scholarly journals The mean-value at risk static portfolio optimization using genetic algorithm

2014 ◽  
Vol 11 (1) ◽  
pp. 89-109 ◽  
Author(s):  
Vladimir Rankovic ◽  
Mikica Drenovak ◽  
Boban Stojanovic ◽  
Zoran Kalinic ◽  
Zora Arsovski
Keyword(s):  
Genetic Algorithm ◽  
At Risk ◽  
Value At Risk ◽  
Computation Time ◽  
Mean Value ◽  
Portfolio Allocation ◽  
Local Optima ◽  
Coherent Risk ◽  
Portfolio Returns ◽  
Risk Metric

In this paper we solve the problem of static portfolio allocation based on historical Value at Risk (VaR) by using genetic algorithm (GA). VaR is a predominantly used measure of risk of extreme quantiles in modern finance. For estimation of historical static portfolio VaR, calculation of time series of portfolio returns is required. To avoid daily recalculations of proportion of capital invested in portfolio assets, we introduce a novel set of weight parameters based on proportion of shares. Optimal portfolio allocation in the VaR context is computationally very complex since VaR is not a coherent risk metric while number of local optima increases exponentially with the number of securities. We presented two different single-objective and a multiobjective technique for generating mean-VaR efficient frontiers. Results document good risk/reward characteristics of solution portfolios while there is a trade-off between the ability to control diversity of solutions and computation time.

Bayesian Value at Risk Metric for Equity Portfolios

2014 ◽  
Author(s):  
Eric Hendries ◽  
Jun Huang ◽  
Rachel Li ◽  
Xiao Li ◽  
Yiyang Qi ◽  
...  
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Equity Portfolios ◽  
Risk Metric

scholarly journals ESTIMATING VALUE-AT-RISK BASED ON NON-NORMAL DISTRIBUTIONS

2015 ◽  
Vol 3 ◽  
pp. 188-195 ◽  
Author(s):  
Mária Bohdalová ◽  
Michal Greguš
Keyword(s):  
At Risk ◽  
Risk Factor ◽  
Value At Risk ◽  
Likelihood Method ◽  
Normal Distributions ◽  
Confidence Levels ◽  
Portfolio Returns ◽  
Distribution Parameters ◽  
Financial Portfolios ◽  
Daily Changes

The article presents a comparative study of parametric linear value-at-risk (VaR) models used for estimating the risk of financial portfolios. We illustrate how to adjust VaR for auto-correlation in portfolio returns. The article presents static and dynamic methodology to compute VaR, based on the assumption that daily changes are independent and identically distributed (normal or non-normal) or auto-correlated in terms of the risk factor dynamics. We estimate the parametric linear VaR over a risk horizon of 1 day and 10 days at 99% and 95% confidence levels for the same data. We compare the parametric VaR and a VaR obtained using Monte Carlo simulations with historical simulations and use the maximum likelihood method to calibrate the distribution parameters of our risk factors. The study investigated whether the parametric linear VaR applies to contemporary risk factor analysis and pertained to selected foreign rates.

Arbor City Community Foundation (A): The Foundation

2017 ◽  
pp. 1-3 ◽  
Author(s):  
Karl Schmedders ◽  
Russell Walker ◽  
Michael Stritch
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Value At Risk ◽  
Management Policy ◽  
City Region ◽  
Portfolio Rebalancing ◽  
City Community ◽  
Portfolio Returns ◽  
The Impact ◽  
Community Foundation

The Arbor City Community Foundation (ACCF) was a medium-sized endowment established in Illinois in the late 1970s through the hard work of several local families. The vision of the ACCF was to be a comprehensive center for philanthropy in the greater Arbor City region. ACCF had a fund balance (known collectively as “the fund”) of just under $240 million. The ACCF board of trustees had appointed a committee to oversee investment decisions relating to the foundation assets. The investment committee, under the guidance of the board, pursued an active risk-management policy for the fund. The committee members were primarily concerned with the volatility and distribution of portfolio returns. They relied on the value-at-risk (VaR) methodology as a measurement of the risk of both short- and mid-term investment losses. The questions in Part (A) of the case direct the students to analyze the risk inherent in both one particular asset and the entire ACCF portfolio. For this analysis the students need to calculate daily VaR and monthly VaR values and interpret these figures in the context of ACCF's risk management. In Part (B) the foundation receives a major donation. As a result, the risk inherent in its portfolio changes considerably. The students are asked to evaluate the risk of the fund's new portfolio and to perform a portfolio rebalancing analysis.Understanding the concept of value at risk (VaR); Calculating daily and monthly VaR by two different methods, the historical and the parametric approach; Interpreting the results of VaR calculations; Understanding the role of diversification for managing risk; Evaluating the impact of portfolio rebalancing on the overall risk of a portfolio.

scholarly journals Mean-value at risk portfolio selection problem using clustering technique : A case study

2019 ◽  
Author(s):  
Sheshma Kiran Kumari ◽  
P. Kumar ◽  
J. Priya ◽  
S. Surya ◽  
A. K. Bhurjee
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Mean Value ◽  
Selection Problem ◽  
Portfolio Selection Problem ◽  
Clustering Technique

scholarly journals A Genetic Algorithm for Investment–Consumption Optimization with Value-at-Risk Constraint and Information-Processing Cost

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 32 ◽  
Author(s):  
Zhuo Jin ◽  
Zhixin Yang ◽  
Quan Yuan
Keyword(s):  
Genetic Algorithm ◽  
At Risk ◽  
Information Processing ◽  
Decision Maker ◽  
Value At Risk ◽  
Optimal Investment ◽  
Finite Horizon ◽  
Risk Constraint ◽  
The Cost ◽  
Consumption Strategies

This paper studies the optimal investment and consumption strategies in a two-asset model. A dynamic Value-at-Risk constraint is imposed to manage the wealth process. By using Value at Risk as the risk measure during the investment horizon, the decision maker can dynamically monitor the exposed risk and quantify the maximum expected loss over a finite horizon period at a given confidence level. In addition, the decision maker has to filter the key economic factors to make decisions. Considering the cost of filtering the factors, the decision maker aims to maximize the utility of consumption in a finite horizon. By using the Kalman filter, a partially observed system is converted to a completely observed one. However, due to the cost of information processing, the decision maker fails to process the information in an arbitrarily rational manner and can only make decisions on the basis of the limited observed signals. A genetic algorithm was developed to find the optimal investment, consumption strategies, and observation strength. Numerical simulation results are provided to illustrate the performance of the algorithm.

Portfolio Optimization by Mean-Value at Risk Framework

2016 ◽  
Vol 10 (5) ◽  
pp. 1935-1948 ◽  
Author(s):  
Shokufeh Banihashemi ◽  
Ali Moayedi Azarpour ◽  
Hamidreza Navvabpour
Keyword(s):  
At Risk ◽  
Portfolio Optimization ◽  
Value At Risk ◽  
Mean Value ◽  
Risk Framework
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