- Ait-Sahalia, Y. and Lo, A.W. 1998. Nonparametric Estimation of State-Price Densities Implicit in Financial Asset Prices. Journal of Finance 53, 499-547.
Paper not yet in RePEc: Add citation now
Amisano, G. and Giacomini, R. 2007. Comparing Density Forecasts via Weighted Likelihood Ratio Tests. Journal of Business and Economic Statistics 25, 177-190.
Andersens, T.G., Bollerslev, T., Diebold, F.X. and Labys, P. 2003. Modeling and Forecasting Realized Volatility. Econometrica 71, 579-625.
Andersens, T.G., Bollerslev,T., Diebold, F.X. and Ebens, H. 2001. The Distribution of Realized Stock Return Volatility. Journal of Financial Econometrics 61, 43-76.
- Anderson, B.D.O. and Moore, J.B. 1979. Optimal Filtering. Prentice.
Paper not yet in RePEc: Add citation now
- Arulampalam, M. S., Maskell, S., Gordon N. and Clapp, T. 2002. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing 50 (2), 174-188.
Paper not yet in RePEc: Add citation now
- Au, C. and Tam, J. 1999. Transforming Variables Using the Dirac Generalized Function. The American Statistician 53, 270-272.
Paper not yet in RePEc: Add citation now
Barndor-Nielsen, O.E. and Shephard, N. 2002. Econometric Analysis of Realized Volatility and Its Use in Estimating Stochastic Volatility Models. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 64, 253-280.
Bauwens, L, Giot, P, Grammig, J and Veredas, D. 2004. A Comparison of Financial Duration Models via Density Forecasts. International Journal of Forecasting 20, 589-609.
Bauwens, L. and Veredas, D. 2004. The Stochastic Conditional Duration Model: A Latent Variable Model for the Analysis of Financial Durations. Journal of Econometrics 199, 381-412.
Berg, J.E., Geweke, J. and Rietz, T.A. 2010. Memoirs of an Indierent Trader: Estimating Forecast Distributions from Prediction Markets, Quantitative Economics 1, 163-186.
Berkowitz, J. 2001. Testing Density Forecasts with Applications to Risk Management, Journal of Business and Economic Statistics 19, 465â474.
Boero, G., Smith, J. and Wallis, K.F. 2011. Scoring Rules and Survey Density Forecasts. International Journal of Forecasting 27, 379-393.
Broadie, M., Chernov, M. and Johannes, M. 2007. Model Speciâcation and Risk Premia: Evidence from Futures Options. The Journal of Finance LXII: 1453 - 1490.
Brownless, C.T. and Gallo, G.M. 2006. Financial Econometrics Analysis at Ultra-High Frequency: Data Handling Concerns, Computational Statistics and Data Analysis 51, 22322245
Bu, R. and McCabe, B.P.M. 2008. Model Selection, Estimation and Forecasting in INAR(p) Models: A Likelihood based Markov Chain Approach, International Journal of Forecasting 24, 151-162.
- Caron, F, Davy, M, Doucet, A and Duâos, E. 2008. Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures. IEEE Transactions on Signal Processing 56, 7184.
Paper not yet in RePEc: Add citation now
Clements, A., Hurn, S. and White, S. 2006. Estimating Stocahstic Volatility Models Using a Discrete Non-Linear Filter. Working Paper No. 3. National Centre for Econometric Research.
Czado, C., Gneiting, T. and Held, L. 2009. Predictive Model Assessment for Count Data. In press, Biometrics.
- Dawid, A.P. 1984. Present Position and Potential Developments: some personal views: statistical theory: the prequential approach. Journal of the Royal Statistical Society, Series A 147, No. 2, 278-292.
Paper not yet in RePEc: Add citation now
De Rossi, G. and Harvey, A. 2009. Quantiles, expectiles and splines. Journal of Econometrics 152, 179-85.
Diebold, F.X., Gunther, T.A. and Tay, A.S. 1998. Evaluating Density Forecasts with Applications to Financial Risk Management. International Economic Review 39, 863-883.
Durham, G.B. 2007. Monte Carlo Methods for Estimating, Smoothing and Filtering Oneand Two-Factor Stochastic Volatility Models. Journal of Econometrics 133, 273-305.
Durham, G.B. 2007. SV Mixture Models with Application to S&P 500 Index Returns. Journal of Financial Econometrics 85, 822-856.
Engle, R.F. and Gonzalez-Rivera, G. 1991. Semiparametric ARCH Models. Journal of Business and Economic Statistics 9, 345-359.
Eraker, B., Johannes, M.S. and Polson, N.G. 2003. The Impact of Jumps in Returns and Volatility, Journal of Finance 53, 1269â1300.
Fernandez, C. and Steel, M.F.J. 1998. On Bayesian Modelling of Fat Tails and Skewness, Journal of the American Statistical Association 93, 359-371
Freeland, R and McCabe, B. 2004. Forecasting Discrete Valued Low Count Time Series. International Journal of Forecasting 20, 427-434.
