Portfolio Risk Management with Value at Risk: A Monte-Carlo Simulation on ISE-100 (original) (raw)
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The main objective of this study is to determine the adequacy of the measurement of market risks of financial institutions in Serbia by the method of Value at Risk (VaR). For investors, in the current global financial crisis, it is particularly important to accurately measure and allocate risk and efficiently manage their portfolio. Possibility of application of VaR methodology, which is basically designed and developed for liquid and developed markets, should be tested on the emerging markets, which are characterized by volatility, illiquidity and shallowness of the market. Value of VaR in this study was calculated using historical and parametric methods and backtesting analysis was used to verify the adequacy of the application of VaR models. Backtesting VaR model performance analysis was conducted to compare the ex-ante VaR estimate to the ex-post returns. The empirical results show that parameter exponentially weighted moving average model gives lower values at risk in both cases (95% and 99%) due to the fact that this method assigns weights to more recent returns while our portfolio is exposed to a lower volatility in recent time. Based on the results of Kupiec's and Christoffersen's test, it was observed that VaR estimates obtained by both, parametric and historical simulation, give a good prediction of market risk, at 95% and 99% confidence level.
A Monte Carlo simulation technique to determine the optimal portfolio
Management Science Letters, 2014
During the past few years, there have been several studies for portfolio management. One of the primary concerns on any stock market is to detect the risk associated with various assets. One of the recognized methods in order to measure, to forecast, and to manage the existing risk is associated with Value at Risk (VaR), which draws much attention by financial institutions in recent years. VaR is a method for recognizing and evaluating of risk, which uses the standard statistical techniques and the method has been used in other fields, increasingly. The present study has measured the value at risk of 26 companies from chemical industry in Tehran Stock Exchange over the period 2009-2011 using the simulation technique of Monte Carlo with 95% confidence level. The used variability in the present study has been the daily return resulted from the stock daily price change. Moreover, the weight of optimal investment has been determined using a hybrid model called Markowitz and Winker model in each determined stocks. The results showed that the maximum loss would not exceed from 1259432 Rials at 95% confidence level in future day.
Value-At-Risk Analysis in Risk Measurement and Formation of Optimal Portfolio in Banking Share
JBTI : Jurnal Bisnis : Teori dan Implementasi, 2021
This study analyzes the application of Value at Risk (VaR) in estimating the risk of investment in banking stocks and the formation of an optimal portfolio using the Mean-VaR method based on the Markowitz approach. Many studies show that market data are often abnormal and make the assumption of normality considered irrelevant. This is the background of research on VaR using the historical simulation method, which is a method that moves away from the concept of normality. In addition, the crisis due to the Covid-19 pandemic makes the market difficult to predict. The period used in this study is during a normal market and a crisis (covid-19 pandemic). VaR is calculated with a holding period (t) of one week and a confidence level of 95%. Based on the backtesting test, the historical simulation method is accepted as an accurate method in estimating the VaR value in both normal and crisis periods. The optimal portfolios formed based on the mean-VaR are Portfolio-1 (normal period) and Por...
Value at Risk: A Standard Tool in Measuring Risk : A Quantitative Study on Stock Portfolio
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The role of risk management has gained momentum in recent years most notably after the recent financial crisis. This thesis uses a quantitative approach to evaluate the theory of value at risk which is considered a benchmark to measure financial risk. The thesis makes use of both parametric and non parametric approaches to evaluate the effectiveness of VAR as a standard tool in measuring risk of stock portfolio. This study uses the normal distribution, student t-distribution, historical simulation and the exponential weighted moving average at 95% and 99% confidence levels on the stock returns of Sonny Ericsson, Three Months Swedish Treasury bill (STB3M) and Nordea Bank. The evaluations of the VAR models are based on the Kupiec (1995) Test. From a general perspective, the results of the study indicate that VAR as a proxy of risk measurement has some imprecision in its estimates. However, this imprecision is not all the same for all the approaches. The results indicate that models which assume normality of return distribution display poor performance at both confidence levels than models which assume fatter tails or have leptokurtic characteristics. Another finding from the study which may be interesting is the fact that during the period of high volatility such as the financial crisis of 2008, the imprecision of VAR estimates increases. For the parametric approaches, the t-distribution VAR estimates were accurate at 95% confidence level, while normal distribution approach produced inaccurate estimates at 95% confidence level. However both approaches were unable to provide accurate estimates at 99% confidence level. For the non parametric approaches the exponentially weighted moving average outperformed the historical simulation approach at 95% confidence level, while at the 99% confidence level both approaches tend to perform equally. The results of this study thus question the reliability on VAR as a standard tool in measuring risk on stock portfolio. It also suggest that more research should be done to improve on the accuracy of VAR approaches, given that the role of risk management in today's business environment is increasing ever than before. The study suggest VAR should be complemented with other risk measures such as Extreme value theory and stress testing, and that more than one back testing techniques should be used to test the accuracy of VAR.
