Abstract
It is assumed that the reader is already at least somewhat familiar with the basics of probability and statistics. The goals of this chapter are to
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1.
review these basics;
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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;
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3.
provide glimpses of how probability and statistics are applied to finance problems in this book; and
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4.
introduce notation that is used throughout the book.
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Ruppert, D. (2004). Probability and Statistical Models. In: Statistics and Finance. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6876-0_2
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