Localizing Temperature Risk

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Localizing Temperature Risk

Weather influences our daily lives and choices Impact on corporate revenues and earnings. Meteorological institutions: business activity is weather dependent. British Met Office: daily beer consumption gain 10% if temperature increases by 3◦C If temperature in Chicago is less than 0◦C consumption of orange juice declines 10% on average. This CL is on local weather risk

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Courselet Content

1 components

Requirements

  • be cool

General Overview

Description

Questions

How to model temperature dynamics?

How to estimate pricing kernels?

Benth et al. (2007), Campbell and Diebold (2005) fall short in

providing Gaussian residuals

Avoid non Gaussian residuals!

Signicantly closer to Gaussianity in over 1000 cities

Preferable in terms of forecasting and pricing

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Meet the instructors !

instructor
About the Instructor

Wolfgang Karl HÄRDLE attained his Dr. rer. nat. in Mathematics at Universität Heidelberg in 1982 and in 1988 his habilitation at Universität Bonn.  He is Ladislaus von Bortkiewicz Professor of Statistics at Humboldt-Universität zu Berlin and the director of the Sino German Graduate School (洪堡大学 + 厦门大学) IRTG1792 on “High dimensional non stationary time series analysis”.  He directs  IDA Institute for Digital Assets,  

  University of Economic Studies, Bucharest, RO. His research focuses on data analytics, dimension reduction and quantitative finance.  He has published over 30 books and more than 300 papers in top statistical, econometrics and finance journals. He is highly ranked and cited on Google Scholar, REPEC and SSRN. He has professional experience in financial engineering, S.M.A.R.T. (Specific, Measurable, Achievable, Relevant, Timely) data analytics, machine learning and cryptocurrency markets. He has created the www.quantlet.com platform, a cryptocurrency index, CRIX www.royalton-crix.com  He is 玉山学者 (Yushan Scholar), web page hu.berlin/wkh