This chapter looks at extreme value data analysis as an application of VGLMs/VGAMs. The two most important models (generalized extreme value or GEV distribution, and generalized Pareto distribution or GPD) are shown to be easily amenable to the VGLM/VGAM framework. Some real data examples are given.
- Finkenstadt, B. and H. Rootzén (Eds.) 2003. Extreme Values in Finance, Telecommunications and the Environment. Boca Raton: Chapman & Hall/CRC.Google Scholar
- Novak, S. Y. 2012. Extreme Value Methods with Applications to Finance. Boca Raton, FL, USA: CRC Press.Google Scholar
- Reiss, R.-D. and M. Thomas 2007. Statistical Analysis of Extreme Values: with Applications to Insurance, Finance, Hydrology and Other Fields (Third ed.). Basel, Switzerland: Birkhäuser.Google Scholar
- Smith, R. L. 2003. Statistics of extremes, with applications in environment, insurance and finance. See Finkenstadt and Rootzén (2003), pp. 1–78.Google Scholar