Risk factor aggregation and stress testing

References

• Alexander, C. and Sheedy, E., Developing a stress testing framework based on market risk models. J. Bank. Financ., 2008, 32(10), 2220–2236.
   Web of Science ®Google Scholar
• Anis, H.T. and Kwon, R.H., A sparse regression and neural network approach for financial factor modeling. Appl. Soft. Comput., 2021, 113, 107983.
   Web of Science ®Google Scholar
• Avellaneda, M., Hierarchical PCA and applications to portfolio management. Revista Mexicana de Economía y Finanzas, 2020, 15(1), 1–16.
   Google Scholar
• Avellaneda, M. and Lee, J.-H., Statistical arbitrage in the US equities market. Quant. Finance, 2010, 10(7), 761–782.
   Web of Science ®Google Scholar
• Bluhm, C., Overbeck, L. and Wagner, C., An Introduction to Credit Risk Modeling, 2003 (Chapman & Hall/CRC: London).
   Google Scholar
• Bollerslev, T., Generalized autoregressive conditional heteroskedasticity. J. Econom., 1986, 31(3), 307–327.
   Web of Science ®Google Scholar
• Bonti, G., Kalkbrener, M., Lotz, C. and Stahl, G., Credit risk concentrations under stress. J. Credit Risk, 2006, 2(3), 115–136.
   Google Scholar
• Bourlard, H. and Kamp, Y., Auto-association by multilayer perceptrons and singular value decomposition. Biol. Cybern., 1988, 59(4-5), 291–294.
   PubMed Web of Science ®Google Scholar
• Chang, X., Nie, F., Yang, Y., Zhang, C. and Huang, H., Convex sparse PCA for unsupervised feature learning. ACM Trans. Knowledge Discovery Data (TKDD), 2016, 11(1), 1–16.
   Web of Science ®Google Scholar
• Crosbie, B. and Bohn, J., Modelling default risk. Working paper, KMV Corporation, 2002. Available online at: www.kmv.com.
   Google Scholar
• Dogariu, M., Ştefan, L.-D., Boteanu, B.A., Lamba, C., Kim, B. and Ionescu, B., Generation of realistic synthetic financial time-series. ACM Trans. Multimedia Comput. Commun. Appl., 2022, 18(4), 1–27.
   Web of Science ®Google Scholar
• Dowd, K., Measuring Market Risk, 2002 (Wiley: Chichester).
   Google Scholar
• EBA, EU-Wide stress test – methodological note. Technical document, European Banking Authority, 2021a.
   Google Scholar
• EBA, EBA discussion paper on machine learning for IRB models. European Banking Authority, EBA/DP/2021/04, November 2021b.
   Google Scholar
• EC, Laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. European Commission, April 2021. Available online at: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52021PC0206.
   Google Scholar
• EC, Proposal for a regulation of the European parliament and of the council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts – Analysis of the final compromise text with a view to agreement. European Commission, January 2024.
   Google Scholar
• Enki, D.G., Trendafilov, N.T. and Jolliffe, I.T., A clustering approach to interpretable principal components. J. Appl. Stat., 2013, 40(3), 583–599.
   Web of Science ®Google Scholar
• Fenn, D.J., Porter, M.A., Williams, S., McDonald, M., Johnson, N.F. and Jones, N.S., Temporal evolution of financial-market correlations. Phys. Rev. E., 2011, 84(2), 026109.
   Web of Science ®Google Scholar
• Freeborough, W. and van Zyl, T., Investigating explainability methods in recurrent neural network architectures for financial time series data. Appl. Sci., 2022, 12(3), 1427.
   Google Scholar
• Frye, J., Principals of risk: Finding value-at-risk through factor-based interest rate scenarios. NationsBanc-CRT, April 1997.
   Google Scholar
• Goodfellow, I., Bengio, Y. and Courville, A., Deep Learning, 2016 (MIT Press: Cambridge).
   Google Scholar
• Gu, S., Kelly, B. and Xiu, D., Autoencoder asset pricing models. J. Econom., 2021, 222(1), 429–450.
   Web of Science ®Google Scholar
• Guhr, T., Müller-Groeling, A. and Weidenmüller, H.A., Random-matrix theories in quantum physics: Common concepts. Phys. Rep., 1998, 299(4-6), 189–425.
   Google Scholar
• Gupton, G., Finger, C. and Bhatia, M., CreditMetrics: technical document. Technical report, JP Morgan & Co., 1997.
   Google Scholar
• Guttman, L., Some necessary conditions for common-factor analysis. Psychometrika, 1954, 19(2), 149–161.
   Google Scholar
• Hinton, G.E. and Salakhutdinov, R.R., Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786), 504–507.
   PubMed Web of Science ®Google Scholar
• Hochreiter, S. and Schmidhuber, J., Long short-term memory. Neural. Comput., 1997, 9(8), 1735–1780.
