CHRIS MARRISON, TIL SCHUERMANN and JOHN D. STROUGHAIR
Since 1996, the Bank for International Setdements (BIS) has set the capital level that banks must hold against market risks by a specific formula. This article presents a…
Abstract
Since 1996, the Bank for International Setdements (BIS) has set the capital level that banks must hold against market risks by a specific formula. This article presents a practical approach for incorporating the effects of asset illiquidity and management response lags in setting regulatory capital levels to account for market risk. According to the BIS guidelines, capital should be a function of the effectiveness of limit management and market liquidity, because actively managing limits and positions can significantly reduce the risk of a trading operation. Although this approach represents an improvement over previous methods of setting capital, significant limitations still remain, namely, liquidity constraints and response lags in management intervention, which increase portfolio risk. The authors suggest specific amendments to the reg‐ulatory capital guidelines that may mitigate both of these limitations
Francis X. Diebold, Til Schuermann and John D. Stroughair
Extreme value theory (EVT) holds promise for advancing the assessment and management of extreme financial risks. Recent literature suggests that the application of EVT generally…
Abstract
Extreme value theory (EVT) holds promise for advancing the assessment and management of extreme financial risks. Recent literature suggests that the application of EVT generally results in more precise estimates of extreme quantiles and tail probabilities of financial asset returns. This article assesses EVT from the perspective of financial risk management. The authors believe that the recent optimism regarding EVT may be appropriate but exaggerated, and that much of its potential remains latent. They support their claim by describing various pitfalls associated with the current use of EVT techniques, and illustrate how these can be avoided. In conclusion, the article defines several specific research directions that may further the practical and effective application of EVT to risk management.
SERGIO M. FOCARDI and FRANK J. FABOZZI
Fat‐tailed distributions have been found in many financial and economic variables ranging from forecasting returns on financial assets to modeling recovery distributions in…
Abstract
Fat‐tailed distributions have been found in many financial and economic variables ranging from forecasting returns on financial assets to modeling recovery distributions in bankruptcies. They have also been found in numerous insurance applications such as catastrophic insurance claims and in value‐at‐risk measures employed by risk managers. Financial applications include:
Martin Odening and Jan Hinrichs
This study examines problems that may occur when conventional Value‐at‐Risk (VaR) estimators are used to quantify market risks in an agricultural context. For example, standard…
Abstract
This study examines problems that may occur when conventional Value‐at‐Risk (VaR) estimators are used to quantify market risks in an agricultural context. For example, standard VaR methods, such as the variance‐covariance method or historical simulation, can fail when the return distribution is fat tailed. This problem is aggravated when long‐term VaR forecasts are desired. Extreme Value Theory (EVT) is proposed to overcome these problems. The application of EVT is illustrated by an example from the German hog market. Multi‐period VaR forecasts derived by EVT are found to deviate considerably from standard forecasts. We conclude that EVT is a useful complement to traditional VaR methods.
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ALEXANDER MUERMANN and ULKU OKTEM
Over recent decades, banks and bank regulators have devoted substantial resources to managing market risk and credit risk. More recently industry and regulatory focus has shifted…
Abstract
Over recent decades, banks and bank regulators have devoted substantial resources to managing market risk and credit risk. More recently industry and regulatory focus has shifted to the mitigation of operational risk. This article addresses the Advanced Measurement Approaches under which banks would be allowed to determine capital requirements, based on their own internal assessment of operational risk, according to standards set by the Basel Committee. The authors propose adopting the concept of “nearmiss” risk assessment employed in the chemical, health, and airline industries to internally evaluate operational risk.
This article uses a Value‐at‐Risk approach to derive an estimator of the failure probability of a financial institution. The proposed approach can be applied to any profit/loss…
Abstract
This article uses a Value‐at‐Risk approach to derive an estimator of the failure probability of a financial institution. The proposed approach can be applied to any profit/loss distribution, although Extreme Value (EV) theory also tells us that the most appropriate distributions are EV. The estimator suggested here is superior to the “Z” indicator of failure risk, which is sometimes used in the literature. Illustrative results confirm that the distribution selected makes a considerable difference to the results, and that estimates of failure probabilities based on the assumption of normality are too low to be valid.
