.As noted by Eraker, Johannes, and Polson (2003), the discretization bias for daily data is not significant.Return to text 19.This is a standard correction of realized volatility measures as given in more detail, for example, by Hansen and Lunde (2005).Return to text 20.One-factor models can be viewed as a special case by restricting for all , omitting the parameters for the f factor and imposing the constraint in the instantaneous correlation matrix.Return to text 21.In particular, in our analysis we focus on the MedRV estimator of Andersen, Dobrev, and Schaumburg (2009) and the tri-power variation measure of Barndorff-Nielsen, Shephard, and Winkel (2006). We report results only for the former as the results obtained for the latter are in the same spirit.Return to text 22.In the one-factor model the case of perfectly observed volatility can be viewed as the limiting case of our volatility measurement equation when the intraday sample frequency grows to infinity. In the two-factor model, though, the volatility measurement equation provides information only about the sum of the two volatility factors without separating them as in the infeasible case of full observability.Return to text 23.In this sense, our results extend those obtained by Alizadeh, Brandt, and Diebold (2002) in a considerably more restrictive range-based analysis of a model without leverage effects.Return to text 24.The data for S&P 500 is provided by Tick Data, while the data for Google is from NYSE TAQ.Return to text 25.Comprehensive surveys of the literature on SV models and estimation include Andersen, Bollserslev, and Diebold (2009), Ghysels, Harvey and Renault (1996), Shephard (1996), Taylor (1994), among others.Return to text This version is optimized for use by screen readers. Descriptions for all mathematical expressions are provided in LaTex format. A printable pdf version is available. Return to text Home | Economic research and data | Publications and education resources Accessibility | Contact us Last update: October 12, 2010

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Gypsum Production Line For Desulfurization Currently the world's most popular desulfurization process is limestonegypsum method But there is one big problem in the past, the fineness of the powder should be reach D90=325 mesh To meet the finess, currently, more and more producers are recommended to use vertical mill or Supperpressure

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The need to physico-chemically upgrade peat and low rank coals has been long recognised. Richard (1984) reports that over 2000 years ago the natives of Delmenhorst produced an upgraded fuel by slurrying, kneading and drying peat. He went on to describe developments in peat maceration/ drying and densification processes throughout the centuries. Much of this peat technology has been successfully transferred to the brown coal industry. For example, the dryers, screens and briquette extrusion presses specially developed for peat were ultimately adopted by the German brown coal industry (Baragwanath, 1953 and Richard, 1984). This technology is used today in Victoria to produce about 1 million tonnes of briquettes per annum from Yallourn brown coal. Briquettes and the briquetting process have been the subject of a number of comprehensive treatise (Baragwanath and Finlayson, 1947, Herman, 1952 and Baragwanath, 1953), and are not discussed here

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ABB has delivered some of the world's largest GMDs: 22 MW GMD for 28' ball mill 28 MW GMD for a 40' SAG mill 28 MW GMD for 42' SAG mill Whether it is size or an installation at the highest altitude ( m.a.s.l.), ABB's GMDs are well positioned to

