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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.

168,695 papers · 148 categories

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59118176235 · May 202619922001200920172026
48 results for horizon risk

The paper introduces a new risk measure for financial models with jumps.

problem The limitations of point-in-time risk measures in models with jumps.
method Proposes an intra-horizon expected shortfall for profit and loss processes.
result The intra-horizon expected shortfall is a coherent risk measure for various Lévy processes.

The paper clarifies long-horizon investment and DCA, showing no risk reduction but different exposure profiles.

problem Misleading claims about reducing risk with longer investment horizons and DCA.
method Unified probabilistic framework, defining risk and uncertainty, and introducing effective investment exposure.
result Different investment timing strategies can lead to distinct exposure profiles over time, affecting risk and uncertainty.

This paper introduces new risk measures for evaluating losses with varying time horizons.

problem Capturing horizon risk and cash non-additivity in risk evaluation.
method Uses BSDEs and shortfall approaches to develop h-generalized shortfall risk measures.
result Introduces hq-entropic risk measures as a new family of fully-dynamic risk measures.

Modeling risk and performance with Levy-stable distributions.

problem Understanding risk and performance in financial markets with non-Gaussian distributions.
method Developed a finite-horizon model using Levy-stable scaling, identified parameters from data, derived formulas for various financial ratios.
result Horizon-correct formulas for risk measures are derived and validated across different horizons.

This study improves credit risk management using advanced reinforcement learning.

problem Sub-optimal hedging of credit losses due to bid-ask costs and model limitations.
method Risk-averse stochastic-horizon reinforcement learning for dynamic risk management.
result Efficacy demonstrated through numerical study of a single FX forward contract portfolio.

The paper analyzes risk measures and optimal reserve allocation strategies.

problem Risk measures and optimal reserve allocation across multiple lines of business.
method Formalizes expected maximum deficit, introduces implicitly bounded risk measures, and proposes capital allocation approaches.
result Theoretical results on static and dynamic coherence, convexity, and exact optimizations of aggregate minimum reserves.

A new method for risk-averse decision-making in Markov processes with improved regret bounds.

problem Risk-averse decision-making in Markov processes.
method Introduces mini-batch measures and multipattern risk-averse problems in a feature-based QQ-learning method.
result Proves a high-probability regret bound of O(H2NHK)\mathcal{O}\big(H^2 N^H \sqrt{ K}\big) for the QQ-learning method.

We discuss when and why custom multi-factor risk models are warranted and give source code for computing some risk factors. Pension/mutual funds do not require customization but standardization. However, using standardized risk models in quant trading with much shorter holding horizons is suboptimal: 1) longer horizon …

2014-09-09abs ↗pdf ↗

For an investor with constant absolute risk aversion and a long horizon, who trades in a market with constant investment opportunities and small proportional transaction costs, we obtain explicitly the optimal investment policy, its implied welfare, liquidity premium, and trading volume. We identify these quantities as…

2011-10-06abs ↗pdf ↗

The paper examines how loss aversion impacts multi-armed bandit decisions over long periods.

problem The impact of loss aversion on multi-armed bandit decisions over long periods.
method A new central limit theorem for measures with history-dependent variances, derived under risk aversion in gains and risk loving in losses.
result Consequences of loss aversion for asymptotic properties are derived in analytical results.

We estimate risk measures in Markov cost processes with lower and upper bounds.

problem Estimating risk measures in infinite-horizon discounted costs within Markov processes.
method Truncation scheme and lower/upper bounds for CVaR and variance estimation.
result Upper and lower bounds for CVaR and variance estimation match up to logarithmic factors.

Predicts long-term return distributions with time-varying volatility.

problem Risk management in long-horizon returns.
method Predicts future return distributions without specifying volatility dynamics or shock distribution.
result Derives risk measures like VaR and CTE from the predicted return distribution.

