Difference between revisions of "Published Papers"

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{{pdf|ACFM2001.pdf|Multi-Step estimation of Multivariate GARCH models}}, ''Proceedings of the
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===Refereed===
International ICSC Symposium: Advanced Computing in Financial Markets'', June 2001.<br />
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* {{pdf|ordering2003.pdf|An Ordering Experiment}}, ''Journal of Economic Behavior and Organization'' with A. Norman, et al.,February 2003, pp. 249-262.
Estimation of large time varying covariance matrices has proven difficult since the original multivariate volatility models were introduced. In this paper, we develop the empirical properties of a new class of MV-GARCH models capable of estimating large time-varying covariance matrices. We show that the problem of multivariate conditional variance estimation can be reduced to a series of univariate GARCH processes plus an additional conditional correlation estimator. We use the model to estimate a conditional covariance of up to 100 assets using S&amp;P 500 Sector Indies and Dow Jones Industrial Average stocks. This new estimator demonstrates very strong performance especially considering the estimators easy of implementation.
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* {{pdf|complexity2003.pdf|On the Computational Complexity of Consumer Decision Rules}}, 2003, with A. Norman, et. al., ''Computational  Economics'', Vol 23, March 2004, pp 173-192.<br />
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* {{pdf|CES_JFeC.pdf|Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns}}, 2006, with L. Cappiello and R. Engle, ''Journal of Financial Econometrics'' Vol. 4, pp. 537-572.
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* {{pdf|Evaluating_Patton_Sheppard_2009.pdf|Evaluating Volatility and Correlation Forecasts}} (joint with Andrew J. Patton), Handbook of Financial Time Series, 2009, (T.G. Andersen, R.A. Davis, J.-P. Kreiss and T. Mikosch (eds.)), Springer Verlag
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* {{pdf|Panel_Garch_Pakel_Shephard_Sheppard_2011.pdf|Nuisance parameters, composite likelihoods and a panel of GARCH models}} Statistica Sinica, 2011, 21, 307--329.  
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* {{pdf|HEAVY_Shephard_Sheppard_2011.pdf|Realising the future: forecasting with high-frequency-based volatility (HEAVY) models}} (joint with Neil Shephard), Journal of Applied Econometrics, 2010, 25:2, 197--231.
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* {{pdf|HEAVY_Noureldin_Shephard_Sheppard_2011.pdf|Multivariate high-frequency-based volatility (HEAVY) models}} (joint with D. Noureldin and N. Shephard), Journal of Applied Econometrics, 2011, Preprint phase.
  
  
{{pdf|ordering2003.pdf|An Ordering Experiment}}, ''Journal of Economic Behavior and Organization'' with A. Norman, et
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===Other===
al.,February 2003, pp. 249-262.<br />
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* {{pdf|ACFM2001.pdf|Multi-Step estimation of Multivariate GARCH models}}, ''Proceedings of the International ICSC Symposium: Advanced Computing in Financial Markets'', June 2001.
Binary comparison operators form the basis of consumer set theory. If humans could only perform binary comparisons, the most efficient procedure a human might employ to make a complete preference ordering of n items would be a n log<sub>2</sub>n algorithm. But, if humans are capable of assigning each item an ordinal utility value, they are capable of implementing a more efficient linear algorithm. In this paper, we consider six incentive systems for ordering three different sets of objects: pens, notebooks, and Hot Wheels. All experimental evidence indicates that humans are capable of implementing a linear algorithm, for small sets.</td>
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* {{pdf|Combination_Patton_Sheppard_2009.pdf|Optimal Combinations of Realised Volatility Estimators}} (joint with Andrew J. Patton) International Journal of Forecasting, 2009, 25:2, 218--238.
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* {{pdf|HEAVY_Noureldin_Shephard_Sheppard_2011.pdf|Forecasting High Dimensional Covariance Matrices}} Handbook of Volatility Models and Their Applications, 2012 (Bauwens, L. Hafner, C. and Laurent, S. eds.) Wiley. Preprint phase.
  
  
{{pdf|complexity2003.pdf|On the Computational Complexity of Consumer Decision Rules}}, 2003, with A. Norman, et. al., ''Computational  Economics'', Vol 23, March 2004, pp 173-192.<br />
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[[Category:Research]]
A consumer entering a new bookstore can face more than 250,000 alternatives. The efficiency of compensatory and noncompensatory decision rules for finding a preferred item depends on the efficiency of their associated information operators. At best, item-by-item information operators lead to linear computational complexity; set information operators, on the other hand, can lead to constant complexity. We perform an experiment demonstrating that subjects are approximately rational in selecting between sublinear and linear rules. Many markets are organized by attributes that enable consumers to employ a set-selection-by-aspect rule using set information operations. In cyberspace decision rules are encoded as decision aids.
 

Latest revision as of 09:05, 14 October 2011

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