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Results for “slow trend” · papers 18 · wiki 3
Academic Papers · 18arXiv q-fin live 1 · desk corpus 100
arXiv · arXiv q-fin · 2025

Stationary Distributions of the Mode-switching Chiarella Model

We derive the stationary distribution in various regimes of the extended Chiarella model of financial markets. This model is a stochastic nonlinear dynamical system that encompasses dynamical competition between a (saturating) trending and a mean-reverting component. We find the so-called mispricing distribution and the trend distribution to be unimodal Gaussians in the small noise, small feedback limit. Slow trends

Jutta G. Kurth, Jean-Philippe Bouchaud
arXiv · arXiv · 2010

GDP Trend Deviations and the Yield Spread: the Case of Five E.U. Countries

Several studies have established the predictive power of the yield curve in terms of real economic activity. In this paper we use data for a variety of E.U. countries: both EMU (Germany, France, Italy) and non-EMU members (Sweden and the U.K.). The data used range from 1991:Q1 to 2009:Q1. For each country, we extract the long run trend and the cyclical component of real economic activity, while the corresponding inte

Periklis Gogas, Ioannis Pragidis
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
arXiv · arXiv · 2023

Uncovering Market Disorder and Liquidity Trends Detection

The primary objective of this paper is to conceive and develop a new methodology to detect notable changes in liquidity within an order-driven market. We study a market liquidity model which allows us to dynamically quantify the level of liquidity of a traded asset using its limit order book data. The proposed metric holds potential for enhancing the aggressiveness of optimal execution algorithms, minimizing market i

Etienne Chevalier, Yadh Hafsi, Vathana Ly Vath
OpenAlex · RePEc: Research Papers in Economics · 2016 · cites 19

Recent Trends in Cross-currency Basis

The cross-currency basis, which is the basis spread added mainly to the U.S. dollar London Interbank Offered Rate (USD LIBOR) when the USD is funded via foreign exchange (FX) swaps using the Japanese yen or the euro as a funding currency, has been widening globally since the beginning of 2014. This development is driven by (1) increased demands for U.S. dollars resulting from a divergence in the monetary policy betwe

Fumihiko Arai, Yoshibumi Makabe, Yasunori Okawara, Teppei Nagano
arXiv · arXiv · 2026

Causal Effects of Protocol-Fee Changes on Liquidity Provision in Automated Market Makers

Automated market maker (AMM) fee rules are often evaluated by liquidity-provider (LP) welfare, but that objective mixes fee revenue, adverse-selection loss (loss-versus-rebalancing, LVR), routing response, and liquidity supply. Fixed-fee Uniswap v3 history cannot separate these channels or identify counterfactual trader-facing dynamic-fee rules. Real fee-related variation nonetheless exists: the Uniswap protocol-fee

Wen-Ting Wang
arXiv · arXiv · 2026

When large trades are not (automatically) news: liquidity tail risk and price discovery

We examine how heavy-tailed liquidity demand changes price discovery in a sequential limit order book with asymmetric information. In our setting, liquidity suppliers observe aggregate order flow, not its decomposition into informed demand and uninformed liquidity shocks. With heavy-tailed uninformed aggregated order flow, large trades remain plausibly uninformed over a wider range of depths, flattening price impact

Umut Çetin, Mingwei Lin, Giulia Livieri
arXiv · arXiv · 2026

What Happens When Institutional Liquidity Enters Prediction Markets: Identification, Measurement, and a Synthetic Proof of Concept

Prediction markets are starting to look less like crowd polls and more like electronic markets. The central question is therefore no longer only whether these markets forecast well, but what happens when institutional liquidity enters: do spreads tighten, does price discovery improve, and do those gains actually reach the traders who are slowest to react when information arrives? This paper offers a research design f

Shaw Dalen
arXiv · arXiv · 2026

Neural Hidden Markov Model with Adaptive Granularity Attention for High-Frequency Order Flow Modeling

We propose a Neural Hidden Markov Model (HMM) with Adaptive Granularity Attention (AGA) for high-frequency order flow modeling. The model addresses the challenge of capturing multi-scale temporal dynamics in financial markets, where fine-grained microstructure signals and coarse-grained liquidity trends coexist. The proposed framework integrates parallel multi-resolution encoders, including a dilated convolutional ne