We consider portfolio selection when decisions based on a dynamic risk measure are affected by the use of a moving horizon, and the possible inconsistencies that this creates. By giving a formal treatment of time consistency which is independent of Bellman's equations, we show that there is a new sense in which these d…

2009-12-08abs ↗pdf ↗

Overview of risk-sensitive Markov decision processes with Optimized Certainty Equivalent.

problem Optimizing decision-making under risk in Markov processes.
method Analyzes risk-sensitive criteria using Optimized Certainty Equivalent, including entropic risk and Conditional Value-at-Risk.
result Conditions for the existence of optimal policies and solution procedures are provided.

Dynamic risk constraints help limit risky behavior in financial portfolios.

problem Static risk measures fail to control tail-risk-seeking traders.
method Introduces dynamic risk constraints applied throughout the trading horizon.
result Dynamic risk constraints can effectively limit risky behavior in portfolios.

Improved sample complexity for identifying best policies in risk-sensitive reinforcement learning.

problem Identifying approximately optimal policies in risk-sensitive reinforcement learning with exponential horizon dependence.
method Forward-model based algorithm with KL-based exploration bonuses adapted for entropic criterion, leveraging smoothness properties of exponential utility and a new stopping rule.
result Achieved sample complexity matching the lower bound, closing the gap between upper and lower bounds.

In this paper, we study optimal liquidation problems in a randomly-terminated horizon. We consider the liquidation of a large single-asset portfolio with the aim of minimizing a combination of volatility risk and transaction costs arising from permanent and temporary market impact. Three different scenarios are analyze…

2017-09-18abs ↗pdf ↗

The paper studies risk-sensitive MDPs with recursive risk measures.

problem Risk-sensitive decision-making in MDPs with unbounded costs.
method Recursive application of static risk measures, Bellman equation derivation, existence of optimal policies.
result Existence of Markovian optimal policies for infinite planning horizons, contractive model for stationary optimal policy.

This paper optimizes cryptocurrency portfolios by clustering price correlations and improving risk-return profiles.

problem Volatility and regulatory uncertainty in cryptocurrency markets make portfolio construction challenging.
method The paper combines network analysis, price forecasting, and portfolio theory to identify stable groups of correlated cryptocurrencies.
result Predictive consensus-clustering portfolios maintain positive and stable performance up to a 14-day horizon, with favourable gain-loss asymmetry and tighter tail-risk control.

This paper completes the analysis of Choulli et al. Non-Arbitrage up to Random Horizons and after Honest Times for Semimartingale Models and contains two principal contributions. The first contribution consists in providing and analysing many practical examples of market models that admit classical arbitrages while the…

2013-12-09abs ↗pdf ↗

In this paper, we obtain analytical expression for the distribution of the occupation time in the red (below level 00) up to an (independent) exponential horizon for spectrally negative Lévy risk processes and refracted spectrally negative Lévy risk processes. This result improves the existing literature in which only…

2019-03-09abs ↗pdf ↗

The paper models exchange rate risk premium using mean-reverting dynamics.

problem Empirical failure of uncovered interest parity (UIP).
method Modeling risk premium using Ornstein-Uhlenbeck (OU) process embedded in stochastic differential equation for exchange rate.
result The model shows strong predictive performance at short and long horizons, but underperforms at intermediate horizons.

In this paper we discuss a general methodology to compute the market risk measure over long time horizons and at extreme percentiles, which are the typical conditions needed for estimating Economic Capital. The proposed approach extends the usual market-risk measure, ie, Value-at-Risk (VaR) at a short-term horizon and …

2014-08-11abs ↗pdf ↗

Investment and consumption strategy for risk-averse agents with Epstein-Zin utility.

problem Optimal investment and consumption strategy for Epstein-Zin utility.
method Detailed introduction to Epstein-Zin utility, existence and uniqueness proof, verification argument.
result Existence and uniqueness of optimal solution for Epstein-Zin utility under certain parameter restrictions.

