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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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2.4%4.9%7.3%9.8% · Jun 201419922001200920182026
48 results for discounting factor

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

problem The challenge of choosing between different interpolation methods for yield curve construction.
method Demonstrates the equivalence between forward rate interpolations and discount factor interpolations.
result Some popular interpolation methods on forward rates are equivalent to classical interpolation methods on discount factors.

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

problem Developing an algorithm that can adapt to an unknown discount factor in online convex optimization.
method Smoothed Online Gradient Descent (SOGD) with Discounted-Normal-Predictor (DNP).
result Achieves a uniform O(logT/1λ)O(\sqrt{\log T/1-λ}) discounted regret across a continuous interval of discount factors.

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

problem Valuation of non-replicable value and damage under default risk.
method Develops two models: one for risk-neutralising discounting and another for survival probability dependent discounting.
result Non-decaying discount factors (negative discount rates) are possible under certain scenarios.

Optimizes spending by adjusting a discount factor modelled as an exponential CIR process.

problem Maximizing discounted spendings/dividend payments given an exponential CIR discounting factor.
method Analytical and numerical methods for deterministic and stochastic surplus processes.
result Explicit expressions for optimal strategies in deterministic cases, and constant-barrier strategies for small volatility in stochastic cases.

A new method maps value estimates to logarithmic space to enable lower discount factors in reinforcement learning.

problem The poor performance of low discount factors in reinforcement learning.
method Introducing a logarithmic mapping to value estimates.
result The method enables lower discount factors, solving challenging reinforcement learning problems.

The valuation process that economic agents undergo for investments with uncertain payoff typically depends on their statistical views on possible future outcomes, their attitudes toward risk, and, of course, the payoff structure itself. Yields vary across different investment opportunities and their interrelations are …

2010-01-08abs ↗pdf ↗

Asset prices contain information about the probability distribution of future states and the stochastic discounting of those states as used by investors. To better understand the challenge in distinguishing investors' beliefs from risk-adjusted discounting, we use Perron-Frobenius Theory to isolate a positive martingal…

2014-11-28abs ↗pdf ↗

This paper improves MARL for networked systems through new protocols and discount factors.

problem Improving control in networked systems using multi-agent reinforcement learning.
method Formulated as a spatiotemporal Markov decision process, introduced a spatial discount factor, and proposed NeurComm.
result Appropriate spatial discount factor enhances learning curves of non-communicative MARL algorithms.

A study finds that only a few factors explain corporate bond risk, rendering extensive bond factor literature redundant.

problem The redundancy of extensive bond factor literature in explaining corporate bond risk premia.
method Bayesian Model Averaging Stochastic Discount Factor analysis of 18 quadrillion models.
result A Bayesian Model Averaging SDF explains risk premia better than low-dimensional models, with an out-of-sample Sharpe ratio of 1.5 to 1.8.

New Q-learning algorithm reduces sample complexity for large discount factors.

problem Large discount factors make Q-learning algorithms inefficient.
method Introduces a new Q-learning algorithm with uniformly bounded sample complexity.
result The new algorithm achieves asymptotic covariance that is a quadratic in 1/(1ργ)1/(1- ρ^* γ).

Q-Learning overestimation bias influenced by learning rate, discount factor, and reward signal.

problem Overestimation bias in Q-Learning algorithm.
method Investigated the influence of learning rate, discount factor, and reward signal on Q-Learning's overestimation bias. Tuned parameters and used an exponential moving average of reward signal.
result Q-Learning can achieve more accurate value estimates by tuning parameters and using an exponential moving average of reward signal.

New concept of Blackwell regret for reinforcement learning with sparse rewards.

problem Sparse rewards in long horizon MDPs.
method Formalization of myopic discount factors, value functions, and policies in terms of Blackwell optimality; introduction of Blackwell regret.
result Selecting a discount factor for zero Blackwell regret becomes arbitrarily hard in long horizon MDPs.

Study reveals a hidden cost in derivatives markets through option-implied discount factors.

problem The hidden cost in derivatives markets, not visible in price space.
method Minute-level NBBO data on options, reduced-form specification linking carry gap to implementation risk, trading frictions, and financial conditions.
result An annualized carry gap exists, linked to implementation risk and financial conditions.

The paper reviews historical and modern approaches to asset pricing probability measures.

problem Constructing or selecting probability measures for asset pricing.
method Historical review of various approaches including state price theory, martingale measures, and modern data-driven methods.
result Modern asset pricing involves constructing, transforming, or selecting probability measures to represent market prices.

