scholarly journals Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management

2004 ◽  
Author(s):  
M. Hashem Pesaran ◽  
P. Zaffaroni
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Value At Risk ◽  
Model Averaging ◽  
Volatility Models ◽  
Asset Volatility

Volatility Forecast by Volatility Index and Its Use as a Risk Management Tool Under a Value-at-Risk Approach

2018 ◽  
Vol 21 (02) ◽  
pp. 1850010 ◽  
Author(s):  
Yam Wing Siu
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Risk Premium ◽  
Value At Risk ◽  
Realized Volatility ◽  
Management Tool ◽  
Spot Price ◽  
Pricing Options ◽  
Significance Levels ◽  
Risk Approach

This paper examines the predicting power of the volatility indexes of VIX and VHSI on the future volatilities (or called realized volatility, [Formula: see text] of their respective underlying indexes of S&P500 Index, SPX and Hang Seng Index, HSI. It is found that volatilities indexes of VIX and VHSI, on average, are numerically greater than the realized volatilities of SPX and HSI, respectively. Further analysis indicates that realized volatility, if used for pricing options, would, on some occasions, result in greatest losses of 2.21% and 1.91% of the spot price of SPX and HSI, respectively while the greatest profits are 2.56% and 2.93% of the spot price of SPX and HSI, respectively, making it not an ideal benchmark for validating volatility forecasting techniques in relation to option pricing. Hence, a new benchmark (fair volatility, [Formula: see text] that considers the premium of option and the cost of dynamic hedging the position is proposed accordingly. It reveals that, on average, options priced by volatility indexes contain a risk premium demanded by the option sellers. However, the options could, on some occasions, result in greatest losses of 4.85% and 3.60% of the spot price of SPX and HSI, respectively while the greatest profits are 4.60% and 5.49% of the spot price of SPX and HSI, respectively. Nevertheless, it can still be a valuable tool for risk management. [Formula: see text]-values of various significance levels for value-at-risk and conditional value-at-value have been statistically determined for US, Hong Kong, Australia, India, Japan and Korea markets.

scholarly journals Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

2017 ◽  
Vol 28 (75) ◽  
pp. 361-376 ◽  
Author(s):  
Leandro dos Santos Maciel ◽  
Rosangela Ballini
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Models ◽  
Time Interval ◽  
Risk Modeling ◽  
Additional Information ◽  
Volatility Models ◽  
Modeling And Forecasting ◽  
The Difference ◽  
Market Indexes

ABSTRACT This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

scholarly journals Risk management of risk under the Basel Accord: forecasting value‐at‐risk of VIX futures

2011 ◽  
Vol 37 (11) ◽  
pp. 1088-1106 ◽  
Author(s):  
Chia‐lin Chang ◽  
Juan‐Ángel Jiménez‐Martín ◽  
Michael McAleer ◽  
Teodosio Pérez‐Amaral
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Value At Risk ◽  
Basel Accord ◽  
Vix Futures ◽  
Management Of Risk

Optimal Reciprocal Reinsurance under VaR or TVaR Constraint

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hung-Hsi Huang ◽  
Ching-Ping Wang
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Moral Hazard ◽  
Value At Risk ◽  
Incentive Compatibility ◽  
Regular Form ◽  
Optimal Reinsurance ◽  
The Common ◽  
Risk Constraint ◽  
Management Requirement

Abstract Most existing researches on optimal reinsurance contract are based on an insurer’s viewpoint. However, the optimal reinsurance contract for an insurer is not necessarily to be optimal for a reinsurer. Hence, this study aims to develop the optimal reciprocal reinsurance which satisfies the benefits of both the insurer and reinsurer. Additionally, due to legislative restriction or risk management requirement, the wealth of insurer and reinsurer are frequently imposed upon a VaR (Value-at-Risk) or TVaR (Tail Value-at-Risk) constraint. Therefore, this study develops an optimal reciprocal reinsurance contract which maximizes the common benefits (evaluated by weighted addition of expected utilities) of the insurer and reinsurer subject to their VaR or TVaR constraints. Furthermore, for avoiding moral hazard problem, the developed contract is additionally restricted to a regular form or incentive compatibility (both indemnity schedule and retained loss schedule are continuously nondecreasing).

The Value-at-Risk Methodology of Basel II and Basel III

2014 ◽  
pp. 100-114 ◽  
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Value At Risk ◽  
Basel Ii ◽  
Risk Measurement ◽  
Capital Adequacy ◽  
Basel Iii ◽  
Regulatory Capital ◽  
Advantages And Disadvantages ◽  
Risk Methodology

This chapter examines the advantages and disadvantages of the risk estimate approach—Value-at-Risk (VaR) which has been extensively embraced by regulators and practitioners in financial markets under the Basel II & III framework as the basis of risk measurement, both for the purpose of ensuring regulatory capital adequacy, and risk management and strategic planning at industry level.

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.

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