The study reveals distinct patterns in retail investors' holding periods affecting stock returns.

problem Understanding the impact of retail investors' investment horizons on stock returns.
method Using self-reported holding periods from StockTwits, the study categorizes retail investors into long-horizon and short-horizon groups and analyzes their return patterns.
result Long-horizon retail investors exhibit underreaction to earnings announcements, while short-horizon investors show overreaction.

This paper solves the consumption-investment problem under Epstein-Zin preferences on a random horizon. In an incomplete market, we take the random horizon to be a stopping time adapted to the market filtration, generated by all observable, but not necessarily tradable, state processes. Contrary to prior studies, we do…

2019-03-21abs ↗pdf ↗

Extends utility maximization theory for infinite horizons without strong no-arbitrage assumptions.

problem Maximizing lifetime utility from wealth over an infinite horizon.
method Develops a duality theory using deflators and supermartingale properties, extending previous work.
result Establishes a strong duality theorem for infinite horizon utility maximization under minimal no-arbitrage assumptions.

In the presence of ambiguity on the driving force of market randomness, we consider the dynamic portfolio choice without any predetermined investment horizon. The investment criteria is formulated as a robust forward performance process, reflecting an investor's dynamic preference. We show that the market risk premium …

2019-04-20abs ↗pdf ↗

Optimal reinsurance and dividend strategy for insurance companies in a finite time.

problem Maximizing dividends while managing risk in a finite time horizon.
method Dynamic control problem with Hamilton-Jacobi-Bellman equation, penalty approximation method.
result Smoothness of the value function and comparison principle for its gradient.

This paper develops a method to derive optimal portfolios and risk premia explicitly in a general diffusion model for an investor with power utility and a long horizon. The market has several risky assets and is potentially incomplete. Investment opportunities are driven by, and partially correlated with, state variabl…

2012-03-07abs ↗pdf ↗

This paper presents a novel scaling method for unbiased risk estimation.

problem Challenges in risk assessment due to limited data, non-stationarity, and heavy tails.
method Develops a statistical framework for efficient risk scaling, extending beyond the square-root-of-time rule.
result Ensures robust and conservative risk estimation, applicable to small sample settings.

This paper examines the volatility and covariance dynamics of cash and futures contracts that underlie the Optimal Hedge Ratio (OHR) across different hedging time horizons. We examine whether hedge ratios calculated over a short term hedging horizon can be scaled and successfully applied to longer term horizons. We als…

2011-03-30abs ↗pdf ↗

Neural Lévy model improves risk and density forecasting for financial returns.

problem Financial returns exhibit heavy tails, volatility clustering, and jumps.
method Proposes a neural Lévy jump-diffusion framework that learns conditional drift, diffusion, jump intensity, and size distribution.
result Demonstrates improved calibration, sharper tail control, and risk reduction.

Investors benefit from long horizons in a market with mean-reverting equity returns.

problem Optimal portfolio choice in a market with mean-reverting risk-free rate and equity risk-premium.
method Mean-variance optimization, Euler-Lagrange equation, Calculus of Variations, spectral problem.
result Optimal policies are characterized by eigenvalues of the lambda-matrix, leading to better risk-return trade-offs for long-term investors.

We solve non-Markovian optimal switching problems in discrete time on an infinite horizon, when the decision maker is risk aware and the filtration is general, and establish existence and uniqueness of solutions for the associated reflected backward stochastic difference equations. An example application to hydropower …

2019-10-09abs ↗pdf ↗

Solves VaR-constrained portfolio optimization in markets with stochastic volatility.

problem Optimizing portfolio in markets with stochastic volatility under VaR constraints.
method Dynamic programming approach to Heston's stochastic volatility model.
result Optimal investment strategy linked to unconstrained problem via a vega-neutral derivative.

Crypto simulations show HODL strategy loads risk onto most investors, with macro-sentiment affecting returns.

problem Understanding real risk-return trade-offs and factors affecting crypto returns.
method Two independent analyses: 480 million Monte Carlo simulations and Bayesian multi-horizon local projection framework.
result HODL strategy exposes most investors to extreme downside risk, and macro-sentiment conditions are dominant indicators for future outcomes.