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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.

168,932 papers · 148 categories

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147295442589 · Jun 202019922001200920172026
48 results for discounted distribution ratios

DualDICE estimates discounted distribution ratios for reinforcement learning datasets.

problem Accurate estimation of discounted stationary distribution ratios for reinforcement learning applications.
method Behavior-agnostic algorithm that avoids importance weights and is theoretically guaranteed.
result Significantly improves off-policy policy evaluation accuracy compared to existing techniques.

Study optimal portfolio strategies with time-varying discount rates.

problem Optimizing portfolio decisions with a non-constant discount rate.
method Introduced subgame perfect strategies to handle time inconsistency, using fixed point iteration to find the utility-weighted discount rate.
result Subgame perfect strategies are equivalent to optimal strategies under certain utility function assumptions.

In the "positive interest" models of Flesaker-Hughston, the nominal discount bond system is determined by a one-parameter family of positive martingales. In the present paper we extend this analysis to include a variety of distributions for the martingale family, parameterised by a function that determines the behaviou…

2010-12-08abs ↗pdf ↗

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.

Market valuation duration is 175 years, but drops to 46 years during crises.

problem Understanding the duration of market valuation and its impact on returns.
method Comparing market valuation ratios and dividends to estimate duration, analyzing the discount rate effect.
result Valuation duration is negatively correlated with market returns, with a robust out-of-sample R2 of 15%.

In this paper, we present an online reinforcement learning algorithm, called Renewal Monte Carlo (RMC), for infinite horizon Markov decision processes with a designated start state. RMC is a Monte Carlo algorithm and retains the advantages of Monte Carlo methods including low bias, simplicity, and ease of implementatio…

2018-04-03abs ↗pdf ↗

Paper introduces novel Bandit algorithms for non-stationary environments in finance.

problem Non-stationary reward distributions in financial markets.
method Introduces Adaptive Discounted Thompson Sampling (ADTS) and Combinatorial Adaptive Discounted Thompson Sampling (CADTS) for non-stationary environments in portfolio optimization.
result Bandit Networks improve portfolio optimization performance by 20% compared to classical models.

New algorithm reduces bias in off-policy reinforcement learning.

problem Challenges in designing off-policy reinforcement learning algorithms.
method Doubly robust off-policy actor-critic (DR-Off-PAC) with a single timescale structure.
result Establishes the first overall sample complexity analysis for a single time-scale off-policy AC algorithm.

Tests factor models by decomposing market into body and tail legs, revealing inconsistent results.

problem Inconsistency between factor models and market behavior.
method Decomposes market into body and tail legs, testing factor models at daily and monthly frequencies.
result q5 model shows inconsistent results, with negative body and positive tail alphas at all split ratios.

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 ↗

Model shows how discount rates affect intergenerational equity in climate mitigation.

problem Intergenerational equity in climate mitigation decisions.
method Extended DICE model with stochastic discount rates and financing extensions.
result Discount-rate uncertainty amplifies intergenerational inequality in climate mitigation.

Cash collateral is perfect in that it provides simultaneous counterparty credit risk protection and derivatives funding. Securities are imperfect collateral, because of collateral segregation or differences in CSA haircuts and repo haircuts. Moreover, the collateral rate term structure is not observable in the repo mar…

2017-02-14abs ↗pdf ↗

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 RL approach handles non-exponential discounting for sequential decisions.

problem Modeling human discounting in sequential decision-making tasks.
method Generalized model-based reinforcement learning with arbitrary discount functions, using Hamilton-Jacobi-Bellman equation and collocation method.
result Validated approach on simulated problems, showing applicability to human discounting.

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.

Proposes a new method to rank risky investments based on Omega measure.

problem Evaluating and ranking risky investment projects.
method Introduces an investment certainty equivalence approach and uses the Omega measure.
result Proposed method ranks projects differently from conventional risk-adjusted discount rate (RADR) approach.

Omega ratio is shown to be equivalent to Sharpe ratio under certain distributional assumptions.

problem Comparing Omega ratio to Sharpe ratio as performance indicators.
method Computation and analysis of Omega ratio for normal distribution and proof for elliptic distributions.
result Omega ratio is equivalent to Sharpe ratio for returns with elliptic distributions.

