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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Correlations in Financial Markets - An empirical analysis of measuring correlation on different sampling frequencies |
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| Institution | University of Zurich |
| Faculty | Faculty of Economics, Business Administration and Information Technology |
| Date | 2015 |
| Zusammenfassung | Over the last 130 Years, ever since Sir Francis Galton first drew a line on a diagram suggesting that the size of a sweet pea was somehow influenced by the size of its progenitors, people have been trying to understand the dependence between variables and use it to gain all sorts of knowledge, the most important being prediction of future events. In statistics, numerous methods are used to determine whether a random variable’s observable outcome (event) can be used to predict its next value, or even the next value of a different variable. Pearson’s product‐moment correlation coefficient invariably plays a big role in this kind of analysis, as it reveals not only linear dependence between two variables but also to what degree such a dependence exists. With inception and evolution of financial markets the correlation coefficient became even more important. Technological progress made the recording of astronomic amounts of financial data as well as detailed, automated analysis of such data possible. These changes have caused the art and science of statistics to reach unprecedented importance. Nowadays, correlation is a term that individuals working in the banking and finance sector must be familiar with, as it is an indispensible tool when looking at topics such as quantitative market data analysis, diversification, or hedging. The aim of this paper is to look at the correlation coefficient and its use in financial markets and further to analyze it over different sampling frequencies (data observations varying from daily, weekly, monthly to even quarterly) via an empirical study of real historical prices and returns, and its associated correlation matrix. |
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