The Impact of Correlation on (Range) Value-at-Risk

2021 ◽  
Author(s):  
Carole Bernard ◽  
Corrado De Vecchi ◽  
Steven Vanduffel
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
The Impact

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.

Estimating and Predicting Value-at-Risk in the presence of structural breaks: A Study based on unbiased extreme value volatility estimator

2020 ◽  
Vol 14 (1) ◽  
pp. 27-48
Author(s):  
Dilip Kumar
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Structural Breaks ◽  
Extreme Value ◽  
Total Loss ◽  
Fat Tails ◽  
Alternative Models ◽  
Forecasting Performance ◽  
The Impact ◽  
The Given

We provide a framework based on the unbiased extreme value volatility estimator to predict long and short position value-at-risk (VaR). The given framework incorporates the impact of asymmetry, structural breaks and fat tails in volatility. We generate forecasts of both long and short position VaR and evaluate the VaR forecasting performance of the proposed framework using various backtesting approaches for both long and short positions and compare the results with that of various alternative models. Our findings indicate that the proposed framework outperforms the alternative models in predicting the long and the short position VaR. Our findings also indicate that the VaR forecasts based on the proposed framework provides the least total loss score for various long and short positions VaR and this supports the superior properties of the proposed framework in forecasting VaR more accurately. The study contributes by providing a framework to predict more accurate VaR measure based on the unbiased extreme value volatility estimator.

scholarly journals OPTIMAL PROPORTIONAL REINSURANCE UNDER TWO CRITERIA: MAXIMIZING THE EXPECTED UTILITY AND MINIMIZING THE VALUE AT RISK

2010 ◽  
Vol 51 (4) ◽  
pp. 449-463 ◽  
Author(s):  
ZHIBIN LIANG ◽  
JUNYI GUO
Keyword(s):  
At Risk ◽  
Expected Utility ◽  
Value At Risk ◽  
Optimal Solution ◽  
Optimal Strategies ◽  
Pareto Optimal Solution ◽  
Model Parameters ◽  
Pareto Optimal ◽  
Proportional Reinsurance ◽  
The Impact

AbstractWe consider the optimal proportional reinsurance from an insurer’s point of view to maximize the expected utility and minimize the value at risk. Under the general premium principle, we prove the existence and uniqueness of the optimal strategies and Pareto optimal solution, and give the relationship between the optimal strategies. Furthermore, we study the optimization problem with the variance premium principle. When the total claim sizes are normally distributed, explicit expressions for the optimal strategies and Pareto optimal solution are obtained. Finally, some numerical examples are presented to show the impact of the major model parameters on the optimal results.

scholarly journals Evaluating the impact of market reforms on Value-at-Risk forecasts of Chinese A and B shares

2008 ◽  
Vol 16 (4) ◽  
pp. 453-475 ◽  
Author(s):  
Bernardo da Veiga ◽  
Felix Chan ◽  
Michael McAleer
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Market Reforms ◽  
The Impact

scholarly journals Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4147
Author(s):  
Krzysztof Echaust ◽  
Małgorzata Just
Keyword(s):  
At Risk ◽  
Crude Oil ◽  
Value At Risk ◽  
Tail Dependence ◽  
Oil Price ◽  
Health Crisis ◽  
Oil Market ◽  
Volatility Index ◽  
Conditional Copula ◽  
The Impact

This study investigates the dependence between extreme returns of West Texas Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well as the predictive power of OVX to generate accurate Value at Risk (VaR) forecasts for crude oil. We focus on the COVID-19 pandemic period as the most violate in the history of the oil market. The static and dynamic conditional copula methodology is used to measure the tail dependence coefficient (TDC) between the variables. We found a strong relationship in the tail dependence between negative returns on crude oil and OVX changes and the tail independence for positive returns. The time-varying copula discloses the strongest tail dependence of negative oil price shocks and the index changes during the COVID-19 health crisis. The findings indicate the ability of the OVX index to be a fear gauge with respect to the oil market. However, we cannot confirm the ability of OVX to improve one day-ahead forecasts of the Value at Risk. The impact of investors’ expectations embedded in OVX on VaR forecasts seems to be negligible.

scholarly journals How We Predict the Stability of Financial Sector: The Conditional Value at Risk Technique Approach

2020 ◽  
Author(s):  
David Kaluge
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Negative Impact ◽  
Stock Exchange ◽  
Banking System ◽  
Conditional Value At Risk ◽  
Negative Contribution ◽  
The Stability ◽  
The Impact

This study aims to identify the level of systemic risk of each bank and the financial linkages between banks in Indonesia. In this study, researcher uses 41 banks that have been actively traded on the Indonesia Stock Exchange in the period 2013-2018. The data of stock capitalization of banks are used as prices in a portfolio of banking system. The method used in this study is the CVaR (Conditional Value at Risk) method which was introduced by Adrian and Brunerrmeir in 2008. The equilibrium of the system is assumed reached at optimum portfolio of the system. At this situation each bank contribution to systemic risk is analyzed, as well as its impact onto it when there is a change in capitalization of a certain bank. The result shows the impact of bank onto systemic risk is not always follow its size in contribution the systemic risk. Due to covariance’s among banks are some positive and others are negative, some banks have negative contribution to systemic risk while others’ are positive. There are 4 banks that have different behavior. These banks have negative contribution to the systemic risk. These banks are BMRI, PNBN, PNBS and NAGA. The negative impact to systemic risk is dominated by BMRI as much as -0.17%, and by PNBN as much as -0.04%. There are 2 major banks that have contribution to systemic risk; BBCA (3,01% or Rp 59,1 trillion) and BBRI (0,54% Rp 10,62 trillion). However their impact on systemic risk are different. The parameters of impact on systemic for BBCA and BBRI are 14,99% and 52,94% respectively. Thus the stability of the system is more sensitive to the volatility of Bank Rakyat Indonesia (BBRI) than of Bank Central Asia (BBCA). Keywords: Systemic Risk, Financial Linkage, Value at Risk, Conditional Value at Risk, covariance banking

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.

The impact of liquidity on portfolio value-at-risk forecasts

2019 ◽  
Vol 52 (3) ◽  
pp. 242-259
Author(s):  
Jui-Cheng Hung ◽  
Jung-Bin Su ◽  
Matthew C. Chang ◽  
Yi-Hsien Wang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
The Impact
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