This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large…
Abstract
Purpose
This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances.
Design/methodology/approach
The implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios.
Findings
In particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios.
Originality/value
The optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.
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This paper aims to empirically test, from a regulatory portfolio management standpoint, the application of liquidity-adjusted risk techniques in the process of getting optimum and…
Abstract
Purpose
This paper aims to empirically test, from a regulatory portfolio management standpoint, the application of liquidity-adjusted risk techniques in the process of getting optimum and investable economic-capital structures in the Gulf Cooperation Council financial markets, subject to applying various operational and financial optimization restrictions under crisis outlooks.
Design/methodology/approach
The author implements a robust methodology to assess regulatory economic-capital allocation in a liquidity-adjusted value at risk (LVaR) context, mostly from the standpoint of investable portfolios analytics that have long- and short-sales asset allocation or for those portfolios that contain long-only asset allocation. The optimization route is accomplished by controlling the nonlinear quadratic objective risk function with certain regulatory constraints along with LVaR-GARCH-M (1,1) procedure to forecast conditional risk parameters and expected returns for multiple asset classes.
Findings
The author’s conclusions emphasize that the attained investable economic-capital portfolios lie-off the efficient frontier, yet those long-only portfolios seem to lie near the efficient frontier than portfolios with long- and short-sales assets allocation. In effect, the newly observed market microstructures forms and derived deductions were not apparent in prior research studies (Al Janabi, 2013).
Practical implications
The attained empirical results are quite interesting for practical portfolio optimization, within the environments of big data analytics, reinforcement machine learning, expert systems and smart financial applications. Furthermore, it is quite promising for multiple-asset portfolio management techniques, performance measurement and improvement analytics, reinforcement machine learning and operations research algorithms in financial institutions operations, above all after the consequences of the 2007–2009 financial crisis.
Originality/value
While this paper builds on Al Janabi’s (2013) optimization algorithms and modeling techniques, it varies in the sense that it covers the outcomes of a multi-asset portfolio optimization method under severe event market scenarios and by allowing for both long-only and combinations of long-/short-sales multiple asset. The achieved empirical results, optimization parameters and efficient and investable economic-capital figures were not apparent in Al Janabi’s (2013) paper because the prior evaluation were performed under normal market circumstances and without bearing in mind the impacts of the 2007–2009 global financial crunch.
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Abbas Khan, Muhammad Yar Khan, Abdul Qayyum Khan, Majid Jamal Khan and Zia Ur Rahman
By testing the weak form of efficient market hypothesis (EMH) this study aims to forecast the short-term stock prices of the US Dow and Jones environmental socially responsible…
Abstract
Purpose
By testing the weak form of efficient market hypothesis (EMH) this study aims to forecast the short-term stock prices of the US Dow and Jones environmental socially responsible index (SRI) and Shariah compliance index (SCI).
Design/methodology/approach
This study checks the validity of the weak form of EMH for both SCI and SRI prices by using different parametric and non-parametric tests, i.e. augmented Dickey-Fuller test, Philip-Perron test, runs test and variance ratio test. If the EMH is invalid, the research further forecasts short-term stock prices by applying autoregressive integrated moving average (ARIMA) model using daily price data from 2010 to 2018.
Findings
The research confirms that a weak form of EMH is not valid in the US SRI and SCI. The historical data can predict short-term future price movements by using technical ARIMA model.
Research limitations/implications
This study provides better guidance to risk-averse national and international investors to earn higher returns in the US SRI and SCI. This study can be extended to test the EMH of Islamic equity in the Middle East and North Africa region and other top Islamic indexes in the world.
Originality/value
This study is a new addition to the existing literature of equity investment and price forecasting by comparing and investigating the market efficiency of two interrelated US SRI and SCI.