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Document type:
Masterarbeit
Author(s):
Rauscher, Marco
Title:
A machine learning approach to predict trends of exchange traded products on the VIX
Translated title:
Ein Machine Learning Ansatz um Trends börsengehandelter Produkte mit Bezug zum VIX vorherzusagen
Abstract:
The CBOE volatility index (VIX) is a well-known index that displays the implied volatility of the S&P500.; Although the VIX itself is not tradable there are different products closely related to the VIX. This thesis examines intraday lead-lag effects of exchange-traded products (ETPs) that are related to this index, namely VXX, VIXY, TVIX, UVXY and SPY. More precisely, we try to identify one product that reacts to the movements of the other products. For this purpose, we introduce two machine learning...     »
Supervisor:
Prof. Dr. Rudi Zagst
Advisor:
Prof. Dr. Luis Seco, J. Motovoy, A. Sokolov
Cooperation:
University of Toronto
Year:
2020
University:
Technische Universität München
Faculty:
Fakultät für Mathematik
TUM Institution:
Lehrstuhl für Finanzmathematik
Commencing Date:
15.09.2020
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