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.

Study decomposes market portfolio into body and tail legs, revealing systematic differences.

problem Understanding the relationship between body and tail components in market portfolios.
method Decomposes CRSP market portfolio into body and tail legs, analyzes their recombination identity.
result Recombination identity holds for all models but not for all, indicating systematic differences.

Study optimal stopping for group with diverse discount rates using an attitude function.

problem Optimal stopping for a group with diverse discount rates under an aggregation preference.
method Develop iterative approach using consistent planning for time-consistent equilibria.
result Characterize all time-consistent mild equilibria as fixed points of an operator.

We introduce a simple generalization of rational bubble models which removes the fundamental problem discovered by [Lux and Sornette, 1999] that the distribution of returns is a power law with exponent less than 1, in contradiction with empirical data. The idea is that the price fluctuations associated with bubbles mus…

2000-10-06abs ↗pdf ↗

Study cash-flow forecasting for derivatives, aligning with replication strategy and addressing timing frictions.

problem Inconsistencies in cash-flow forecasting under different measures and stochastic payment times.
method Use discounting sensitivities (funding-curve hedge ratios) for replication and propose a liquidity valuation adjustment.
result Aligns forecasting with replication strategy and avoids measure-mixing issues.

In this study we prove the existence of statistical arbitrage opportunities in the Black-Scholes framework by considering trading strategies that consists of borrowing from the risk free rate and taking a long position in the stock until it hits a deterministic barrier level. We derive analytical formulas for the expec…

2014-06-21abs ↗pdf ↗

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.

Unified framework for OOD detection using class ratio estimation.

problem Density-based OOD detection is unreliable for OOD images.
method Unified framework that builds energy-based models and employs differing base distributions, directly estimating the density ratio through class ratio estimation.
result Competitive results on OOD image problems compared to recent work.

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.

The article presents a general discrete time dividend valuation model when the dividend growth rate is a general continuous variable. The main assumption is that the dividend growth rate follows a discrete time semi-Markov chain with measurable space. The paper furnishes sufficient conditions that assure finiteness of …

2016-05-09abs ↗pdf ↗

Optimal option portfolios under Sharpe Ratio maximization with skew-elliptical t-distributed returns

problem Optimal option portfolios under Sharpe Ratio maximization
method Formulation for explicit portfolio weights
result Different optimal portfolios for Sharpe Ratio and return-to-Value-at-Risk (VaR) ratio

New method estimates density ratio for well-separated distributions using multi-class logistic regression.

problem Challenges in estimating density ratio for well-separated distributions.
method Uses multi-class logistic regression with auxiliary densities to estimate log(p/q).
result Demonstrates superior performance on density ratio estimation, mutual information, and representation learning tasks.

Sharpe ratio is widely used in asset management to compare and benchmark funds and asset managers. It computes the ratio of the excess return over the strategy standard deviation. However, the elements to compute the Sharpe ratio, namely, the expected returns and the volatilities are unknown numbers and need to be esti…

2018-08-02abs ↗pdf ↗

A firm with heterogeneous shareholders optimizes dividends under ambiguity aggregation.

problem Optimizing dividends for a firm with heterogeneous shareholders under ambiguity aggregation.
method Characterizing equilibrium dividends using a partition of the state space.
result Time-homogeneous equilibrium dividend law characterized by a partition of the state space.

The proposed model modifies option pricing formulas for the basic case of log-normal probability distribution providing correspondence to formulated criteria of efficiency and completeness. The model is self-calibrating by historic volatility data; it maintains the constant expected value at maturity of the hedged inst…

2008-02-25abs ↗pdf ↗

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

problem Understanding how discounts influence train ticket buying and rescheduling behavior.
method Machine learning techniques, including causal machine learning, to analyze survey data.
result Increasing a discount rate by 1% increases the rescheduled trip share by 0.16% among always buyers.

Paper introduces ENZ to measure significant coefficients in sparse recovery, improving over classical methods.

problem Numerical noise creates long tails of negligible coefficients in sparse recovery.
method Entropy-based notion of effective sparsity (ENZ) to measure significant coefficients, proving stability under restricted isometry condition.
result ENZ decomposes into support cardinality and efficiency factor, providing a precise measure of sparsity.