academic publications

Interests

Financial economics and econometrics, asset pricing, market microstructures and manipulation, forecasting methods, high dimensional estimation, machine learning. 

Papers in Refereed Journals (the up-to-date list can be found here) 

1. Molten Lava Meets Market Languor (with Steve Satchell), Quantitative Finance 2 (2002), (comments section) 405.  link to journal

2. The Bernstein Copula Function as a Model and Approximation to Multivariate Distributions (with Steve Satchell). Econometric Theory 20 (2004), 535-562. pdf

3. Calculating Hedge Fund Risk: The Draw Down and the Maximum Draw Down (with Steve Satchell). Applied Mathematical Finance 11 (2004), 259-282. pdf

4. New Test Statistics for Market Timing with Applications to Emerging Markets (with Steve Satchell). The European Journal of Finance 11 (2005), 419-443. link to journal

5. Distance between Non-Identically Weakly Dependent Random Vectors and Gaussian Random Vectors under the Bounded Lipschitz Metric. Statistics and Probability Letters 75 (2005), 158-168. pdf

6. Changing Correlation and Equity Portfolio Diversification Failure for Linear Factor Models during Market Declines (with Steve Satchell). Applied Mathematical Finance 14 (2007), 227-242.  pdf

7. Weak Convergence of Laws on RK with Common Marginals. Journal of Theoretical Probability 20 (2007), 371-380. pdf

8. Online Forecast Combinations of Distributions: Worst Case Bounds. Journal of Econometrics 141 (2007), 621-651. pdf 

9. Nonparametric Estimation of Distributions with Given Marginals via Bernstein-Kantorovich Polynomials: L1 and Pointwise Convergence Theory. Journal of Multivariate Analysis 98 (2007), 1376-1390. pdf

10. Sample Covariance Shrinkage for High Dimensional Dependent Data. Journal of Multivariate Analysis 99 (2008), 949-967. pdf

11. Strong Law of Large Numbers for Pairwise Positive Quadrant Dependent Random Variables. Statistical Inference for Stochastic Processes 12 (2009), 55-64. pdf

12. Maximal Inequalities for U-Processes of Strongly Mixing Random Variables. Probability and Mathematical Statistics 29 (2009), 155-167. pdf

13. Consistent Estimation of a General Nonparametric Regression Function in Time Series (with O.B. Linton). Journal of Econometrics (Peter Robinson's Festschrift) 152 (2009), 70-78.  pdf

14. Forecasting and Prequential Validation for Time Varying Meta-Elliptical Distributions (with A. Nikandrova). Journal of Time Series Econometrics 1 (2009), Iss. 2, Article 1. pdf

15. Nearest Neighbor Conditional Estimation for Harris Recurrent Markov Chains. Journal of Multivariate Analysis 100 (2009), 2224-2236. pdf

16. Bootstrap Model Selection for Possibly Dependent and Heterogeneous Data. Annals of the Institute of Statistical Mathematics 62, (2010), 515-546. pdf

17. Recursive Forecast Combination for Dependent Heterogeneous Data. Econometric Theory 26 (2010), 598-631. pdf

18. Universality of Bayesian Predictions (with discussion). Bayesian Analysis 7 (2012), Number 1, 1-36, 45-46 (rejoinder). pdf

19. Weak Conditions for Nonparametric Density Shrinkage. Journal of Multivariate Analysis 115 (2013), 285-300. pdf 

20. Conditional Estimation for Dependent Functional Data (with Heather Battey). Journal of Multivariate Analysis 120 (2013), 1-17. pdf

21. A Recursive Algorithm for Mixture of Densities Estimation. IEEE Transactions on Information Theory 59 (2013), 6893-6906. pdf

22. An open Problem on Strongly Consistent Learning of the Best Prediction for Gaussian Processes (with Laci Györfi). Topics in Nonparametric Statistics, Proceedings of the First Conference of the International Society for Nonparametric Statistics (2014). pdf 

23. Semiparametric Estimation of a Class of Generalized Linear Models without Smoothing. Journal of Multivariate Analysis 130 (2014), 141-154. pdf

24. A Nonparametric Estimator of the Covariance Function for Functional Data. Econometric Theory 31 (2015), 1359-1381. pdf

25. Greedy Algorithms for Predictions. Bernoulli 22 (2016), 1227-1277. pdf

26. Estimation for the Prediction of Point Processes with Many Covariates. Econometric Theory 34 (2018), 598-627. pdf

27. Inference for Constrained Stationary Autoregressive Processes. To appear in IEEE Transactions on Information Theory 65 (2018),  538-550. pdf

28. Price Drift before U.S. Macroeconomic News: Private Information about Public Announcements? (with Alexander Kurov, Georg Strasser and Marketa Wolfe). Journal of Financial and Quantitative Analysis 54 (2019),  449-479. pdf

29. Intra-Day Prediction of End of Day Trading Volume. Journal of Financial Econometrics (2019), https://doi.org/10.1093/jjfinec/nbz019. pdf

30. Estimation in Reproducing Kernel Hilbert Spaces With Dependent Data. IEEE Transactions on Information Theory 67 (2020), 1782-1795. pdf

31. Intraday Trades Profile Estimation: An Intensity Approach. Journal of Financial Econometrics (2021), https://doi.org/10.1093/jjfinec/nbab014. pdf

32. Testing Subspace Restrictions in the Presence of High Dimensional Nuisance Parameters. Electronic Journal of Statistics 16 (2022), 5277-5320. pdf

33. Estimation of a High-Dimensional Counting Process without Penalty for High Frequency Events (with L. Mucciante). Econometric Theory (2022), 1-20,  https://doi.org/10.1017/S0266466622000238. pdf 

34.  Empirical Asset Pricing with Functional Factors (with P. Nadler). Journal of Financial Econometrics (2022), https://doi.org/10.1093/jjfinec/nbac003. pdf

35.  Drift Begone! Release Policies and Preannouncement Informed Trading (with A. Kurov and M.H. Wolfe). Journal of International Money and Finance 128 (2022), https://doi.org/10.1016/j.jimonfin.2022.102718. pdf

Book Chapter 

Draw Downs as a Measure of Hedge Fund Risk: Some Stylized Facts. (with Gianfranco Lande and Steve Satchell). In G.N. Gregoriou, F. Rouah, and V.N. Karavas (editors), Hedge Funds: Strategies, Risk Assessment, and Returns. Washington: Beard Books, 2003, 235-246.

Work in Progress

1. An Order Book Dependent Hawkes Process for Large Datasets (with Luca Mucciante). pdf

2.  Consistent Causal Inference for High Dimensional Time Series (with Francesco Cordoni). pdf

3. Reinforcement Learning for Dynamic Model Selection (with Francesco Cordoni). pdf 

Companion code to some of the most recent papers can be found in the github repository: https://github.com/asancetta