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 An analytical method of estimating Value-at-Risk on the Belgrade Stock Exchange

2009 ◽  
Vol 54 (183) ◽  
pp. 119-138 ◽  
Author(s):  
Milica Obadovic ◽  
Mirjana Obadovic
Keyword(s):  
At Risk ◽  
Failure Rate ◽  
Confidence Level ◽  
Analytical Method ◽  
Value At Risk ◽  
Risk Evaluation ◽  
Risk Model ◽  
Stock Exchange ◽  
Confidence Levels ◽  
The Given

This paper presents market risk evaluation for a portfolio consisting of shares that are continuously traded on the Belgrade Stock Exchange, by applying the Value-at-Risk model - the analytical method. It describes the manner of analytical method application and compares the results obtained by implementing this method at different confidence levels. Method verification was carried out on the basis of the failure rate that demonstrated the confidence level for which this method was acceptable in view of the given conditions.

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.

scholarly journals Analysis of Risk using Value at Risk (VaR) After Crisis in 2008 Study in Stocks of Bank Mandiri, Bank BRI and Bank BNI in 2009-2011

2013 ◽  
Vol 5 (8) ◽  
pp. 394-400 ◽  
Author(s):  
Hasna Fadhila ◽  
Nora Amelda Rizal
Keyword(s):  
At Risk ◽  
Maximum Likelihood ◽  
Value At Risk ◽  
Maximum Likelihood Method ◽  
Historical Data ◽  
Likelihood Function ◽  
Likelihood Method ◽  
Normal Curve ◽  
Historical Simulation ◽  
Time Value

Value at Risk (VaR) is a tool to predict the greater loss less than the certain confidence level over a period of time. Value at Risk Historical Simulation produce reliable value of VaR because of the historical data and measure the skewness of the observe data. So, Value at Risk well used by investors to determine the risk to be faced on their investment. To calculate VAR it is better to use maximum likelihood, which has been considered for estimating from historical data and also available for estimating nonlinear model. It is also a mathematic function that can approximate return. From the maximum likelihood function with normal distribution, we can draw the normal curve at one tail test. This research conducted to calculate Value at Risk using maximum likelihood. The normal curve will be compared with data return at each bank (Bank Mandiri, Bank BRI and Bank BNI). Empirical results demonstrated that Bank BNI in 2009, Bank BRI in 2010 and Bank BNI in 2011, had less value of VaR by historical simulation in each year. It is concluded that by using maximum likelihood method in the estimation of VaR, has certain appropriates compared with the normal curve.

scholarly journals Non-extensive value-at-risk estimation during times of crisis

2021 ◽  
pp. 2150099
Author(s):  
Ahmad Hajihasani ◽  
Ali Namaki ◽  
Nazanin Asadi ◽  
Reza Tehrani
Keyword(s):  
At Risk ◽  
Financial Markets ◽  
Value At Risk ◽  
Risk Estimation ◽  
Specific Pattern ◽  
Gaussian Probability Density Function ◽  
Confidence Levels ◽  
Control Instrument ◽  
The Difference ◽  
Non Gaussian

Value-at-risk (VaR) is a crucial subject that researchers and practitioners extensively use to measure and manage uncertainty in financial markets. Although VaR is a standard risk control instrument, there are criticisms about its performance. One of these cases, which has been studied in this research, is the VaR underestimation during times of crisis. In these periods, the non-Gaussian behavior of markets intensifies, and the estimated VaRs by typical models are lower than the real values. A potential approach that can be used to describe the non-Gaussian behavior of return series is the Tsallis entropy framework and nonextensive statistical methods. This paper has used the nonextensive models for analyzing financial markets’ behavior during crisis times. By applying the q-Gaussian probability density function for emerging and mature markets over 20 years, we can see a better VaR estimation than the regular models, especially during crisis times. We have shown that the q-Gaussian models composed of VaR and Expected Shortfall (ES) estimate risk better than the standard models. By comparing the ES, VaR, [Formula: see text]-VaR and [Formula: see text]-ES for emerging and mature markets, we see in confidence levels more than 0.98, the outputs of q models are more real, and the [Formula: see text]-ES model has lower errors than the other ones. Also, it is evident that in the mature markets, the difference of VaR between normal condition and nonextensive approach increases more than one standard deviation during times of crisis. Still, in the emerging markets, we cannot see a specific pattern. The findings of this paper are useful for analyzing the risk of financial crises in different markets.

scholarly journals Local Likelihood Density Estimation and Value-at-Risk

2010 ◽  
Vol 2010 ◽  
pp. 1-26 ◽  
Author(s):  
Christian Gourieroux ◽  
Joann Jasiak
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Local Approximation ◽  
Conditional Density ◽  
Conditional Value At Risk ◽  
Computation Method ◽  
Toronto Stock Exchange ◽  
Historical Simulation ◽  
Portfolio Returns

