Abstract
Often we are not interested merely in a single random variable but rather in the joint behavior of several random variables, for example, returns on several assets and a market index. Multivariate distributions describe such joint behavior. This chapter is an introduction to the use of multivariate distributions for modeling financial markets data. Readers with little prior knowledge of multivariate distributions may benefit from reviewing Appendices A.12–A.14 before reading this chapter.
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Notes
- 1.
The matrix of second partial derivatives of a function is called its Hessian matrix, so the Fisher information matrix is the expectation of the Hessian of the negative log-likelihood.
References
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Ruppert, D., Matteson, D.S. (2015). Multivariate Statistical Models. In: Statistics and Data Analysis for Financial Engineering. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2614-5_7
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DOI: https://doi.org/10.1007/978-1-4939-2614-5_7
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