Arbor City Community Foundation: Executive Education Version

2017 ◽  
pp. 1-5
Author(s):  
Karl Schmedders ◽  
Russell Walker ◽  
Michael Stritch
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Critical Evaluation ◽  
Critical Discussion ◽  
Management Policy ◽  
City Region ◽  
City Community ◽  
Portfolio Returns ◽  
Fund Balance ◽  
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. In its report for the investment committee, the ACCF risk analytics team recommended the daily VaR at 95% confidence as a measure for short-term risk and reported the corresponding numbers. It is now the task of the investment committee to interpret these figures. The case questions guide the executive students to a critical evaluation of both the reported VaR figures as well as of the VaR methodology.Understanding the concept of value at risk (VaR); Interpreting the results of VaR calculations; Evaluating the appropriateness of VaR calculations; Critical discussion of the VaR methodology.

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.

Arbor City Community Foundation (B): Managing Good Fortune

2017 ◽  
pp. 1-4
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 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.

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 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 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%

scholarly journals Value-at-Risk Estimation Method Based on Normal Distribution, Logistics Distribution and Historical Simulation

2020 ◽  
Vol 1 (1) ◽  
pp. 13-18
Author(s):  
Dwi Susanti ◽  
Sukono Sukono ◽  
Maria Jatu Verrany
Keyword(s):  
At Risk ◽  
Normal Distribution ◽  
Value At Risk ◽  
Risk Estimation ◽  
Estimation Method ◽  
Simulation Method ◽  
Logistic Distribution ◽  
Historical Simulation ◽  
Logistics Distribution ◽  
Portfolio Returns

This paper discusses the risk analysis of single stock and portfolio returns. The stock data analyzed are BNI, BRI shares and portfolio. After obtaining a stock return, value at risk (VaR) will be estimated using the normal distribution approach, logistic distribution, and historical simulation. From the VaR results, a backtest is then conducted to test the validity of the model and the backtest results for BNI and the portfolio produce a smaller QPS on the historical simulation method compared to the normal distribution and logistics distribution approaches. This shows that BNI VaR and VaR portfolios with the historical simulation method are more consistent than other methods. While the backtest results for BRI produced the smallest QPS on the normal distribution approach compared to the logistical distribution and historical simulation approaches. This shows that the VaR BRI using the normal distribution approach is more consistent than the other methods.

scholarly journals THE ESTIMATION AND DECOMPOSITION OF VALUE-AT-RISK FOR NON-NORMAL PORTFOLIO RETURNS

2013 ◽  
Vol 14 (Supplement_1) ◽  
pp. S213-S226
Author(s):  
Doowoo Nam
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Analytical Methods ◽  
Value At Risk ◽  
Decomposition Analysis ◽  
Market Risk ◽  
Management Tool ◽  
Portfolio Returns ◽  
Var Decomposition ◽  
The Individual

Value-at-risk (VaR) is a widely used measure for evaluating the market risk of a trading portfolio. This article presents the g-and-h method for estimating the VaR of a portfolio with non-normal returns, and adds to the usefulness of VaR as a risk management tool by decomposing the portfolio into individual VaRs to estimate the contribution of the individual components toward the overall VaR. While the VaR decomposition is algebraically simple under the assumption of normality, that is not the case under non-normality which is the property exhibited by most financial returns. We show that, by using the g-and-h VaR method, the decomposition analysis under non-normality can be performed with the same degree of intuitiveness and ease as for the analytical methods based on the assumption of normality.

Extremwerttheorie und Value-at-Risk

2015 ◽  
Vol 44 (5) ◽  
pp. 259-267
Author(s):  
Frank Schuhmacher ◽  
Benjamin R. Auer
Keyword(s):  
At Risk ◽  
Value At Risk
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