arXiv research

Optimal online linear regression in dynamic environments using discounted Vovk-Azoury-Warmuth forecaster.

A new algorithm for non-stationary linear bandits with improved regret bound.

The study uses reproducing kernels to model bond discount curves.

Paper introduces non-linear discounting models for default compensation and climate valuation.

New algorithm reduces online regression error in RKHS.

Study on CEF discount in Bangladesh, finds size and maturity impact, turnover negative.

Framework for transferring discount curve estimates across fixed-income product classes.

New theory extends LQ control to non-exponential discount scenarios.

New algorithms for GLMs adapt to non-stationary contexts.

AGBoost uses attention weights to improve GBM for regression problems.

An uncollateralized swap hedged back-to-back by a CCP swap is used to introduce FVA. The open IR01 of FVA, however, is a sure sign of risk not being fully hedged, a theoretical no-arbitrage pricing concern, and a bait to lure market risk capital, a practical business concern. By dynamically trading the CCP swap, with t…

In this paper we extend the existing literature on xVA along three directions. First, we enhance current BSDE-based xVA frameworks to include initial margin in presence of defaults. Next, we solve the consistency problem that arises when the front-office desk of the bank uses trade-specific discount curves (CSA discoun…

Improved RL algorithm with linear MDPs for offline learning with partial data coverage.

New algorithm reduces reinforcement learning regret to sqrt(T) without strong dynamics assumptions.

New algorithm minimizes regret in multi-armed bandits with network interference.

Two randomized algorithms improve performance in non-stationary linear bandits.

We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumulative discounted surplus. For this setting, first, we propose a novel algorithm that never decreases …

Paper tackles time inconsistency in portfolio management with stochastic volatility and power utility.

We present a non-parametric method to estimate the discount curve from market quotes based on the Moore-Penrose pseudoinverse. The discount curve reproduces the market quotes perfectly, has maximal smoothness, and is given in closed-form. The method is easy to implement and requires only basic linear algebra operations…

Adaptive policies solve a linear program to maximize rewards while minimizing costs in sales with discounts.

A firm with heterogeneous shareholders optimizes dividends under ambiguity aggregation.

Deep neural networks decompose SDF into linear and nonlinear components.

We consider a general class of non-linear Bellman equations. These open up a design space of algorithms that have interesting properties, which has two potential advantages. First, we can perhaps better model natural phenomena. For instance, hyperbolic discounting has been proposed as a mathematical model that matches …

New algorithm for nonstationary GLBs reduces computation and memory costs.

Paper proposes an efficient RL algorithm for discounted MDPs using feature mapping.

In this paper, we settle the sampling complexity of solving discounted two-player turn-based zero-sum stochastic games up to polylogarithmic factors. Given a stochastic game with discount factor $γ\in(0,1)$ we provide an algorithm that computes an $ε$-optimal strategy with high-probability given $\tilde{O}((1 - γ)^{-3}…

New framework for reinforcement learning with sporadic state observations.

A new Twitter sentiment model predicts stock market trends with high accuracy.

Paper develops a discounted algorithm for online convex optimization that adapts to unknown discount factors.

New algorithms identify best policies in discounted linear MDPs efficiently.

New method for optimistic planning in MDPs using regularization.

This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrix-variate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multivariate beta distribu…

The paper presents a model-free method for stabilizing unknown control systems.

In this paper, we investigate the Merton portfolio management problem in the context of non-exponential discounting. This gives rise to time-inconsistency of the decision-maker. If the decision-maker at time t=0 can commit his/her successors, he/she can choose the policy that is optimal from his/her point of view, and …

Study analyzes how discounts affect train ticket purchases and rescheduling in Switzerland.

We demonstrate a limitation of discounted expected utility, a standard approach for representing the preference to risk when future cost is discounted. Specifically, we provide an example of the preference of a decision maker that appears to be rational but cannot be represented with any discounted expected utility. A …

Proves lower discount rates are needed for future losses.

Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process. The discount factor values future rewards by an exponential scheme that leads to theoretical convergence guarantees of the Bellman equation. However, evidence from psychology, economics and neuroscience suggests that…

There is an observed basis between repo discounting, implied from market repo rates, and bond discounting, stripped from the market prices of the underlying bonds. Here, this basis is explained as a convexity effect arising from the decorrelation between the discount rates for derivatives and bonds. Using a Hull-White …

Proposes a new framework for discount models.

Study shows TD(0) with linear approx. converges for reversible Markov chains.

The paper uses regression trees/random forests to price Bermudan options more efficiently.

Revises derivative pricing post financial crisis by defining a discount rate.

This paper shows how forward rate interpolations are equivalent to discount factor interpolations in yield curve construction.

This paper analyzes RMR under Markov-dependent samples, improving understanding of its generalization error.

A new algorithm finds optimal solutions for constrained decision processes.

Study optimal stopping problems with finite-time horizon and proves continuity and strict monotonicity of the boundary.

New RL approach handles non-exponential discounting for sequential decisions.