CF99 Program

Explanation of the letters in the first column:
[ K ] = Keynote talk   (45 minutes)
[ I ] = Invited talk (45 minutes)
[ T ] = Talk (25 minutes)
[ S ] = Spotlight poster (3 minutes)
[ P ] = Poster

CF99 Program (Download Program as Word Document)

Wednesday January 6 *(Tutorials)*
7:30 am	Registration
8:15	Welcome
8:30	Hedge Fund Styles	David A. Hsieh, Fuqua School of Business, Duke University
10:30-10:45	Break
10:45	Forecasting Volatility	Stephen Figlewski, Leonard N. Stern School of Business, New York University
12:45	Lunch (provided)
1:45	Neuro-Dynamic Programming and Reinforcement Learning for Finance	Benjamin Van Roy, Stanford University
3:45-4:00	Break
4:00	Data Snooping	Halbert White, University of California, San Diego

Thursday January 7 (Conference Day 1)
8:00 am	Registration
9:00	Welcome
9:15 K	The Evolution of Risk Measurement: What we can learn from the experience of the duration measure?	H. Gifford Fong, President of Gifford Fong Associates
10:00 T	What is the spread without rounding? A Monte Carlo Markov chain approach	Clifford A. Ball, Vanderbilt University Tarun Chordia, Vanderbilt University
10:30-10:45	Break
10:45 I	Mutual fund styles	Stephen J. Brown, New York University
11:30 T	Valuing American options by simulation: A simple least-squares approach	Francis A. Longstaff, UCLA Eduardo S. Schwartz, UCLA
12:00-12:30	Spotlight Poster Presentations Group I
12:30-2:00	Lunch (provided)
2:00 T	What data should be used to price options?	Mikhail Chernov, Pennsylvania State Univ. Eric Ghysels, Pennsylvania State Univ.
2:30 T	Option valuation with the genetic programming approach	Christian Keber, University of Vienna
3:00-3:15	Break
3:15 T	Volatility clustering in financial markets: A micro-simulation of interacting agents	Thomas Lux, University of Bonn Michele Marchesi, University of Cagliari
3:45 I	Safe and effective importance sampling	Prof. Art Owen, Stanford University Yi Zhou, Goldman Sachs
4:30-5:00	Spotlight Poster Presentations Group II
Till 11:30	Dinner and Poster Session

Friday January 8 (Conference Day 2)
9:00-9:15	Welcome
9:15 I	Asymptotically optimal importance sampling and stratification for pricing path-dependent options	Paul Glasserman, Columbia University
10:00 T	Does volatility timing matter?	Jeff Flemming, Rice University Chris Kirby, Rice University Barbara Ostdiek, Rice University
10:30-10:45	Break
10:45 T	Nonparametric testing of ARCH for option pricing	Peter Christoffersen, McGill University Jinyon Hahn, University of Pennsylvania
11:15 T	Pricing stock options under stochastic volatility and interest rates with efficient methods of moments estimation	George J. Jiang, University of Groningen Pieter J. van der Sluis, University of Amsterdam
11:45 T	Implementing trading strategies for forecasting models	Neville Towers, London Business School A. Neil Burgess, London Business School
12:15-2:00	Lunch (on your own)
2:00 T	Minimizing downside risk via stochastic dynamic programming	John Moody, Oregon Graduate Institute Matthew Saffell, Oregon Graduate Institute
2:30 T	Exchange rates and fundamentals: Evidence from out-of-sample forecasting using neural networks	Min Qi, Kent State University Yangru Wu, Rutgers University
3:00 T	Real-time trading models with heterogeneous expectations and the statistical properties of foreign exchange rates	Ramazan Gencay, University of Windsor, visiting Olsen and Associates Giuseppe Ballocchi, Olsen and Associates Michel Dacorogna, Olsen and Associates Richard Olsen, Olsen and Associates Olivier Pictet, Olsen and Associates
3:30-3:45	Break
3:45 T	Dangers of data-driven inference: The case of calendar effects in stock returns	Ryan Sullivan, UCSD Allan Timmermann, UCSD Halbert White, UCSD
4:15 K	Technology and the future of finance	David E. Shaw, Chairman and CEO of D. E. Shaw & Co., Inc.

