Tax reforms and the capital structure of banks

This paper studies the link between corporate income tax (CIT) reforms and domestic banks’ financing decisions. We use a dataset of CIT reforms and estimate the effect of tax rate changes on leverage, dividend policies and earnings management of banks. The results suggest that taxation influences all three variables. Leverage increases with the CIT rate in the first three years after the reform. The reason is that the statutory CIT rate determines the value of the debt tax shield. A higher tax rate increases incentives to use debt finance when interest payments are deductible from the CIT base. The tax effects we find are statistically and economically significant but considerably lower than those found in previous research. Also, dividend pay-outs increase after an increase in CIT rates. This could indicate that banks actively manage their pay-out policies around tax reforms and adjust their capital structure with changes in dividends. Furthermore, banks increase loss loan reserves in anticipation of tax rate cuts since losses become less valuable with lower CIT rates.     

Consistent recalibration of yield curve models

The analytical tractability of affine (short rate) models, such as the Vasi?ek and the Cox–Ingersoll–Ross (CIR) models, has made them a popular choice for modeling the dynamics of interest rates. However, in order to properly account for the dynamics of real data, these models must exhibit time‐dependent or even stochastic parameters. This breaks their tractability, and modeling and simulating become an arduous task. We introduce a new class of Heath–Jarrow–Morton (HJM) models that both fit the dynamics of real market data and remain tractable. We call these models consistent recalibration (CRC) models. CRC models appear as limits of concatenations of forward rate increments, each belonging to a Hull–White extended affine factor model with possibly different parameters. That is, we construct HJM models from “tangent” affine models. We develop a theory for continuous path versions of such models and discuss their numerical implementations within the Vasi?ek and CIR frameworks.     

A NOTE ON NONAFFINE SOLUTIONS OF TERM STRUCTURE EQUATIONS WITH APPLICATIONS TO POWER EXCHANGES

A method based on transformations of well-known solutions of term structure equations is presented in order to incorporate Martin Barlow's spot price model for electricity into a model for future prices on electricity. The setting for the evolution of term structures is chosen in the spirit of Da Prato and Zabczyk.     

Deep hedging

We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, liquidity constraints or risk limits using modern deep reinforcement machine learning methods. We discuss how standard reinforcement learning methods can be applied to non-linear reward structures, i.e. in our case convex risk measures. As a general contribution to the use of deep learning for stochastic processes, we also show in Section 4 that the set of constrained trading strategies used by our algorithm is large enough to ε-approximate any optimal solution. Our algorithm can be implemented efficiently even in high-dimensional situations using modern machine learning tools. Its structure does not depend on specific market dynamics, and generalizes across hedging instruments including the use of liquid derivatives. Its computational performance is largely invariant in the size of the portfolio as it depends mainly on the number of hedging instruments available. We illustrate our approach by an experiment on the S&P500 index and by showing the effect on hedging under transaction costs in a synthetic market driven by the Heston model, where we outperform the standard ‘complete-market’ solution.