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Results for “time decay” · papers 18 · wiki 2
Academic Papers · 18arXiv q-fin live 8 · desk corpus 573
arXiv · arXiv q-fin · 2023

Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation

Classical recommender systems often assume that historical data are stationary and fail to account for the dynamic nature of user preferences, limiting their ability to provide reliable recommendations in time-sensitive settings. This assumption is particularly problematic in finance, where financial products exhibit continuous changes in valuations, leading to frequent shifts in client interests. These evolving inte

Ashraf Ghiye, Baptiste Barreau, Laurent Carlier, Michalis Vazirgiannis
arXiv · arXiv q-fin · 2025

The Subtle Interplay between Square-root Impact, Order Imbalance & Volatility: A Unifying Framework

In this work, we aim to reconcile several apparently contradictory observations in market microstructure: is the famous "square-root law" of metaorder impact, which decays with time, compatible with the random-walk nature of prices and the linear impact of order imbalances? Can one entirely explain the volatility of prices as resulting from the flow of uninformed metaorders that mechanically impact them? We introduce

Guillaume Maitrier, Jean-Philippe Bouchaud
arXiv · arXiv q-fin · 2025

Limit Order Book Dynamics in Matching Markets: Microstructure, Spread, and Execution Slippage

Conventional models of matching markets assume that monetary transfers can clear markets by compensating for utility differentials. However, empirical patterns show that such transfers often fail to close structural preference gaps. This paper introduces a market microstructure framework that models matching decisions as a limit order book system with rigid bid ask spreads. Individual preferences are represented by a

Yao Wu
arXiv · arXiv q-fin · 2026

Taming Tail Risk in Financial Markets: Conformal Calibration for Nonstationary Portfolio VaR

Value-at-risk (VaR) forecasts drive trading constraints and capital allocation, yet realized exceedance rates concentrate in stress periods, when losses are largest. This paper studies sequential one-sided VaR calibration via conformal prediction. It proposes regime-weighted conformal calibration (RWC), which builds a safety buffer from past forecast errors using exponential time decay and regime-similarity weights.

Marc Schmitt
arXiv · arXiv q-fin · 2004

The American put and European options near expiry, under Levy processes

We derive explicit formulas for time decay, for the European call and put options at expiry, and use them to calculate analytical approximations to the price of the American put and early exercise boundary near expiry. We show that for many families of non-Gaussian processes used in empirical studies of financial markets, the early exercise boundary for the American put without dividends is separated from the strike

Sergei Levendorskii
arXiv · arXiv q-fin · 2021

Do fundamentals shape the price response? A critical assessment of linear impact models

We compare the predictions of the stationary Kyle model, a microfounded multi-step linear price impact model in which market prices forecast fundamentals through information encoded in the order flow, with those of the propagator model, a purely data-driven model in which trades mechanically impact prices with a time-decaying kernel. We find that, remarkably, both models predict the exact same price dynamics at high

Michele Vodret, Iacopo Mastromatteo, Bence Tóth, Michael Benzaquen
arXiv · arXiv q-fin · 2019

Large scale continuous-time mean-variance portfolio allocation via reinforcement learning

We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the optimality of a multivariate Gaussian feedback policy, with time-decaying variance, in trading off exploration and exploitation. Base

Haoran Wang
arXiv · arXiv q-fin · 2019

Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework

We approach the continuous-time mean-variance (MV) portfolio selection with reinforcement learning (RL). The problem is to achieve the best tradeoff between exploration and exploitation, and is formulated as an entropy-regularized, relaxed stochastic control problem. We prove that the optimal feedback policy for this problem must be Gaussian, with time-decaying variance. We then establish connections between the entr

Haoran Wang, Xun Yu Zhou
arXiv · arXiv · 2026

Optimal Block Time for AMM Liquidity Providers under Jump-Diffusion Prices

Loss-versus-Rebalancing (LVR) is the dominant adverse-selection cost borne by liquidity providers on automated market makers. Under geometric Brownian motion, arbitrage profit scales with the probability of a profitable block, which vanishes as the block time $Δt \to 0$; this is the standing argument for ever-shorter blocks. Modeling the reference price instead as a jump-diffusion, I show that the constant-product LV

