- 著者
-
森谷 博之
- 出版者
- 中央大学企業研究所
- 雑誌
- 企業研究 (ISSN:13479938)
- 巻号頁・発行日
- vol.34, pp.77-105, 2019-02-28
Strong computation power and the age of big data have enabled us to apply artificial intelligence, machine learning and genetic algorithms to financial markets. In the 1950’s, Harry Markowitz invented the portfolio optimization technique that changed the concept of investment opportunities from highest return, ignoring risk, to the balance between risk and rewards. However, this innovative idea has presented many obstacles. But, over the years innovative people have been inventing mathematical solutions for these various constraints. Even though this has been challenging historically, practitioners prefer the simple heuristic methods based on their experience due to difficulty to estimate expected returns and volatility. Finally, new solution is developed and called a Risk Parity policy and enhanced version, Hierarchical Risk Parity introduced by de Prado in 2016.This paper first introduces the history of Modern Portfolio Theory and Risk Parity portfolio and then explains how to develop hierarchical risk parity. In conclusion, a hedge fund portfolio is constructed by using hierarchical risk parity to compare the results with those of an equally weighted portfolio and a minimum variance portfolio.