   PubMed Web of Science ®Google Scholar
• Hotelling, H., Analysis of a complex of statistical variables into principal components. J. Educ. Psychol., 1933, 24(6), 417.
   Google Scholar
• James, G., Witten, D., Hastie, T. and Tibshirani, R., An Introduction to Statistical Learning, Vol. 2013 (Springer: New York).
   Google Scholar
• Jamshidian, F. and Zhu, Y., Scenario simulation: Theory and methodology. Finance Stochast., 1996, 1(1), 43–67.
   Google Scholar
• Jolliffe, I., Principal Component Analysis, 2nd ed., 2002 (Springer: New York).
   Google Scholar
• Jorion, P, Value At Risk: the New Benchmark for Managing Financial Risk, 2007 (The McGraw-Hill Companies Inc.: New York).
   Google Scholar
• Kent, J., Bibby, J. and Mardia, K., Multivariate Analysis, 1979 (Academic Press: Amsterdam).
   Google Scholar
• Kramer, M.A., Nonlinear principal component analysis using autoassociative neural networks. AIChE J., 1991, 37(2), 233–243.
   Web of Science ®Google Scholar
• Kupiec, P., Stress testing in a value at risk framework. J. Derivatives, 1998, 6, 7–24.
   Google Scholar
• Laloux, L., Cizeau, P., Potters, M. and Bouchaud, J.-P., Random matrix theory and financial correlations. Int. J. Theoretical Appl. Finance, 2000, 3(3), 391–397.
   Google Scholar
• Litterman, R. and Scheinkman, J., Common factors affecting bond returns. J. Fixed Income, 1991, 1(1), 54–61.
   Google Scholar
• Loretan, M., Generating market risk scenarios using principal components analysis: methodological and practical considerations. Manuscript, Federal Reserve Board, March 1997.
   Google Scholar
• Mao, K., Identifying critical variables of principal components for unsupervised feature selection. IEEE Trans. Syst. Man Cybernetics, Part B (Cybernetics), 2005, 35(2), 339–344.
   PubMed Web of Science ®Google Scholar
• Masaeli, M., Yan, Y., Cui, Y., Fung, G. and Dy, J.G., Convex principal feature selection. Paper presented at the 2010 SIAM International Conference on Data Mining, pp. 619–628, 2010 (SIAM: Philadelphia, PA).
   Google Scholar
• McNeil, A., Frey, R. and Embrechts, P., Quantitative Risk Management, 2005 (Princeton University Press: Princeton, NJ).
   Google Scholar
• McNeil, A., Frey, R. and Embrechts, P., Quantitative Risk Management, 2nd ed., 2015 (Princeton University Press: Princeton, NJ).
   Google Scholar
• Murphy, K.P., Probabilistic Machine Learning, 2022 (MIT Press: Cambridge, MA).
   Google Scholar
• Ni, H., Szpruch, L., Sabate-Vidales, M., Xiao, B., Wiese, M. and Liao, S., Sig-Wasserstein GANs for time series generation. Paper presented at the 2nd ACM International Conference on AI in Finance, 2021 (ACM: New York, NY).
   Google Scholar
• Packham, N. and Woebbeking, C.F., A factor-model approach for correlation scenarios and correlation stress testing. J. Bank. Finance., 2019, 101, 92–103.
   Web of Science ®Google Scholar
• Packham, N. and Woebbeking, F., Correlation scenarios and correlation stress testing. J. Econ. Behav. Organ., 2023, 205, 55–67.
   Web of Science ®Google Scholar
• Park, J., Müller, J., Arora, B., Faybishenko, B., Pastorello, G., Varadharajan, C., Sahu, R. and Agarwal, D., Long-term missing value imputation for time series data using deep neural networks. Neural Comput. Appl., 2022, 35, 9071–9091.
   Web of Science ®Google Scholar
• Pearson, K., LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Mag. J. Sci., 1901, 2(11), 559–572.
   Google Scholar
• Sagheer, A. and Kotb, M., Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems. Nature Sci. Rep., 2019, 9(1), 19038.
   PubMedGoogle Scholar
• Shiryaev, A.N., Probability, 2nd ed., 1996 (Springer: Berlin).
   Google Scholar
• Spilak, B. and Härdle, W.K., Portfolio tail-risk protection with non-linear latent factors. Working Paper, Humboldt University, June 2023. Available online at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4474100.
   Google Scholar
• Ward Jr, J.H., Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc., 1963, 58(301), 236–244.
   Web of Science ®Google Scholar
• Zhang, A., Lipton, Z.C., Li, M. and Smola, A.J., Dive Into Deep Learning, 2024 (Cambridge University Press: Cambridge, UK).
   Google Scholar