Nils Bundi
OpenAlex · Review of Financial Studies · 2005 · cites 933

How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise

In theory, the sum of squares of log returns sampled at high frequency estimates their variance. When market microstructure noise is present but unaccounted for, however, we show that the optimal sampling frequency is finite and derives its closed-form expression. But even with optimal sampling, using say 5-min returns when transactions are recorded every second, a vast amount of data is discarded, in contradiction t

Yacine Aı̈t-Sahalia, Per A. Mykland, Lan Zhang
OpenAlex · European Finance Review · 2005 · cites 189

The Price of Future Liquidity: Time-Varying Liquidity in the U.S. Treasury Market

Abstract This paper examines the price differences between very liquid on-the-run U.S. Treasury securities and less liquid off-the-run securities over the on/off cycle. Comparing pairs of securities in time-series regressions allows us to disregard any fixed cross-sectional differences between securities. Also, since the liquidity of Treasury notes varies predictably over time, we can distinguish between current and

David Goldreich, Bernd Hanke, Purnendu Nath
arXiv · arXiv · 2025

Formal State-Machine Models for Uniswap v3 Concentrated-Liquidity AMMs: Priced Timed Automata, Finite-State Transducers, and Provable Rounding Bounds

Concentrated-liquidity automated market makers (CLAMMs), as exemplified by Uniswap v3, are now a common primitive in decentralized finance frameworks. Their design combines continuous trading on constant-function curves with discrete tick boundaries at which liquidity positions change and rounding effects accumulate. While there is a body of economic and game-theoretic analysis of CLAMMs, there is negligible work tha

Julius Tranquilli, Naman Gupta
arXiv · arXiv · 2025

Optimal Exit Time for Liquidity Providers in Automated Market Makers

We study the problem of optimal liquidity withdrawal for a representative liquidity provider (LP) in an automated market maker (AMM). LPs earn fees from trading activity but are exposed to impermanent loss (IL) due to price fluctuations. While existing work has focused on static provision and exogenous exit strategies, we characterise the optimal exit time as the solution to a stochastic control problem with an endog

Philippe Bergault, Sébastien Bieber, Leandro Sánchez-Betancourt
arXiv · arXiv · 2024

Reinforcement Learning for Optimal Execution when Liquidity is Time-Varying

Optimal execution is an important problem faced by any trader. Most solutions are based on the assumption of constant market impact, while liquidity is known to be dynamic. Moreover, models with time-varying liquidity typically assume that it is observable, despite the fact that, in reality, it is latent and hard to measure in real time. In this paper we show that the use of Double Deep Q-learning, a form of Reinforc

Andrea Macrì, Fabrizio Lillo
arXiv · arXiv · 2023

The Paradox Of Just-in-Time Liquidity in Decentralized Exchanges: More Providers Can Sometimes Mean Less Liquidity

We study Just-in-time (JIT) liquidity provision in blockchain-based decentralized exchanges. A JIT liquidity provider (LP) monitors pending swap orders in public mempools of blockchains to sandwich orders of their choice with liquidity, depositing right before and withdrawing right after the order. Our game-theoretic model with asymmetrically informed agents reveals that a JIT LP's presence does not always enhance li

Agostino Capponi, Ruizhe Jia, Brian Zhu
arXiv · arXiv · 2022

A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model

Based on the commentary data of the Shenzhen Stock Index bar on the EastMoney website from January 1, 2018 to December 31, 2019. This paper extracts the embedded investor sentiment by using a deep learning BERT model and investigates the time-varying linkage between investment sentiment, stock market liquidity and volatility using a TVP-VAR model. The results show that the impact of investor sentiment on stock market

Chenrui Zhang, Xinyi Wu, Hailu Deng, Huiwei Zhang
arXiv · arXiv · 2020

Real-Time Detection of Volatility in Liquidity Provision

Previous research has found that high-frequency traders will vary the bid or offer price rapidly over periods of milliseconds. This is a benefit to fast traders who can time their trades with microsecond precision, however it is a cost to the average market participant due to increased trade execution price uncertainty. In this analysis we attempt to construct real-time methods for determining whether the liquidity o

Matthew Brigida
arXiv · arXiv · 2017

Market impact with multi-timescale liquidity

We present an extended version of the recently proposed "LLOB" model for the dynamics of latent liquidity in financial markets. By allowing for finite cancellation and deposition rates within a continuous reaction-diffusion setup, we account for finite memory effects on the dynamics of the latent order book. We compute in particular the finite memory corrections to the square root impact law, as well as the impact de

Michael Benzaquen, Jean-Philippe Bouchaud
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