ufRisk {ufRisk}

ufRisk: A package for user friendly and practical usage of various backtesting methods.

Description

The goal of the ufRisk package (univariate financial risk) is to enable the user to compute one-step ahead forecasts of Value at Risk (VaR) and Expected Shortfall (ES) by means of various parametric and semiparametric GARCH-type models. For the latter the estimation of the nonparametric scale function is carried out by means of a data-driven smoothing approach. Currently the GARCH, the exponential GARCH (EGARCH), the Log-GARCH, the asymmetric power ARCH (APARCH), the FIGARCH and FI-Log-GARCH can be employed within the scope of ufRisk. Model quality, in terms of forecasting VaR and ES, can be assessed by means of various backtesting methods.

Functions

varcast is a function to calculate rolling one-step ahead forecasts of VaR and ES for a selection of parametric and semiparametric GARCH-type models (see also varcast).

trafftest is a function for backtesting VaR and ES. ES is backtested via a newly developed traffic light approach. (see also trafftest).

covtest is a function for conducting the conditional and the unconditional coverage tests introduced by Kupiec (1995) and Christoffersen (1998). (see also covtest).

Author(s)

• Yuanhua Feng (Department of Economics, Paderborn University),
  Author of the Algorithms
  Website: https://wiwi.uni-paderborn.de/en/dep4/feng/

• Xuehai Zhang (Former research associate at Paderborn University),
  Author
  

• Shujie Li (Scientific Employee) (Department of Economics, Paderborn University),
  Author
  

• Christian Peitz (Department of Economics, Paderborn University),
  Author
  

• Dominik Schulz (Scientific Employee) (Department of Economics, Paderborn University),
  Author
  

• Sebastian Letmathe (Scientific Employee) (Department of Economics, Paderborn University),
  Package Creator and Maintainer

References

Basel Committee on Banking Supervision (1996). Supervisory Framework For The Use of Back-Testing in Conjunction With The Internal Models Approach to Market Risk Capital Requirements. Available online: https://www.bis.org/publ/bcbs22.htm (accessed on 23 June 2020).

Beran, J., and Feng, Y. (2002). Local polynomial fitting with long-memory, short-memory and antipersistent errors. Annals of the Institute of Statistical Mathematics, 54(2), pp. 291-311.

Constanzino, N., and Curran, M. (2018). A Simple Traffic Light Approach to Backtesting Expected Shortfall. In: Risks 6.1.2.

Feng, Y. (2004). Simultaneously modeling conditional heteroskedasticity and scale change. In: Econometric Theory, pp. 563-596.

Feng, Y., Beran, J., Letmathe, S., & Ghosh, S. (2020). Fractionally integrated Log-GARCH with application to value at risk and expected shortfall (No. 137). Paderborn University, CIE Center for International Economics.

Letmathe, S., Feng, Y., & Uhde, A. (2021). Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall (No. 141). Paderborn University, CIE Center for International Economics.

McNeil, A.J., Frey, R., and Embrechts, P. (2015). Quantitative risk management: concepts, techniques and tools - revised edition. Princeton University Press.