Probability and Statistical Models
review these basics;
discuss more advanced; topics needed in our empirical study of financial markets data such as random vectors, covariance matrices, best linear prediction, heavy-tailed distributions, maximum likelihood estimation, and likelihood ratio tests;
provide glimpses of how probability and statistics are applied to finance problems in this book; and
introduce notation that is used throughout the book.
KeywordsProbability Density Function Cumulative Distribution Function Pareto Distribution Normal Probability Plot Tail Index
Unable to display preview. Download preview PDF.
- Alexander, C. (2001) Market Models: A Guide to Financial Data Analysis, Wiley, Chichester.Google Scholar
- Casella, G. and Berger, R. L. (2002) Statistical Inference, 2nd Ed., Thomson Learning, Pacific Grove, CA.Google Scholar
- Congdon, P. (2001) Bayesian Statistical Modelling, Wiley, Chichester. Congdon, P. (2003) Applied Bayesian Modelling, Wiley, Chichester.Google Scholar
- Delwiche, L. D. and Slaughter, S. J. (1998) The Little SAS Book: A Primer, 2nd Ed., SAS Publishing, Cary, N. C.Google Scholar
- Edwards, W. (1982) Conservatism in human information processing. In Judgement Under Uncertainty: Heuristics and Biases, edited by Kahneman, D., Slovic, P., and Tversky, A., Cambridge University Press, New York.Google Scholar
- Hanselman, D. and Littlefield, B. R. (2000) Mastering MATLAB 6, Prentice-Hall, Upper Saddle River, NJ.Google Scholar
- Johnson, N. L., Kotz, S., and Kemp, A. W. (1993) Discrete Univariate Distributions, 2nd Ed., Wiley, New York.Google Scholar
- Resnick, S. I. (2001) Modeling Data Networks, School of Operations Research and Industrial Engineering, Cornell University, Technical Report #1345.Google Scholar