Study uses put-call parity to estimate cost of funding in equity derivatives markets.

problem Estimating the cost of funding in active equity derivative markets.
method Develops a method using European put and call prices to recover the implicit discount factor and cost of funding.
result Identifies the cost of funding in major equity markets, showing it is typically around 34 basis points above OIS.

The paper proposes a new SDF scaled by time-varying volatility from S&P 500 options.

problem Estimating the SDF from option prices and predicting the equity premium.
method Utilizes S&P 500 options data to recover a stable, non-monotonic SDF.
result The SDF exhibits a hump on the put side, which transitions into a W-shape with maturity.

This paper considers the problem of consumption and investment in a financial market within a continuous time stochastic economy. The investor exhibits a change in the discount rate. The investment opportunities are a stock and a riskless account. The market coefficients and discount factor switch according to a finite…

2013-03-06abs ↗pdf ↗

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

problem Efficient reinforcement learning for large state and action spaces.
method Uses feature mapping to represent states and actions in a low-dimensional space, proposing a novel algorithm with polynomial regret bound.
result Achieves a O(dT/(1γ)2)O(d\sqrt{T}/(1-γ)^2) regret bound, near-optimal up to a (1γ)0.5(1-γ)^{-0.5} factor.

Paper solves discounted stochastic games with near-optimal time and sample complexity.

problem Solving discounted stochastic two-player games with optimal complexity.
method Generalizes Q-learning to two-player strategy computation, overcoming limitations of existing methods.
result Near-optimal εε-strategy computation with polylogarithmic factors in 1γ1 - γ and ε2ε^{-2}.

Policy gradient methods do not optimize the discounted objective, leading to suboptimal results.

problem Understanding the true optimization objective of policy gradient methods.
method Analyzing the update direction of policy gradient methods and proving it is not the gradient of any function.
result Policy gradient methods do not optimize the discounted objective, leading to suboptimal results.

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

problem Achieving optimal performance in dynamic online linear regression without prior knowledge.
method Developed a discounted variant of the Vovk-Azoury-Warmuth forecaster to achieve optimal dynamic regret guarantees.
result Achieved dynamic regret of the form $O\left(d\log(T)\vee \sqrt{dP_{T}^γ(\vec{u})T} ight)$, with a learnable discount factor.

The objective of the present paper is to analyse various features of the Smith-Wilson method used for discounting under the EU regulation Solvency II, with special attention to hedging. In particular, we show that all key rate duration hedges of liabilities beyond the Last Liquid Point will be peculiar. Moreover, we sh…

2016-02-05abs ↗pdf ↗

In a continuous time stochastic economy, this paper considers the problem of consumption and investment in a financial market in which the representative investor exhibits a change in the discount rate. The investment opportunities are a stock and a riskless account. The market coefficients and discount factor switches…

2011-07-10abs ↗pdf ↗

New findings reveal discount regularization can be seen as a strong prior, leading to poor performance in unevenly sampled data.

problem Discount regularization leads to poor performance in unevenly sampled data.
method Equivalence theorem showing discount regularization as a strong prior, setting regularization parameters locally for individual state-action pairs.
result Discount regularization can be seen as a strong prior, leading to poor performance in unevenly sampled data.

We propose a model for the credit markets in which the random default times of bonds are assumed to be given as functions of one or more independent "market factors". Market participants are assumed to have partial information about each of the market factors, represented by the values of a set of market factor informa…

2010-06-15abs ↗pdf ↗

New algorithm reduces online regression error in RKHS.

problem Online regression with time-varying functions in RKHS.
method Hierarchical Vovk-Azoury-Warmuth with discounting.
result Achieves optimal dynamic regret with O(T2/3PT1/3+TlnT)O(T^{2/3}P_T^{1/3} + \sqrt{T}\ln T) regret bound.

Unified framework linking firm signals and cross-asset spillovers for SDF estimation.

problem Estimating SDF with cross-asset spillovers and firm-level predictive signals.
method Maximizing Sharpe ratio to jointly estimate signals and spillovers, yielding interpretable SDF.
result SDF consistently outperforms benchmarks across various investment universes and market states.

Reward tweaking optimizes behavior for long-term goals by adjusting the reward function.

problem Optimizing behavior for long-term goals in reinforcement learning with unstable long planning horizons.
method Reward tweaking learns a surrogate reward function that induces optimal behavior for the original task.
result Reward tweaking guides agents towards better long-term returns while planning for short horizons.

Proof connects Gittins indices to Bayesian upper confidence bounds for patient agents.

problem Connecting Gittins indices to Bayesian upper confidence bounds for patient agents.
method Proof using Gaussian multi-armed bandit problem with discount factor γ.
result Gittins index equals γ-quantile of posterior mean plus vanishing error term.