Spotlight Posters Group I
1	A computational framework for contingent claim pricing and hedging under general processes	Les Clewlow, Warwick Business School Russell Grimwood, Warwick Business School
2	Market force, ecology, and evolution	J. Doyne Farmer, Prediction Company
3	Endogenous information selection: Fundamentals and technical trading rules	David Goldbaum, Rutgers University
4	The entropic market hypothesis	Les Gulko, General Re Corporation
5	Trading mutual funds with piece-wise constant models	Michael de la Maza, Redfire Capital Management Group

Spotlight Posters Group II
6	Conditional value at risk	Dirk Ormoneit, Stanford University Ralph Neuneier, Siemens AG
7	Identifying noise traders: The head-and-shoulders pattern in U.S. equities	Carol Osler, Federal Reserve Bank of New York
8	A recursive modelling approach to predicting UK stock returns	M. Hashem Pesaran, Trinity College, Cambridge Allan Timmerman, University of California at San Diego
9	Pricing discrete knock-out options with tree methods	Manfred Steiner, University of Augsburg Martin Wallmeier, University of Augsburg Reinhold Hafner, RiskLab
10	Confidence intervals and hypothesis testing for the Sharpe and Treynor performance measures: A bootstrap approach	H. D. Vinod, Fordham University Matthew R. Morey, Fordham University
11	Active portfolio-management with neural networks	Hans Georg Zimmermann, Siemens AG Ralph Neuneier, Siemens AG

