A Practical Guide to Market Risk Model Validations

2017 ◽  
Author(s):  
Vilen Abramov ◽  
Xianwen Zhou
Keyword(s):  
Risk Model ◽  
Market Risk ◽  
Practical Guide

Assessment of the size of VaR backtests for small samples

2020 ◽  
Vol 67 (2) ◽  
pp. 114-151
Author(s):  
Daniel Kaszyński ◽  
Bogumił Kamiński ◽  
Bartosz Pankratz
Keyword(s):  
Value At Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Methodological Approach ◽  
Market Risk ◽  
Small Sample ◽  
Small Samples ◽  
P Value ◽  
Typical Situation ◽  
Risk Management Process

The market risk management process includes the quantification of the risk connected with defined portfolios of assets and the diagnostics of the risk model. Value at Risk (VaR) is one of the most common market risk measures. Since the distributions of the daily P&L of financial instruments are unobservable, literature presents a broad range of backtests for VaR diagnostics. In this paper, we propose a new methodological approach to the assessment of the size of VaR backtests, and use it to evaluate the size of the most distinctive and popular backtests. The focus of the paper is directed towards the evaluation of the size of the backtests for small-sample cases – a typical situation faced during VaR backtesting in banking practice. The results indicate significant differences between tests in terms of the p-value distribution. In particular, frequency-based tests exhibit significantly greater discretisation effects than duration-based tests. This difference is especially apparent in the case of small samples. Our findings prove that from among the considered tests, the Kupiec TUFF and the Haas Discrete Weibull have the best properties. On the other hand, backtests which are very popular in banking practice, that is the Kupiec POF and Christoffersen’s Conditional Coverage, show significant discretisation, hence deviations from the theoretical size.

Risk Configuration of S&P 500 Industries: Sigma-risk and Alpha-risk Approximation

2017 ◽  
Vol 11 (2) ◽  
pp. 196-221
Author(s):  
Samet Günay
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Stock Return ◽  
Service Industry ◽  
Risk Model ◽  
Panel Data Analysis ◽  
Internal Variable ◽  
Market Risk ◽  
Alpha Risk ◽  
Industry Risk

Since the pioneering studies of Mandelbrot, a great deal of interest has arisen for the parameters of fractal finance theory. With this in mind, the present study attempts to examine the risk composition of S&P 500 index industries through panel data analysis. In the modelling of industries’ stock return risk, we use internal and external variables which are related to the companies’ financial ratios and stock market movements. In the first section (sigma-risk) of the study, we model the stock return risk through standard deviation, while in the second section (alpha-risk), the alpha parameter of stable distributions has been used for the same purpose. Panel data analysis results demonstrate that in the sigma-risk model for the healthcare industry, there is a significant internal variable (roa) that negatively affects the industry risk. However, for the alpha-risk model, some significant variables are obtained for the service industry. Another important finding is the changing level of market risk under the two models. While in the sigma-risk model, the magnitude of the market (external) risk variable is high, under the alpha-risk model, we have seen that market risk is relatively low despite the fact that it still has the highest effect on industry risk. JEL Classification: C33, G10, G30, G32

A Style-Based Market Risk Model for Hedge Fund Portfolios

2010 ◽  
Vol 36 (4) ◽  
pp. 124-131 ◽  
Author(s):  
Xuelong Zhou ◽  
Adam Litke ◽  
Michael Mclaughlin
Keyword(s):  
Risk Model ◽  
Hedge Fund ◽  
Market Risk

scholarly journals Major Crops Market Risk Based on Value at Risk Model in P.R. China

2018 ◽  
Vol 34 (2) ◽  
Author(s):  
Abdur Rehman ◽  
Wang Jian ◽  
Noor Khan ◽  
Raheel Saqib
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Model ◽  
Market Risk

scholarly journals Market Risk Model agricolture portfolio value-at-risk

2020 ◽  
Author(s):  
Giulio Carlone
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Model ◽  
Stock Exchange ◽  
Market Risk ◽  
Point Of View ◽  
Market Model ◽  
Basel Accords ◽  
Price Distortions ◽  
Portfolio Investments

Abstract Thinking about this current extreme scenario of stock exchange observed in a world scenario perspective and the related choices for worldbank portfolio investments in Agricolture commodity, this study its based in an advanced economic observation and analisys of the Agricolture commodity in a scenario of portfolio diversification without have the market risk default. This study its based in an advanced financial strategy to define the market model composed of London stock exchange agricolture commodity observed first in a London scenario and second in a Europe scenario and finally in a world scenario. The authorities regulation and the requirements used to define , the mathematical point of view and to describe , the market value at risk point of view , have been standardized in this empirical market model. The commodity scenario observed and the empirical market model defined to observe the max price distortions of the agricolture commodity defined and defined to observe the porfolio value at risk , are in this market model well described and standardized. Authorities are interested in the empirical market model to observe the VaR data because they are interested in a bank’s ability to withstand extreme events. VaR is monitored and is sanctioned by regulators defined in the Basel accords. The observed price are used in a variable choice of number of data price observation of five price for week a data price observation of one prices for week and a data price observation of two price for week and further similar strategies .

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