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.
Two models incorporate market microstructure noise into asset pricing and option valuation.
In this paper, we build tests for the presence of residual noise in a model where the market microstructure noise is a known parametric function of some variables from the limit order book. The tests compare two distinct quasi-maximum likelihood estimators of volatility, where the related model includes a residual nois…
Using recent advances in the econometrics literature, we disentangle from high frequency observations on the transaction prices of a large sample of NYSE stocks a fundamental component and a microstructure noise component. We then relate these statistical measurements of market microstructure noise to observable charac…
Study uses neural networks to filter financial spillovers from noise.
Sales data in a commodity market (supermarket sales to consumers) has been analysed by studying the fluctuation spectrum and noise correlations. Three related products (ketchup, mayonnaise and curry sauce) have been analysed. Most noise in sales is caused by promotions, but here we focus on the fluctuations in baseline…
We address microscopic, agent based, and macroscopic, stochastic, modeling of the financial markets combining it with the exogenous noise. The interplay between the endogenous dynamics of agents and the exogenous noise is the primary mechanism responsible for the observed long-range dependence and statistical propertie…
Study finds stock and crypto markets tend to be robust, not antifragile.
Modeling financial market dynamics with noise and fundamentalist agents.
Equity options are known to be notoriously difficult to price accurately, and even with the development of established mathematical models there are many assumptions that must be made about the underlying processes driving market movements. As such, the theoretical prices outputted by these models are often slightly di…
Improved market state classification for risk assessment.
A new macroscopic market making model connects market making and optimal execution.
Optimal dynamic fees found for AMMs to deter arbitrageurs and attract noise traders.
Our main task is to study the effect of corporate governance on the market liquidity of listed companies' stocks. We establish a theoretical model that contains the heterogeneity of investors' beliefs to explain the mechanisms by which corporate governance improves liquidity of the corporate stocks. In this process we …
The evolution of prices on ideal market is given by geometrical Brownian motion, where Gaussian white noise describes fluctuations. We study the effect of correlations introduced by a color noise.
Statistical physics method analyzes minority game dynamics in financial markets.
Study shows unique linear equilibrium in market with constrained trader.
We present a novel microscopic stock market model consisting of a large number of random agents modeling traders in a market. Each agent is characterized by a set of parameters that serve to make iterated predictions of two successive returns. The future price is determined according to the offer and the demand of all …
We study a generalization of the Heston model, which consists of two coupled stochastic differential equations, one for the stock price and the other one for the volatility. We consider a cubic nonlinearity in the first equation and a correlation between the two Wiener processes, which model the two white noise sources…
The study tackles rough noise in high-frequency financial data using fractional Brownian motion.
Study optimizes interbank lending and borrowing to reduce systemic risk.
Paper models limit order book with informed traders and market makers.
This paper is a continuation of Ishitani and Kato (2015), in which we derived a continuous-time value function corresponding to an optimal execution problem with uncertain market impact as the limit of a discrete-time value function. Here, we investigate some properties of the derived value function. In particular, we …
A new pricing strategy learns customer valuations without noise distribution knowledge.
This paper provides an alternative approach to Duffie and Lando [Econometrica 69 (2001) 633-664] for obtaining a reduced form credit risk model from a structural model. Duffie and Lando obtain a reduced form model by constructing an economy where the market sees the manager's information set plus noise. The noise makes…
An asymmetric information model is introduced for the situation in which there is a small agent who is more susceptible to the flow of information in the market than the general market participant, and who tries to implement strategies based on the additional information. In this model market participants have access t…
The paper analyzes arbitrage opportunities in a large investor market with common stock noises.
The paper reviews recent statistical methods for financial markets, focusing on jumps, volatility, and microstructure noise.
Market inefficiencies arise from density-dependent returns in a noisy environment.
We find a novel correlation structure in the residual noise of stock market returns that is remarkably linked to the composition and stability of the top few significant factors driving the returns, and moreover indicates that the noise band is composed of multiple subbands that do not fully mix. Our findings allow us …
Paper presents a deep learning method for estimating asset return precision matrices in noisy financial markets.
The existence of the pricing kernel is shown to imply the existence of an ambient information process that generates market filtration. This information process consists of a signal component concerning the value of the random variable X that can be interpreted as the timing of future cash demand, and an independent no…
Improved eigenvalue distribution method for financial data.
Time changes of noise level at Warsaw Stock Market are analyzed using a recently developed method basing on properties of the coarse grained entropy. The condition of the minimal noise level is used to build an efficient portfolio. Our noise level approach seems to be a much better tool for risk estimations than standa…
We study an economic model where agents trade a variety of products by using one of three competing rules: "need", "greed" and "noise". We find that the optimal strategy for any agent depends on both product composition in the overall market and composition of strategies in the market. In particular, a strategy that do…
A new model captures irregularly spaced high-frequency prices and their volatility.
ContestTrade uses competitive teams to improve LLM trading performance.
Study examines NFT market dynamics using correlation and noise analysis.
We analyse the structure of the distribution of eigenvalues of the stock market correlation matrix with increasing length of the time series representing the price changes. We use 100 highly-capitalized stocks from the American market and relate result to the corresponding ensemble of Wishart random matrices. It turns …
Modeling financial chaos with market makers' risk appetite.
Improved volatility estimation using SV-PF-RNN.
We are looking for the agent-based treatment of the financial markets considering necessity to build bridges between microscopic, agent based, and macroscopic, phenomenological modeling. The acknowledgment that agent-based modeling framework, which may provide qualitative and quantitative understanding of the financial…
Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.
Detects crypto pump-and-dump schemes with a thresholding-based model.
Study uses Kalman-Filter to assess market efficiency in major stock markets.
We studied non-dynamical stochastic resonance for the number of trades in the stock market. The trade arrival rate presents a deterministic pattern that can be modeled by a cosine function perturbed by noise. Due to the nonlinear relationship between the rate and the observed number of trades, the noise can either enha…
Method detects jumps in high-frequency order prices using local minima.
We review the recent approach of correlation based networks of financial equities. We investigate portfolio of stocks at different time horizons, financial indices and volatility time series and we show that meaningful economic information can be extracted from noise dressed correlation matrices. We show that the metho…
Study analyzes market equilibrium returns with price impact and transaction costs.