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Maximum Varma Entropy Distribution with Conditional Value at Risk Constraints

by Chang Liu 1, Chuo Chang 2 and Zhe Chang 3,4,*
1
Institute of Chinese Finance Studies, Southwestern University of Finance and Economics, Chengdu 611130, China
2
PBC School of Finance, Tsinghua University, Beijing 100083, China
3
Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
4
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(6), 663; https://doi.org/10.3390/e22060663
Received: 28 May 2020 / Revised: 13 June 2020 / Accepted: 14 June 2020 / Published: 16 June 2020
(This article belongs to the Section Multidisciplinary Applications)
It is well known that Markowitz’s mean-variance model is the pioneer portfolio selection model. The mean-variance model assumes that the probability density distribution of returns is normal. However, empirical observations on financial markets show that the tails of the distribution decay slower than the log-normal distribution. The distribution shows a power law at tail. The variance of a portfolio may also be a random variable. In recent years, the maximum entropy method has been widely used to investigate the distribution of return of portfolios. However, the mean and variance constraints were still used to obtain Lagrangian multipliers. In this paper, we use Conditional Value at Risk constraints instead of the variance constraint to maximize the entropy of portfolios. Value at Risk is a financial metric that estimates the risk of an investment. Value at Risk measures the level of financial risk within a portfolio. The metric is most commonly used by investment bank to determine the extent and occurrence ratio of potential losses in portfolios. Value at Risk is a single number that indicates the extent of risk in a given portfolio. This makes the risk management relatively simple. The Value at Risk is widely used in investment bank and commercial bank. It has already become an accepted standard in buying and selling assets. We show that the maximum entropy distribution with Conditional Value at Risk constraints is a power law. Algebraic relations between the Lagrangian multipliers and Value at Risk constraints are presented explicitly. The Lagrangian multipliers can be fixed exactly by the Conditional Value at Risk constraints. View Full-Text
Keywords: maximum entropy; value at risk; portfolio selection; Varma entropy; investment risk; power law maximum entropy; value at risk; portfolio selection; Varma entropy; investment risk; power law
MDPI and ACS Style

Liu, C.; Chang, C.; Chang, Z. Maximum Varma Entropy Distribution with Conditional Value at Risk Constraints. Entropy 2020, 22, 663.

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