Market risk model selection and medium-term risk with limited data: Application to ocean tanker freight markets

2011 ◽  
Vol 20 (5) ◽  
pp. 258-268 ◽  
Author(s):  
Manolis G. Kavussanos ◽  
Dimitris N. Dimitrakopoulos
Keyword(s):  
Model Selection ◽  
Risk Model ◽  
Market Risk ◽  
Limited Data ◽  
Medium Term ◽  
Data Application

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 The partly parametric and partly nonparametric additive risk model

2021 ◽  
Author(s):  
Nils Lid Hjort ◽  
Emil Aas Stoltenberg
Keyword(s):  
Hazard Rate ◽  
Goodness Of Fit ◽  
Risk Model ◽  
Real Data ◽  
Rate Function ◽  
Nonparametric Model ◽  
Additive Risk Model ◽  
Data Application ◽  
Additive Risk ◽  
Hazard Factor

AbstractAalen’s linear hazard rate regression model is a useful and increasingly popular alternative to Cox’ multiplicative hazard rate model. It postulates that an individual has hazard rate function $$h(s)=z_1\alpha _1(s)+\cdots +z_r\alpha _r(s)$$ h ( s ) = z 1 α 1 ( s ) + ⋯ + z r α r ( s ) in terms of his covariate values $$z_1,\ldots ,z_r$$ z 1 , … , z r . These are typically levels of various hazard factors, and may also be time-dependent. The hazard factor functions $$\alpha _j(s)$$ α j ( s ) are the parameters of the model and are estimated from data. This is traditionally accomplished in a fully nonparametric way. This paper develops methodology for estimating the hazard factor functions when some of them are modelled parametrically while the others are left unspecified. Large-sample results are reached inside this partly parametric, partly nonparametric framework, which also enables us to assess the goodness of fit of the model’s parametric components. In addition, these results are used to pinpoint how much precision is gained, using the parametric-nonparametric model, over the standard nonparametric method. A real-data application is included, along with a brief simulation study.

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