Geweke, J. and Amisano, G. 2010. Comparing and Evaluating Bayesian Predictive Distributions of Asset Returns, International Journal of Forecasting (Special Issue on Applied Bayesian Forecasting in Economics) 26, 216-230.
Giacomini, R. and Komujer, I. 2005. Evaluation and Combination of Conditional Quantile Forecasts. JBES 23, 416-431.
Gneiting, T. 2008. Editorial: Probabilistic Forecasting, Journal of the Royal Statistical Society (A) 171, 319-321.
Gneiting, T. and Raftery, A.E. 2007. Strictly Proper Scoring Rules, Prediction, and Estimation, Journal of the American Statistical Association 102, 359-378.
Gneiting, T., Balabdaoui, F. and Raftery, A.E. 2007. Probabilistic Forecasts, Calibration and Sharpness. Journal of Royal Statistical Society 69, 243-268.
- Hassani, S. 2009. Mathematical Methods for Students of Physics and Related Fields (2nd Edition).
Paper not yet in RePEc: Add citation now
Jensen, M.J. and Maheu, J.M. 2010. Bayesian Semiparametric Stochastic Volatility Modeling. Journal of Econometrics 157, 306-316.
- Johnson, N.L., Kotz, S. and Balakrishnan, N. 1994. Distributions in Statistics: Continuous Univariate Distributions, Vol. 2. Wiley, New York
Paper not yet in RePEc: Add citation now
- Khuri, A.I. 2004. Application of Diracâs Delta Function in Statistics. International Journal of Mathematical Eduction in Science and Technology 35, 185-195.
Paper not yet in RePEc: Add citation now
- Kitagawa, G. 1987. Non-Gaussian State Space Modeling of Nonstationary Time Series. Journal of the American Statistical Association 76, 1032-1064.
Paper not yet in RePEc: Add citation now
Kitagawa, G. 1994. The Two-Filter Formula for Smoothing and an Implementation of the Gaussian-sum Smoother. Annals of the Institute of Statistical Mathematics 46, 605-623.
Lim, G, Martin, G and Martin, V, 2005. Parametric Pricing of Higher Order Moments in S&P500 Options. Journal of Applied Econometrics 20, 377-404.
Martin, G, Reidy, A and Wright, J. 2009. Does the Option Market Produce Superior Forecasts of Noise-corrected Volatility Measures. Journal of Applied Econometrics 24, 77104.
McCabe, B and Martin, G. 2005. Bayesian Predictions of Low Count Time Series. International Journal of Forecasting 21, 315-330.
McCabe, B., Martin, G.M. and Harris, D. 2011. E cient Probabilistic Forecasts for Counts. Forthcoming, Journal of the Royal Statistical Society, Series B 73, 253-272.
Monteiro, A.A. 2010. A Semiparametric State Space Model. Working paper, Statistics and Econometrics Series Universidad Carlos III de Madrid, Spain. Available at http://earchivo. uc3m.es/bitstream/10016/9247/1/ws103418.pdf.
Pascual, L., Romo, J. and Ruiz, E. 2001. Eects of parameter estimation on prediction densities: a bootstrap approach. International Journal of Forecasting 17, 83-103.
Pascual, L., Romo, J. and Ruiz, E. 2005. Bootstrap prediction intervals for powertransformed time series. International Journal of Forecasting 21, 219-235.
Rodriguez, A. and Ruiz, E. 2009. Bootstrap Prediction Intervals in State-Space Models. Journal of Time Series Analysis 30, 167-178.
- Rosenblatt, R.F. 1952. Remarks on a Multivariate Transformation. Annals of Mathematical Statistics 23, 470-472.
Paper not yet in RePEc: Add citation now
- Scott, D.W., Tapia, R.A. and Thompson, J.R. 1980. Nonparametric Probability Density Estimation by Discrete Maximum Penalized-Likelihood Criteria. Annals of Statistics 8, 820-832.
Paper not yet in RePEc: Add citation now
- Sorenson H.W. and Alspach D.L. 1971. Recursive Bayesian Estimation Using Gaussian Sums. Autmatica 7, 465-479
Paper not yet in RePEc: Add citation now
Strickland, C.M., Forbes, C.S. and Martin, G.M. 2006. Bayesian Analysis of the Stochastic Conditional Duration Model. Computational Statistics and Data Analysis (Special Issue on Statistical Signal Extraction and Filtering) 50, 2247-2267.
Tay, A and Wallis, K. 2000. Density forecasting: A Survey. Journal of Forecasting 19, 235-254.
Yau, C., Papaspiliopoulos, O., Roberts, G.O. and Holmes, C. 2011. Bayesian nonparametric hidden Markov models with applications in genomics. Journal of the Royal Statistical Society, Series B 73, 37-57.