The standard market risk model of the Swiss solvency test: an analytic solution

2019 ◽  
Vol 23 (2) ◽  
pp. 59-71 ◽  
Author(s):  
Andras Niedermayer
Keyword(s):  
Analytic Solution ◽  
Risk Model ◽  
Market Risk ◽  
Solvency Test ◽  
Swiss Solvency Test

Swiss Solvency Test

2005 ◽  
pp. 569-593 ◽  
Author(s):  
Phillip Keller ◽  
Thomas Luder ◽  
Mark Stober
Keyword(s):  
Solvency Test ◽  
Swiss Solvency Test

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
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