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.
Geometric Mean Market Makers super-hedge impermanent loss without models.
New market makers improve on existing models in DeFi.
This paper extends liquidity returns in geometric mean markets to time-varying weights.
We analyze impermanent loss in AMMs and show G3Ms are simplest.
Consider the problem of pricing options on forwards in energy markets, when spot prices follow a geometric multi-factor model in which several rates of mean reversion appear. In this paper we investigate the role played by slow mean reversion when pricing and hedging options. In particular, we determine both upper and …
Optimal fees for G3Ms align LP value with market accuracy.
The paper extends the market price of risk for electricity swap contracts, incorporating jump risk.
We study an optimal execution problem in the presence of market impact where the security price follows a geometric Ornstein-Uhlenbeck process, which implies the mean-reverting property, and show that the optimal strategy is a mixture of initial/terminal block liquidation and gradual intermediate liquidation. The mean-…
Geometric Arbitrage Theory reformulates a generic asset model possibly allowing for arbitrage by packaging all assets and their forwards dynamics into a stochastic principal fibre bundle, with a connection whose parallel transport encodes discounting and portfolio rebalancing, and whose curvature measures, in this geom…
No fair and strategy-proof automated market maker exists for more than two assets.
In this paper, we consider the asset-liability management under the mean-variance criterion. The financial market consists of a risk-free bond and a stock whose price process is modeled by a geometric Brownian motion. The liability of the investor is uncontrollable and is modeled by another geometric Brownian motion. W…
In this paper we study the continuum time dynamics of a stock in a market where agents behavior is modeled by a Minority Game and a Grand Canonical Minority Game. The dynamics derived is a generalized geometric Brownian motion; from the Black & Scholes formula the calibration of both the Minority Game and the Grand Can…
Study analyzes prediction market convergence and pricing mechanisms.
We study the mean escape time in a market model with stochastic volatility. The process followed by the volatility is the Cox Ingersoll and Ross process which is widely used to model stock price fluctuations. The market model can be considered as a generalization of the Heston model, where the geometric Brownian motion…
Model uncertainty is a type of inevitable financial risk. Mistakes on the choice of pricing model may cause great financial losses. In this paper we investigate financial markets with mean-volatility uncertainty. Models for stock markets and option markets with uncertain prior distribution are established by Peng's G-s…
QGMS framework detects market endpoints using geometric patterns.
New method finds better arbitrage opportunities in AMMs.
For a commodity spot price dynamics given by an Ornstein-Uhlenbeck process with Barndorff-Nielsen and Shephard stochastic volatility, we price forwards using a class of pricing measures that simultaneously allow for change of level and speed in the mean reversion of both the price and the volatility. The risk premium i…
K-means algorithm improves financial market risk prediction accuracy.
Study growth of LP wealth in G3Ms affected by trading fees and arbitrage.
Study of a generalized geometric Brownian motion with varying entry and exit rates.
In electricity markets, it is sensible to use a two-factor model with mean reversion for spot prices. One of the factors is an Ornstein-Uhlenbeck (OU) process driven by a Brownian motion and accounts for the small variations. The other factor is an OU process driven by a pure jump Lévy process and models the characteri…
Complex network analysis reveals dominant stocks in financial stock returns correlations.
Market-maker optimizes quotes based on strategic market-takers' behavior.
Study clusters Indian stocks using polyspectral means for nuanced market insights.
Extends LIBOR market model to reduce exploding scenarios.
The paper defines symmetries in no-arbitrage markets.
Modeling price dynamics in AMMs with fees using geometric Brownian motion.
Study geometric step options with jumps, deriving pricing equations and characterizations.
Recent studies have shown that online portfolio selection strategies that exploit the mean reversion property can achieve excess return from equity markets. This paper empirically investigates the performance of state-of-the-art mean reversion strategies on real market data. The aims of the study are twofold. The first…
It seems to be very unlikely that all relevant information in the stock market could be fully encoded in a geometrical shape. Still,the present paper will reveal the geometry behind the stock market transactions. The prices of market index (DJIA) stock components are arranged in ascending order from the smallest one in…
In this paper, we propose a minimal model beyond geometric Brownian motion that aims to describe price actions with market inefficiency. From simple financial theory considerations, we arrive at a simple two-variable hidden Markovian time series model, with one of the variable entirely unobserved. Then, we analyze the …
GeomHerd predicts herding behavior before market prices move, using Ricci curvature of agent interaction graphs.
Study -player and mean-field games in Itô-diffusion markets with competitive or homophilous interactions.
Optimized portfolio management with dynamic market regimes using RL and OC learning.
The paper defines and studies the geometric mean for tensors and its associated Riemannian geometry.
We introduce and study a non-equilibrium continuous-time dynamical model of the price of a single asset traded by a population of heterogeneous interacting agents in the presence of uncertainty and regulatory constraints. The model takes into account (i) the price formation delay between decision and investment by the …
We have embedded the classical theory of stochastic finance into a differential geometric framework called Geometric Arbitrage Theory and show that it is possible to: --Write arbitrage as curvature of a principal fibre bundle. --Parameterize arbitrage strategies by its holonomy. --Give the Fundamental Theorem of Asset …
Proves uniqueness of geometric flow in various Riemannian manifolds.
We propose a simple non-equilibrium model of a financial market as an open system with a possible exchange of money with an outside world and market frictions (trade impacts) incorporated into asset price dynamics via a feedback mechanism. Using a linear market impact model, this produces a non-linear two-parametric ex…
Alternative perspective on mean-field LIBOR market model, maintaining practicality and applicability.
Market stability depends on a fundamental value anchor, not price crashes.
Study examines local extrema and crossing statistics in financial markets.
We apply Geometric Arbitrage Theory to obtain results in mathematical finance for credit markets, which do not need stochastic differential geometry in their formulation. We obtain closed form equations involving default intensities and loss given defaults characterizing the no-free-lunch-with-vanishing-risk condition …
Proposes a virtual bidding strategy for electricity markets using stochastic control.
Geometric theory explains substitutability in market outcomes based on production constraints.
We study the optimal timing strategies for trading a mean-reverting price process with afinite deadline to enter and a separate finite deadline to exit the market. The price process is modeled by a diffusion with an affine drift that encapsulates a number of well-known models,including the Ornstein-Uhlenbeck (OU) model…
The paper analyzes arbitrage opportunities in a large investor market with common stock noises.