论文标题

基于频率的凯利最佳投资组合的必要条件

Necessary and Sufficient Conditions for Frequency-Based Kelly Optimal Portfolio

论文作者

Hsieh, Chung-Han

论文摘要

在本文中,我们将带有$ M \ GEQ 2 $资产优化问题的离散时间组合考虑在内,其中包括重新平衡频率是最大化中的附加参数。所谓的凯利标准用作性能指标;即,最大化交易者帐户的预期对数增长,并获得的投资组合称为基于频率的凯利最佳投资组合。本文的重点是扩展我们先前工作的结果,以获得对投资组合的各种最佳特征。更具体地说,使用凯利在基于频率的公式中的标准,我们首先证明基于频率的凯利最佳投资组合是必要和足够的条件。借助这些条件,我们显示了几种新的最优性特征,例如预期比率最优性和渐近相对最优性,以及我们称之为扩展的主要资产定理的结果。也就是说,我们证明$ i $ th的资产在投资组合中占主导地位,并且仅当Kelly Optimal Portfolio仅由该资产组成时。定理上的“扩展”一词源于以下事实:这只是我们以前的工作中证明的足够的结果。因此,在本文中,我们将其改进,以涉及必要部分的证明。此外,在我们的基于频率的交易框架中,还详细研究了交易者的生存问题(无破产考虑)。最后,为了弥合理论和实践,我们建议使用称为主要资产条件的概念来决定何时应该触发交​​易的简单交易算法。使用历史价格数据的相应交易绩效报告为支持证据。

In this paper, we consider a discrete-time portfolio with $m \geq 2$ assets optimization problem which includes the rebalancing~frequency as an additional parameter in the maximization. The so-called Kelly Criterion is used as the performance metric; i.e., maximizing the expected logarithmic growth of a trader's account, and the portfolio obtained is called the frequency-based Kelly optimal portfolio. The focal point of this paper is to extend upon the results of our previous work to obtain various optimality characterizations on the portfolio. To be more specific, using Kelly's criterion in our frequency-based formulation, we first prove necessary and sufficient conditions for the frequency-based Kelly optimal portfolio. With the aid of these conditions, we then show several new optimality characterizations such as expected ratio optimality and asymptotic relative optimality, and a result which we call the Extended Dominant Asset Theorem. That is, we prove that the $i$th asset is dominant in the portfolio if and only if the Kelly optimal portfolio consists of that asset only. The word "extended" on the theorem comes from the fact that it was only a sufficiency result that was proved in our previous work. Hence, in this paper, we improve it to involve a proof of the necessity part. In addition, the trader's survivability issue (no bankruptcy consideration) is also studied in detail in our frequency-based trading framework. Finally, to bridge the theory and practice, we propose a simple trading algorithm using the notion called dominant asset condition to decide when should one triggers a trade. The corresponding trading performance using historical price data is reported as supporting evidence.

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