This paper presents a new nonparametric method for computing the conditional Value-at-Risk, based on a local approximation of the conditional density function in a neighborhood of a predetermined extreme value for univariate and multivariate series of portfolio returns. For illustration, the method is applied to intraday VaR estimation on portfolios of two stocks traded on the Toronto Stock Exchange. The performance of the new VaR computation method is compared to the historical simulation, variance-covariance, and J. P. Morgan methods.

scholarly journals PERHITUNGAN VALUE AT RISK KUNJUNGAN WISATAWAN ASING KE BALI

2020 ◽  
Vol 9 (1) ◽  
pp. 69
Author(s):  
AGUS PUTU SURYAWAN ◽  
KOMANG DHARMAWAN ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Value At Risk ◽  
Rapid Development ◽  
Tourism Industry ◽  
Provincial Government ◽  
Confidence Levels ◽  
Tourism Promotion ◽  
Natural Beauty

The development of the tourism industry in Bali is very fast compared to other regions in Indonesia. This is due to the fascination of Bali which fascinates tourists, such as culture, customs and natural beauty. The rapid development of tourism in Bali requires tourism risk management. The purpose of this study is to calculate the Value At Risk (VaR) of Chinese, British and American tourists visiting Bali. The study was conducted using the VaR method with the GARCH (1,1) and GJR (1,1) models. Chinese tourist visit data is homocedasticity so it cannot proceed to GARCH (1.1) and GJR (1.1) modeling. VaR value of British and American tourist visits using the GARCH (1.1) and GJR (1.1) models at 95% confidence levels respectively -69.2% and -43.6 with an average VaR value of -56, 4%, and -69.3% and -44.7% with an average VaR of -57%. This means that if the Bali Government targets the number of tourist visits to be 7,100,000 people with a tourism promotion cost of Rp.134.1 per person, then there will be at least 4,004,400 people visiting Bali. So the investment costs incurred by the Provincial Government of Bali for tourism promotion of Rp. 536,990,040.

Risk factor beta conditional value-at-risk

2009 ◽  
Vol 28 (6) ◽  
pp. 549-558 ◽  
Author(s):  
Andrei Semenov
Keyword(s):  
At Risk ◽  
Risk Factor ◽  
Value At Risk ◽  
Conditional Value At Risk

scholarly journals Estimation of the Portfolio Risk from Conditional Value at Risk Using Monte Carlo Simulation

2021 ◽  
Vol 17 (3) ◽  
pp. 370-380
Author(s):  
Ervin Indarwati ◽  
Rosita Kusumawati
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Risk Estimation ◽  
Conditional Value At Risk ◽  
Portfolio Risk ◽  
Portfolio Returns

Portfolio risk shows the large deviations in portfolio returns from expected portfolio returns. Value at Risk (VaR) is one method for determining the maximum risk of loss of a portfolio or an asset based on a certain probability and time. There are three methods to estimate VaR, namely variance-covariance, historical, and Monte Carlo simulations. One disadvantage of VaR is that it is incoherent because it does not have sub-additive properties. Conditional Value at Risk (CVaR) is a coherent or related risk measure and has a sub-additive nature which indicates that the loss on the portfolio is smaller or equal to the amount of loss of each asset. CVaR can provide loss information above the maximum loss. Estimating portfolio risk from the CVaR value using Monte Carlo simulation and its application to PT. Bank Negara Indonesia (Persero) Tbk (BBNI.JK) and PT. Bank Tabungan Negara (Persero) Tbk (BBTN.JK) will be discussed in this study.  The  daily  closing  price  of  each  BBNI  and BBTN share from 6 January 2019 to 30 December 2019 is used to measure the CVaR of the two banks' stock portfolios with this Monte Carlo simulation. The steps taken are determining the return value of assets, testing the normality of return of assets, looking for risk measures of returning assets that form a normally distributed portfolio, simulate the return of assets with monte carlo, calculate portfolio weights, looking for returns portfolio, calculate the quartile of portfolio return as a VaR value, and calculate the average loss above the VaR value as a CVaR value. The results of portfolio risk estimation of the value of CVaR using Monte Carlo simulation on PT. Bank Negara Indonesia (Persero) Tbk and PT. Bank Tabungan Negara (Persero) Tbk at a confidence level of 90%, 95%, and 99% is 5.82%, 6.39%, and 7.1% with a standard error of 0.58%, 0.59%, and 0.59%. If the initial funds that will be invested in this portfolio are illustrated at Rp 100,000,000, it can be interpreted that the maximum possible risk that investors will receive in the future will not exceed Rp 5,820,000, Rp 6,390,000 and Rp 7,100,000 at the significant level 90%, 95%, and 99%

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