References

References

Bibliography and related work.

Credit Risk Modeling

Structural Models:

  • Merton, R. C. (1974). On the Pricing of Corporate Debt: The Risk Structure of Interest Rates. Journal of Finance, 29(2), 449-470.

  • Black, F., & Cox, J. C. (1976). Valuing Corporate Securities: Some Effects of Bond Indenture Provisions. Journal of Finance, 31(2), 351-367.

Reduced-Form Models:

  • Jarrow, R. A., & Turnbull, S. M. (1995). Pricing Derivatives on Financial Securities Subject to Credit Risk. Journal of Finance, 50(1), 53-85.

  • Duffie, D., & Singleton, K. J. (1999). Modeling Term Structures of Defaultable Bonds. Review of Financial Studies, 12(4), 687-720.

Portfolio Credit Risk

Factor Models:

  • Gordy, M. B. (2003). A Risk-Factor Model Foundation for Ratings-Based Bank Capital Rules. Journal of Financial Intermediation, 12(3), 199-232.

  • McNeil, A. J., & Wendin, J. P. (2007). Bayesian Inference for Generalized Linear Mixed Models of Portfolio Credit Risk. Journal of Empirical Finance, 14(2), 131-149.

Copula Models:

  • Li, D. X. (2000). On Default Correlation: A Copula Function Approach. Journal of Fixed Income, 9(4), 43-54.

  • Schonbucher, P. J., & Schubert, D. (2001). Copula-Dependent Default Risk in Intensity Models. Working Paper.

Deep Learning for Finance

Variational Autoencoders:

  • Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. ICLR 2014.

  • Sohn, K., Lee, H., & Yan, X. (2015). Learning Structured Output Representation using Deep Conditional Generative Models. NeurIPS 2015.

Transformers:

  • Vaswani, A., et al. (2017). Attention Is All You Need. NeurIPS 2017.

  • Li, S., et al. (2019). Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. NeurIPS 2019.

Diffusion Models:

  • Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS 2020.

  • Song, Y., et al. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. ICLR 2021.

Time Series Generation

  • Yoon, J., Jarrett, D., & van der Schaar, M. (2019). Time-series Generative Adversarial Networks. NeurIPS 2019.

  • Rasul, K., et al. (2021). Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. ICML 2021.

  • Lim, B., et al. (2021). Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting. International Journal of Forecasting, 37(4), 1748-1764.

Structured Finance

CLO/CDO Modeling:

  • Duffie, D., & Garleanu, N. (2001). Risk and Valuation of Collateralized Debt Obligations. Financial Analysts Journal, 57(1), 41-59.

  • Hull, J., & White, A. (2004). Valuation of a CDO and an n-th to Default CDS Without Monte Carlo Simulation. Journal of Derivatives, 12(2), 8-23.

Loss Distribution:

  • Vasicek, O. A. (2002). The Distribution of Loan Portfolio Value. Risk, 15(12), 160-162.

  • Pykhtin, M., & Dev, A. (2002). Credit Risk in Asset Securitisations: Analytical Model. Risk, 15(5), S16-S20.

Regulatory Framework

  • Basel Committee on Banking Supervision (2006). International Convergence of Capital Measurement and Capital Standards (Basel II).

  • Basel Committee on Banking Supervision (2017). Basel III: Finalising Post-Crisis Reforms.

  • European Banking Authority (2018). Guidelines on the Application of the Definition of Default.

Software and Data

PyTorch:

  • Paszke, A., et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. NeurIPS 2019.

Pandas:

  • McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference.

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