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.
Solves optimal control for trading multiple mean-reverting assets.
The paper prices swaps on generalized variance measures for multiple assets.
Extends return risk measures to multiple assets, proving properties and comparing different risk models.
Efficiently prices American options with multiple assets using sparse grids.
This paper provides a framework for modeling the financial system with multiple illiquid assets when liquidation of illiquid assets is caused by failure to meet a leverage requirement. This extends the network model of Cifuentes, Shin & Ferrucci (2005) which incorporates a single asset with fire sales and capital adequ…
This paper examines momentum spillover across multiple asset classes using only pricing data.
We consider robust pricing and hedging for options written on multiple assets given market option prices for the individual assets. The resulting problem is called the multi-marginal martingale optimal transport problem. We propose two numerical methods to solve such problems: using discretisation and linear programmin…
We design an optimal strategy for investment in a portfolio of assets subject to a multiplicative Brownian motion. The strategy provides the maximal typical long-term growth rate of investor's capital. We determine the optimal fraction of capital that an investor should keep in risky assets as well as weights of differ…
Study optimizes trading in multiple assets with cross-effects.
Paper presents a machine learning-based method for efficiently pricing and hedging autocallable structured notes with multiple underlying assets.
We analyze a simple asset transfer model in which the transfer amount is a fixed fraction of the giver's wealth. The model is analyzed in a new way by Laplace transforming the master equation, solving it analytically and numerically for the steady-state distribution, and exploring the solutions for various values o…
We study a multiplicative transient price impact model for an illiquid financial market, where trading causes price impact which is multiplicative in relation to the current price, transient over time with finite rate of resilience, and non-linear in the order size. We construct explicit solutions for the optimal contr…
Study uses BSDEs to price European options in markets with multiple defaults.
Study optimal liquidation with incomplete trend information and multiplicative price impact.
Solves portfolio optimization with costs using numerical methods.
This research improves asset life prediction by integrating deep learning with mixture distributions.
New process explains asset volatility patterns.
Flexible framework for optimal trading across multiple asset venues.
Study shows integrating OFI from multiple levels improves price impact explanation but not forecasting.
We consider a portfolio allocation problem for trend following (TF) strategies on multiple correlated assets. Under simplifying assumptions of a Gaussian market and linear TF strategies, we derive analytical formulas for the mean and variance of the portfolio return. We construct then the optimal portfolio that maximiz…
In this work we study the optimal execution problem with multiplicative price impact in algorithm trading, when an agent holds an initial position of shares of a financial asset. The inter-selling-decision times are modelled by the arrival times of a Poisson process. The criterion to be optimised consists in maximising…
Clusters asset classes to identify lead-lag relationships in market regimes.
Optimal crypto asset routing with CFMMs, including fixed costs.
When trading incurs proportional costs, leverage can scale an asset's return only up to a maximum multiple, which is sensitive to its volatility and liquidity. In a model with one safe and one risky asset, with constant investment opportunities and proportional costs, we find strategies that maximize long term returns …
A new method simulates implied volatility surfaces for multiple assets.
This paper proposes swaps on two important new measures of generalized variance, namely the maximum eigen-value and trace of the covariance matrix of the assets involved. We price these generalized variance swaps for financial markets with Markov-modulated volatilities. We consider multiple assets in the portfolio for …
Subordination is an often used stochastic process in modeling asset prices. Subordinated Levy price processes and local volatility price processes are now the main tools in modern dynamic asset pricing theory. In this paper, we introduce the theory of multiple internally embedded financial time-clocks motivated by beha…
We introduce and study a class of over-the-counter market models specified by systems of Ordinary Differential Equations (ODE's), in the spirit of Duffie- G^arleanu-Pedersen [6]. The key innovation is allowing for multiple assets. We show the existence and uniqueness of a steady state for these ODE's.
Representative investors whose behaviour is modelled by a deterministic finite automaton generate complexity both in the time series of each asset and in the cross-sectional correlation when the rule governing their behaviour is schizophrenic, meaning the investor must hold multiple seemingly contradictory beliefs simu…
This paper optimizes brokerage contracts for multiple clients trading a single asset.
The risk of financial positions is measured by the minimum amount of capital to raise and invest in eligible portfolios of traded assets in order to meet a prescribed acceptability constraint. We investigate nondegeneracy, finiteness and continuity properties of these risk measures with respect to multiple eligible ass…
EB improves asset pricing by mining large strategies without lookahead bias.
Study of a risk-averse informed trader in a multi-asset market with non-Gaussian prices.
We extend Kyle's model to include stochastic liquidity and multiple assets.
The study examines markets with multiple numéraires and finds equivalent martingale measures.
We prove the Fundamental Theorem of Asset Pricing for a discrete time financial market where trading is subject to proportional transaction cost and the asset price dynamic is modeled by a family of probability measures, possibly non-dominated. Using a backward-forward scheme, we show that when the market consists of a…
In this article, inspired by Shi, et al. we investigate the optimal portfolio selection with one risk-free asset and one risky asset in a multiple period setting under cumulative prospect theory (CPT). Compared with their study, our novelty is that we consider a stochastic benchmark, and portfolio constraints. We test …
Paper proposes a CNN model for improved multi-asset portfolio risk prediction.
This paper provides a framework for modeling the financial system with multiple illiquid assets during a crisis. This work generalizes the paper by Amini, Filipovic and Minca (2016) by allowing for differing liquidation strategies. The main result is a proof of sufficient conditions for the existence of an equilibrium …
Deep neural net solves multi-agent optimal trading problem.
This paper extends static hedging for European options over multiple maturities.
We apply the procedure of Lee et al. to the problem of performing inference on the signal-noise ratio of the asset which displays maximum sample Sharpe ratio over a set of possibly correlated assets. We find a multivariate analogue of the commonly used approximate standard error of the Sharpe ratio to use in this condi…
The subject of this paper is an optimal consumption/optimal portfolio problem with transaction costs and with multiple risky assets. In our model the transaction costs take a special form in that transaction costs on purchases of one of the risky assets (the endowed asset) are infinite, and transaction costs involving …
A new VWAP execution method using transformer and signature features.
Generalizes insider trading model to multiple assets.
In this paper, we propose an innovative investment framework incorporating asset allocation and class diversification oriented specifically for the biotechnology industry. With growing interests and capitalization in multiple biotech markets, investors require a more dynamic method of managing their assets within indiv…
Combines multiple asset views with machine learning for better portfolio allocation.
How do individuals accumulate wealth as they interact economically? We outline the consequences of a simple microscopic model in which repeated pairwise exchanges of assets between individuals build the wealth distribution of a population. This distribution is determined for generic exchange rules --- transactions that…