Applying the Elastic Asset Allocation Models to the Securities Markets of Mainland China Hong Kong and Taiwan

  • 朱 柏樺

Student thesis: Master's Thesis

Abstract

In this paper we take the Elastic Asset Allocation (EAA) introduced by Keller (2014) and apply it to the securities markets of the Greater China Economic Sphere comprising China Hong Kong and Taiwan EAA is based on geometrical elasticities composed of historical asset returns (R) volatilities (V) and correlations to an equal weighted index (C) It was inspired by the Flexible Asset Allocations (FAA) also proposed by Keller (2012) In principle both models utilize the concept of generalized momentum where we assume persistence in the short term for R V and C Portfolio formation is done monthly according to the combined score and the rule is to select the top N assets We also briefly introduce the FAA illustrate the process of generalization from FAA to EAA after which we elaborate on the EAA and its derivative models including an optimized model called the EAA Golden Models which includes both defensive and offensive ones The process of optimization goes through a dataset spanning over a century in order to avoid the data-snooping (or curve-fitting) problem Following Keller(2014) we apply the EAA models to the stock index ETFs bond index ETFs and REITs of China Hong Kong and Taiwan using the monthly data from Jan 2006 to Dec 2015 in which the portfolio has been rebalanced monthly since 1st May 2007 The empirical results show that the EAA Golden models demonstrate extraordinary risk-adjusted and absolute out-of-sample performance over the equal weighted index for a diversified asset universe selected from the Greater China; other EAA derivative models show a considerably lower degree of risk
Date of Award2016 Jun 23
Original languageEnglish
SupervisorMeng-Feng Yen (Supervisor)

Cite this

Applying the Elastic Asset Allocation Models to the Securities Markets of Mainland China Hong Kong and Taiwan
柏樺, 朱. (Author). 2016 Jun 23

Student thesis: Master's Thesis