Posters
12	Portfolio formation and asset classification with neural networks	Hamid Ahmadi, California State University, Sacramento Laurence Takeuchi, California State University, Sacramento Burton F. Schaffer, California State University, Sacramento
13	Change of measure in monte-carlo integration via Gibbs sampling	Filippo Altissimo, Bank of Italy
14	Portfolio optimization: a model and heuristic to capture the trade-off between replication error and the number of instrument	Edoardo Amaldi, Cornell University Jean-Francois Pusztaszeri, Cornell University
15	A Bayesian approach to estimating mutual fund returns	Amir F. Atiya, California Institute of Technology Malik Magdon-Ismail, California Institute of Technology
16	Estimating variance in the foreign exchange market with high frequency data	Andrea Beltratti, University of Turin Claudio Morana, Heriot-Watt University
17	A simulation algorithm based on measure relationships in the lognormal market models	Alan Brace, University of New South Wales Marek Musiela, University of New South Wales Erik Schlogl, University of New South Wales
18	Statistical arbitrage models of the FTSE 100	A. Neil Burgess, London Business School
19	Estimating the complexity function of financial time series: an estimation based on predictive stochastic complexity	Shu-Heng Chen, National Chengchi University Ching-Wei Tan, National Chengchi University
20	An adaptive evolutionary approach to option pricing via genetic programming	N. K. Chidambaran, NYU C. H. Jevons Lee, Tulane University Joaquin R. Trigueros, Tulane University
21	Tracking indices fixed-income securities for the Italian market	Rita Laura D'Ecclesia, Universita di Urbino Marida Bertocchi, Universita di Bergamo Jozsef Abaffy, University of Economics
22	A support vector machine approach to bankruptcy prediction: A case study	A. Fan, The University of Melbourne D. Hong, The University of Melbourne M. Palaniswami, The University of Melbourne C. Tan, The University of Melbourne C. Scott, The University of Melbourne
23	Nonparametric estimation of the Black-Scholes option pricing equation: a comparative analysis of statistical and machine learning methods	Jorge Galindo-Flores, Harvard University
24	Using nonlinear neurogenetic models with profit related objective functions to trade the US T-bond future	Zac Harland, Krueger Research
25	Parameter tuning in trading algorithms using ASTA	Thomas Hellstrom, Malardalen University Kenneth Holmstrom, Malardalen University
26	Goodness of fit, stability and data mining	Juan del Hoyo, Universidad Autonoma de Madrid Guillermo Llorente Alvarez, Universidad Autonoma de Madrid
27	Bank lending policy, credit scoring and value at risk	Tor Jacobson, Sveriges Riksbank Kasper Roszbach, Stockholm School of Economics
28	Technical trading creates a prisoner's dilemma: results from an agent-based model	Shareen Joshi, Santa Fe Institute Jeffrey Parker, Reed College Mark Bedau, Reed College
29	Application of adaptive supervised learning decision (ASLD) network in stock market	Kei Keung Hung, The Chinese University of Hong Kong Lei Xu, The Chinese University of Hong Kong
30	Further study on independent components in analysis of financial data	Zhi-bin Lai, The Chinese University of Hong Kong Yiu-ming Cheung, The Chinese University of HK Lei Xu, The Chinese University of HK
31	A constrained hybrid approach to pricing options	Paul Lajbcygier, Monash University Jerome Connor, London Business School Raymond Tsang, Monash University
32	Curved principle component analyis of Asian currencies	Juan K. Lin, MIT
33	Nonparametric testing of the high-frequency efficiency of the 1997 Asian foreign exchange markets	Cornelis K. Los, Nanyan Technological University
34	Comparing connectionist and symbolic models for bankruptcy prediction	E. Martineli, University of Sao Paulo H. Diniz, University of Sao Paulo A. de Carvalho, University of Sao Paulo S. O. Rezende, University of Sao Paulo A. Matias, University of Sao Paulo
35	Clustering analysis procedures and international private indebtness	A. M. D. Monteiro, Catholic University of Rio de Janeiro D. D. Carneiro, Catholic University of Rio de Janeiro C. E. Pedreira, Catholic University of Rio de Janeiro
36	The term structure of interactions of foreign exchange rates	John Moody, Oregon Graduate Institute Howard Yang, Oregon Graduate Institute
37	A state-space HJM framework for modelling the term structure of interest rates using Eurodollar futures prices	Nesan N. Nair, University of Natal at Durban
38	Bootstrap methods and statistical accuracy: The case of South African rights issue and initial public offering markets	Michael Page, University of Cape Town Arthur H. E. Money, Henley Management College Karolina Koztowska, University of Szczecin
39	Relative performance of incentive mechanisms in delegated investments: a computational study	T. S. Raghu, Arizona State University H. R. Rao, SUNY Buffalo P. K. Sen, SUNY Buffalo
40	A comparison of option pricing and hedging performances of the IT model versus the Black's model	Wu Ronghui, National University of Singapore Chang Shih Kang, National University of Singapore
41	Bank lending policy, credit scoring, and the survival of loans	Kasper Roszbach, De Nederlandsche Bank
42	An optimal binary predictor for an investor in a futures market	Dirk W. Rudolph, Institute of Agricultural Development in Central and Eastern Europe
43	Bayesian network models of portfolio risk and return	Catherine Shenoy, University of Kansas Prakash P. Shenoy, University of Kansas
44	Risk neutral forecasting	Spyros Skouras, European University Institute
45	Dynamic asset allocation and fixed income management	Carsten Sorensen, Copenhagen Business School
46	A note on arbitrage asset pricing	Manfred Steiner, University of Augsburg Sebastian Schneider, University of Augsburg
47	Bootstrap predictability of daily exchange rates in ARMA models	Demoosthenes N. Tambakis, City University Business School, London
48	A symbolic dynamics approach to volatility prediction	Peter Tino, Austrian Research Institute for Artificial Intelligence Christian Schittenkopf, Austrian Research Institute for Artificial Intelligence Georg Dorffner, Austrian Research Institute for Artificial Intelligence Engelbert J. Dockner, University of Vienna
49	Optimization of technical trading strategy using a split search genetic algorithm	Raymond Tsang, Monash University Paul Lajbcygier, Monash University

CF99 Top Registration

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URL: http://www-psych.stanford.edu/~andreas/CF99