Related papers: An effective interest rate cap: a clarification
While optimal taxation theory provides clear prescriptions for tax design, translating these insights into actual tax codes remains difficult. Existing work largely offers theoretical characterizations of optimal systems, while practical…
In financial applications, reinforcement learning (RL) agents are commonly trained on historical data, where their actions do not influence prices. However, during deployment, these agents trade in live markets where their own transactions…
Constrained Reinforcement Learning (CRL) is a subset of machine learning that introduces constraints into the traditional reinforcement learning (RL) framework. Unlike conventional RL which aims solely to maximize cumulative rewards, CRL…
In this paper, we investigate the impact of mortgage rates on home prices, and how the impact may be used to help property purchase discussions at individual buyer level and to adjust home price indices across time. A mortgage-rate-adjusted…
This study investigates the impact of increased debt servicing costs on household consumption resulting from monetary policy tightening. It utilizes observational panel microdata on all mortgage holders in Israel and leverages…
This paper offers a new class of models of the term structure of interest rates. We allow each instantaneous forward rate to be driven by a different stochastic shock, constrained in such a way as to keep the forward rate curve continuous.…
The model of this paper gives a convenient strategy that a bank in the federal funds market can use in order to maximize its profit in a contemporaneous reserve requirement (CRR) regime. The reserve requirements are determined by the demand…
The aim of the present paper is to provide criteria for a central bank of how to choose among different monetary-policy rules when caring about a number of policy targets such as the output gap and expected inflation. Special attention is…
A significant challenge for the practical application of reinforcement learning in the real world is the need to specify an oracle reward function that correctly defines a task. Inverse reinforcement learning (IRL) seeks to avoid this…
Any solvency regime for financial institutions should be aligned with the fundamental objectives of regulation: protecting liability holders and securing the stability of the financial system. The first objective leads to consider…
The effects of the odd-even constraint - as an interleaver design criterion - on the performance of rate-1/2 binary turbo codes are revisited. According to the current understanding, its adoption is favored because it makes the information…
Recent work in reinforcement learning has focused on several characteristics of learned policies that go beyond maximizing reward. These properties include fairness, explainability, generalization, and robustness. In this paper, we define…
Response-Adaptive Randomization (RAR) is recognized for its potential to deliver improvements in patient benefit. However, the utility of RAR is contingent on regularization methods to mitigate early instability and preserve statistical…
In inverse reinforcement learning (IRL), a learning agent infers a reward function encoding the underlying task using demonstrations from experts. However, many existing IRL techniques make the often unrealistic assumption that the agent…
1. A recent unpublished manuscript whose conclusions were widely circulated in the electronic media (Zinman, 2009) claimed that Oregon 2007 payday loan (PL) rate-limiting regulations (hereafter, "Cap") have hurt borrowers. 2. The report's…
While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert's behavior. It is well known that, in…
Implicit Neural representations (INRs) are widely used for scientific data reduction and visualization by modeling the function that maps a spatial location to a data value. Without any prior knowledge about the spatial distribution of…
In online Inverse Reinforcement Learning (IRL), the learner can collect samples about the dynamics of the environment to improve its estimate of the reward function. Since IRL suffers from identifiability issues, many theoretical works on…
The interpretability of model has become one of the obstacles to its wide application in the high-stake fields. The usual way to obtain interpretability is to build a black-box first and then explain it using the post-hoc methods. However,…
This paper formulates and solves the optimal stopping problem for a loan made to one's self from a tax-advantaged retirement account such as a 401(k), 403(b), or 457(b) plan. If the plan participant has access to an external asset with a…