Deep Replication of a Runoff Portfolio

To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. This article presents the key notions of Deep Asset Liability Management (Deep~ALM) for a technological transformation in the management of assets and liabilities along a whole term structure. The approach has a profound impact on a wide range of applications such as optimal decision making for treasurers, optimal procurement of commodities or the optimisation of hydroelectric power plants. As a by-product, intriguing aspects of goal-based investing or Asset Liability Management (ALM) in abstract terms concerning urgent challenges of our society are expected alongside. We illustrate the potential of the approach in a stylised case.

[1]  R. C. Merton,et al.  On the Pricing of Corporate Debt: The Risk Structure of Interest Rates , 1974, World Scientific Reference on Contingent Claims Analysis in Corporate Finance.

[2]  William T. Ziemba,et al.  A Bank Asset and Liability Management Model , 1986, Oper. Res..

[3]  Martin L. Leibowitz,et al.  Investing: The Collected Works of Martin L. Leibowitz , 1992 .

[4]  M. Avellaneda,et al.  Pricing and hedging derivative securities in markets with uncertain volatilities , 1995 .

[5]  N. Karoui,et al.  Dynamic Programming and Pricing of Contingent Claims in an Incomplete Market , 1995 .

[6]  S. Eddy Hidden Markov models. , 1996, Current opinion in structural biology.

[7]  S. Browne Reaching goals by a deadline: digital options and continuous-time active portfolio management , 1996, Advances in Applied Probability.

[8]  Michael A. H. Dempster,et al.  Dynamic Stochastic Programming for Asset-Liability Management , 1998 .

[9]  Francis A. Longstaff,et al.  Valuing American Options by Simulation: A Simple Least-Squares Approach , 2001 .

[10]  Sunil K. Sharma,et al.  Emerging Issues in Banking Regulation , 2003, SSRN Electronic Journal.

[11]  Karl Frauendorfer,et al.  Management of non-maturing deposits by multistage stochastic programming , 2003, Eur. J. Oper. Res..

[12]  D. Heath,et al.  A Benchmark Approach to Quantitative Finance , 2006 .

[13]  Markus Lang,et al.  Why Taxing Executives' Bonuses Can Foster Risk-Taking Behavior , 2011 .

[14]  C. Schwab,et al.  Computational Methods for Quantitative Finance: Finite Element Methods for Derivative Pricing , 2013 .

[15]  Jan Peters,et al.  Reinforcement learning in robotics: A survey , 2013, Int. J. Robotics Res..

[16]  R. Ryan The Evolution of Asset/Liability Management , 2013 .

[17]  Stefan W. Schmitz,et al.  The Impact of the Liquidity Coverage Ratio (LCR) on the Implementation of Monetary Policy , 2013 .

[18]  K. Hess,et al.  The Good and Bad News about the New Liquidity Rules of Basel III in Western European Countries , 2012 .

[19]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[20]  Pierre Brugiere,et al.  Machine Learning in Finance , 2016 .

[21]  Andres Hernandez Model Calibration with Neural Networks , 2016 .

[22]  J. Teichmann,et al.  A fundamental theorem of asset pricing for continuous time large financial markets in a two filtration setting , 2017, 1705.02087.

[23]  Dhruvil Trivedi,et al.  Machine Learning in Finance , 2018, 2018 IEEE Punecon.

[24]  J. Teichmann,et al.  Deep hedging , 2018, Quantitative Finance.

[25]  Marcos M. López de Prado,et al.  Advances in Financial Machine Learning: Numerai's Tournament (seminar slides) , 2018, SSRN Electronic Journal.

[26]  Demis Hassabis,et al.  A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play , 2018, Science.

[27]  M. Spillmann,et al.  Asset Liability Management (ALM) in Banken , 2019 .

[28]  Sebastian Becker,et al.  Deep Optimal Stopping , 2018, J. Mach. Learn. Res..

[29]  J. Ruf,et al.  Neural Networks for Option Pricing and Hedging: A Literature Review , 2019, The Journal of Computational Finance.

[30]  Victor Talpaert,et al.  Deep Reinforcement Learning for Autonomous Driving: A Survey , 2020, IEEE Transactions on Intelligent Transportation Systems.