Tianzuo Hu
arXiv · arXiv · 2025

Asset Pricing in Pre-trained Transformer

This paper proposes an innovative Transformer model, Single-directional representative from Transformer (SERT), for US large capital stock pricing. It also innovatively applies the pre-trained Transformer models under the stock pricing and factor investment context. They are compared with standard Transformer models and encoder-only Transformer models in three periods covering the entire COVID-19 pandemic to examine

Shanyan Lai
arXiv · arXiv · 2024

No Questions Asked: Effects of Transparency on Stablecoin Liquidity During the Collapse of Silicon Valley Bank

Fiat-pegged stablecoins are by nature exposed to spillover effects during market turmoil in Traditional Finance (TradFi). We observe a difference in TradFi market shocks impact between various stablecoins, in particular, USD Coin (USDC) and Tether USDT (USDT), the former with a higher reporting frequency and transparency than the latter. We investigate this, using top USDC and USDT liquidity pools in Uniswap, by adap

Walter Hernandez Cruz, Jiahua Xu, Paolo Tasca, Carlo Campajola
arXiv · arXiv · 2023

Market-Adaptive Ratio for Portfolio Management

Traditional risk-adjusted returns, such as the Treynor, Sharpe, Sortino, and Information ratios, have been pivotal in portfolio asset allocation, focusing on minimizing risk while maximizing profit. Nevertheless, these metrics often fail to account for the distinct characteristics of bull and bear markets, leading to sub-optimal investment decisions. This paper introduces a novel approach called the Market-adaptive R

Ju-Hong Lee, Bayartsetseg Kalina, KwangTek Na
arXiv · arXiv · 2023

Handling missing data in Burundian sovereign bond market

Constructing an accurate yield curve is essential for evaluating financial instruments and analyzing market trends in the bond market. However, in the case of the Burundian sovereign bond market, the presence of missing data poses a significant challenge to accurately constructing the yield curve. In this paper, we explore the limitations and data availability constraints specific to the Burundian sovereign market an

Irène Irakoze, Rédempteur Ntawiratsa, David Niyukuri
arXiv · arXiv · 2022

Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management

Traditional portfolio management methods can incorporate specific investor preferences but rely on accurate forecasts of asset returns and covariances. Reinforcement learning (RL) methods do not rely on these explicit forecasts and are better suited for multi-stage decision processes. To address limitations of the evaluated research, experiments were conducted on three markets in different economies with different ov

Ruan Pretorius, Terence van Zyl
arXiv · arXiv · 2022

Are all Credit Default Swap Databases equal?

We compare the five major sources of corporate Credit Default Swap prices: GFI, Fenics, Reuters, CMA, and Markit, using the most liquid single name 5-year CDS in the iTraxx and CDX indexes from 2004 to 2010. Deviations from the common trend among prices in the different databases are not random but are explained by idiosyncratic factors, financing costs, global risk, and other trading factors. The CMA quotes lead the

Sergio Mayordomo, Juan Ignacio Peña, Eduardo S. Schwartz
arXiv · arXiv · 2021

A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection

This work proposes a novel portfolio management technique, the Meta Portfolio Method (MPM), inspired by the successes of meta approaches in the field of bioinformatics and elsewhere. The MPM uses XGBoost to learn how to switch between two risk-based portfolio allocation strategies, the Hierarchical Risk Parity (HRP) and more classical Naïve Risk Parity (NRP). It is demonstrated that the MPM is able to successfully ta

Damian Kisiel, Denise Gorse
arXiv · arXiv · 2019

Endogenous Liquidity Crises

Empirical data reveals that the liquidity flow into the order book (depositions, cancellations andmarket orders) is influenced by past price changes. In particular, we show that liquidity tends todecrease with the amplitude of past volatility and price trends. Such a feedback mechanism inturn increases the volatility, possibly leading to a liquidity crisis. Accounting for such effects withina stylized order book mode

Antoine Fosset, Jean-Philippe Bouchaud, Michael Benzaquen
arXiv · arXiv · 2014

The adaptive nature of liquidity taking in limit order books

In financial markets, the order flow, defined as the process assuming value one for buy market orders and minus one for sell market orders, displays a very slowly decaying autocorrelation function. Since orders impact prices, reconciling the persistence of the order flow with market efficiency is a subtle issue. A possible solution is provided by asymmetric liquidity, which states that the impact of a buy or sell ord

Damian Eduardo Taranto, Giacomo Bormetti, Fabrizio Lillo
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