1. Dobrislav Dobrev: Federal Reserve Board of Governors, [email protected] Pawel Szerszen: Federal Reserve Board of Governors, [email protected] are grateful to Torben Andersen, Federico Bandi, Luca Benzoni, Neil Ericsson, Michael Gordy, Erik Hjalmarsson, Michael Johannes, Matthew Pritsker, and Viktor Todorov for helpful discussions and comments. We also thank conference participants at the 2010 International Risk Management Conference, the 30th International Symposium on Forecasting, the 16th International Conference on Computing in Economics and Finance, as well as seminar participants at the Federal Reserve Board, Johns Hopkins University, the Office of the Comptroller of the Currency, and University of Maryland for their feedback. Excellent research assistance has been provided by Patrick Mason as well as Erica Reisman and Raymond Zhong. Any errors or omissions are our sole responsibility. The views in this paper are solely those of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System. Return to text 2.See for example, Chernov and Ghysels (2000), [#!Pan-JumpRiskPremia::2002!#], and Eraker (2004) among others.Return to text 3.Recent surveys of the realized volatility literature include Andersen, Bollserslev, and Diebold (2009), Bandi and Russell (2007), Barndorff-Nielsen and Shephard (2007), McAleer and Medeiros (2008).Return to text 4.Thorough empirical evidence pointing more broadly towards the value of realized volatility for modeling equity returns can be found in Andersen, Bollerslev, Diebold, and Ebens (2001).Return to text 5.Results in the same spirit have been obtained also in studies based solely on daily returns or in combination with options data such as Broadie, Chernov, and Johannes (2007). Other non-parametric studies based on high-frequency data include Andersen, Bollerslev, and Dobrev (2007), Bandi and Reno (2009).Return to text 6.Cf. Andersen, Benzoni, and Lund (2002) and Chernov, Gallant, Ghysels, and Tauchen (2003).Return to text 7.As such, our results add to the growing body of evidence showing the economic value of high-frequency realized volatility measures in finance applications, e.g. Fleming, Kirby, and Ostdiek (2003) among others.Return to text 8.The rate of convergence is typically square-root. It is slower for high-frequency volatility measures that are robust also to market microstructure noise, empirically found to matter at sample frequencies higher than a few minutes.Return to text 9.Our subsequent analysis remains valid to the extent that the utilized asymptotic results are unaffected by jumps of possibly infinite activity (but still finite variation).Return to text 10.For recent surveys of the realized volatility literature see, e.g., Andersen, Bollserslev, and Diebold (2009), Bandi and Russell (2007), Barndorff-Nielsen and Shephard (2007), McAleer and Medeiros (2008).Return to text 11.Other approaches that involve potentially delicate threshold or bandwidth choices include the truncated RV of Mancini (2006) and At-Sahalia and Jacod (2007), the truncated bipower variation of Corsi, Pirino, and Ren (2008), as well as the quantile RV estimator of Christensen, Oomen, and Podolskij (2008).Return to text 12.The scaling factor is defined as , . Return to text 13.The asymptotic variance factor for TV is 3.06 as opposed to 2.96 for MedRV. Also by design, MedRV is somewhat more robust than TV not only to jumps but also to the occurrence of "zero" returns in finite samples.Return to text 14.Without loss of generality, in our empirical analysis we focus on the popular realized quad-power quarticity estimator QQ of Barndorff-Nielsen and Shephard (2004) as well as the slightly more efficient (and robust to both jumps and zero returns) median realized quarticity estimator MedRQMmed , of Andersen, Dobrev, and Schaumburg (2009).Return to text 15.Volatility measures obtained at higher frequencies can incur biases due to market imperfections such as bid-ask bounce effects, stale quotes, price discreteness, and intraday patterns.Return to text 16.The extra robustness comes at the cost of lower convergence rate that can be easily accommodated by our generic volatility measurement equation (9) by changing the power of M accordingly.Return to text 17.As usual, we restrict to satisfy standard stationarity conditions for .Return to text 18.As noted by Eraker, Johannes, and Polson (2003), the discretization bias for daily data is not significant.Return to text 19.This is a standard correction of realized volatility measures as given in more detail, for example, by Hansen and Lunde (2005).Return to text 20.One-factor models can be viewed as a special case by restricting for all , omitting the parameters for the f factor and imposing the constraint in the instantaneous correlation matrix.Return to text 21.In particular, in our analysis we focus on the MedRV estimator of Andersen, Dobrev, and Schaumburg (2009) and the tri-power variation measure of Barndorff-Nielsen, Shephard, and Winkel (2006). We report results only for the former as the results obtained for the latter are in the same spirit.Return to text 22.In the one-factor model the case of perfectly observed volatility can be viewed as the limiting case of our volatility measurement equation when the intraday sample frequency grows to infinity. In the two-factor model, though, the volatility measurement equation provides information only about the sum of the two volatility factors without separating them as in the infeasible case of full observability.Return to text 23.In this sense, our results extend those obtained by Alizadeh, Brandt, and Diebold (2002) in a considerably more restrictive range-based analysis of a model without leverage effects.Return to text 24.The data for S&P 500 is provided by Tick Data, while the data for Google is from NYSE TAQ.Return to text 25.Comprehensive surveys of the literature on SV models and estimation include Andersen, Bollserslev, and Diebold (2009), Ghysels, Harvey and Renault (1996), Shephard (1996), Taylor (1994), among others.Return to text This version is optimized for use by screen readers. Descriptions for all mathematical expressions are provided in LaTex format. A printable pdf version is available. Return to text Home | Economic research and data | Publications and education resources Accessibility | Contact us Last update: October 12, 2010

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Matire premire pour la presse peut tre briquette de charbon, de la mtallurgie, la poudre de scories, de poudre, la poudre de magnsium mtal etc Selon diffrentes matires, diffrentes pressions de compactage sont ncessaires pour les presses briquette. Il a de nombreuses applications dans les grands industriels tels que la mtallurgie domaine. La mine de poudre de couleur mtal, noir mtal doivent tre compact en balle pour plus de fondre, comme la salet, l'oxydation de peau, la poussire collecte, le mtal en poudre fine, mine de matires premires en poudre, de manganse en poudre, poudre de silico-manganse et autres matriaux en poudre

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Matire premire pour la presse peut tre briquette de charbon, de la mtallurgie, la poudre de scories, de poudre, la poudre de magnsium mtal etc Selon diffrentes matires, diffrentes pressions de compactage sont ncessaires pour les presses briquette. Il a de nombreuses applications dans les grands industriels tels que la mtallurgie domaine. La mine de poudre de couleur mtal, noir mtal doivent tre compact en balle pour plus de fondre, comme la salet, l'oxydation de peau, la poussire collecte, le mtal en poudre fine, mine de matires premires en poudre, de manganse en poudre, poudre de silico-manganse et autres matriaux en poudre

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