The Analysis of Portfolio Risk Management using VAR Approach Based on Investor Risk Preference
KINERJA
Ackert and Deaves (2010) said that most people have tendency to being risk averse, but with appropriate amount of compensation, people may take more risk. Understanding those circumstances, this research trying to figure risk involved in a Mean-Variance Model. This model has taken consideration about investor risk preference in composed VAR model. VAR define as a measure of the risk of investments, which in this research focuses on risk preferences. This research also conducts comparison between optimum portfolio model known as Single Index Model and Mean-Variance Mode. Robustness test taken too analyze the outcomes from different data input. Research showed that risk preference has an impact on generating portfolio based on Mean-Variance Mode (MVM). Meanwhile, Single Index Model (SIM) found to given a similar result as MVM in high risk preference. This has shown that SIM may not adequate for those who have low risk preference. Research also show that risk taker investor get more ga...
Minimization of Portfolio Risk using Three Different Methods (A Comparative Study)
International Journal of Computer Applications, 2015
Portfolio risk plays an important role in stock market decisions. This paper considers an alternative idea which is to compute the risk assuming fixed return. Three different methods used to study this problem. The given study suggests expressing the general index of a given stock market in terms of other countries stock markets. A comparison between the three proposed methods is conducted using three different measures of error (the Mean-Variance (MV), Mean-Absolute Deviation (MAD), Conditional Value-at-Risk (CVaR)). The obtained results show that there are significant differences between the used methods. It is recommended using the simplest one.
Investment of portfolio known that there is an important level of uncertainty about the future worth of a portfolio. The concept of value at risk (VAR) has been used to help describe a portfolio's uncertainty. The current trend of investment in India is to invest in stock market which categorized as a high-risk level of investment. There are various methods to calculate the variance. Monte Carlo simulation method is one of the methods to calculate the VAR of the portfolio. Monte-Carlo simulation method is considered to be the optimization technique in which objective is to minimize/maximize the risk/profit before making any type of investment with portfolio. The Monte Carlo simulation method calculation for VAR of a portfolio can briefly be summarized in two steps. In the first step, a stochastic process is specified for financial variables. In the second step, financial variable of interest are simulated to get fictitious price path. The aim of the research is to develop the fi...
Portfolio Analysis of Investments in Risk Management
In many practical investment situations the amount of available memory on stock data is extremely huge. Thus many investors are attracted to base their decisions on the information "currently available in their minds" (see ). In the present paper various risk measurement models having application in the investment management are discussed. First we explain the concept of mean variance efficient frontier and Markowitz's model to find all efficient portfolios that maximize the expected returns and minimize the risk. Markovian risk measures are also mentioned. Some measures of portfolio analysis based on entropy mean-variance frontier are studied. Risk aversion index and Pareto-optimal sharing of risk are explained. In view of these facts it is very interesting to study how the investor should make investments so that his total expected return is maximized and the risk of losing his capital is minimized. A maximum entropy model in risk sharing is proposed and applied to some problems.
Jurnal Matematika, Statistika dan Komputasi
In investing, all investors must be faced with risk that must be borne. Therefore, to determine the best strategy in investing, every investor must calculate the risk. One statistical approach that can be used to measure the risk is Value at Risk (VaR). VaR is defined as a tolerable loss with a certain level of confidence. The purpose of this research is to estimate VaR using Variance Covariance and Historical Simulation methods on banking stock portfolio consisting of three stocks for the period 11 September 2020-30 September 2021. Both methods will then be evaluated using backtesting to determine the accuracy of VaR and to obtain the best method. From the research results, if the holding period is 1 day, then the VaR calculation for banking stock portfolio using both methods can be used to estimate the risk at 99% and 95% confidence levels, except for the VaR value using the Variance Covariance method for banking stock portfolio at 95% confidence level. The results show that Varia...
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In order to find out which risk measurement is the best indicator of efficiency in a portfolio, this study considers three different risk measurements: the meanvariance model, the mean absolute deviation model, and the downside risk model. Meanwhile short selling is also taken into account since it is an important