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arXiv research

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.

168,742 papers · 148 categories

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285583110 · Jun 202019922001200920172026
48 results for financial stylized facts

GANs can learn stylized facts of financial time series, but performance varies by architecture.

problem Capturing stylized facts of financial time series using GANs.
method Examination of GANs' ability to learn stylized facts of financial time series, focusing on univariate and multivariate data.
result GANs can capture stylized facts of financial time series, but performance varies by architecture.

Generates financial time series with stylized facts using diffusion models.

problem Generating realistic synthetic financial time series with statistical properties like fat tails, volatility clustering, and seasonality.
method Utilizes denoising diffusion probabilistic models (DDPMs) with wavelet transformation to convert and generate financial time series.
result Demonstrates that the proposed approach satisfies stylized financial time series properties.

SFAG generates realistic financial data that passes trading tests.

problem Financial generative models often produce unrealistic and unstable trading outcomes.
method Introduces SFAG, a GAN variant that aligns stylized facts and optimizes with adversarial loss.
result SFAG generates synthetic data that preserves stylized facts and supports robust trading strategies.

CoFinDiff generates synthetic financial data capturing stylized facts and meeting specified conditions.

problem Limited data availability and difficulty in controlling synthetic financial data generation.
method Conditional diffusion model with cross-attention to incorporate conditions derived from price data.
result Synthetic data generated by CoFinDiff accurately meets specified conditions for trends and volatility.

The Minority Game is a generic model of competing adaptive agents, which is often believed to be a model of financial markets. We discuss to which extend this is a reasonable statement, and present minimal modifications that make this model reproduce stylized facts. The resulting model shows that without speculators, p…

2001-03-01abs ↗pdf ↗

Generative adversarial networks with attention improve financial time series simulation.

problem Limited real financial data for training and evaluation of trading strategies.
method Two generative adversarial networks (GANs) using convolutional networks with attention and transformers.
result Attention-based GANs better reproduce stylized facts and smooth returns autocorrelation.

We present and study a Minority Game based model of a financial market where adaptive agents -- the speculators -- interact with deterministic agents -- called producers. Speculators trade only if they detect predictable patterns which grant them a positive gain. Indeed the average number of active speculators grows wi…

2001-01-22abs ↗pdf ↗

We propose a new method (implemented in an R-program) to simulate long-range daily stock-price data. The program reproduces various stylized facts much better than various parametric models from the extended GARCH-family. In particular, the empirically observed changes in unconditional variance are truthfully mirrored …

2016-12-15abs ↗pdf ↗

In the past decade there has been a growing interest in agent-based econophysical financial market models. The goal of these models is to gain further insights into stylized facts of financial data. We derive the mean field limit of the econophysical model by Cross, Grinfeld, Lamba and Seaman (Physica A, 354) and show …

2017-11-07abs ↗pdf ↗

Generative Adversarial Networks create realistic financial correlation matrices.

problem Creating realistic financial correlation matrices for practical applications.
method Generative Adversarial Networks (GANs) to model correlation matrices.
result GANs can recover known stylized facts about empirical correlation matrices.

Researchers calibrate an adaptive Farmer-Joshi model to recover stylized facts in financial markets.

problem Recovering stylized facts in financial markets using the Farmer-Joshi model.
method Calibrated an adaptive Farmer-Joshi model using genetic and Nelder-Mead algorithms, incorporating agent adaptation.
result The adaptive model recovers additional stylized facts, including auto-correlations and kurtosis, compared to the original model.

Financial markets show a number of non-stationarities, ranging from volatility fluctuations over ever changing technical and regulatory market conditions to seasonalities. On the other hand, financial markets show various stylized facts which are remarkably stable. It is thus an intriguing question to find out how thes…

2018-12-18abs ↗pdf ↗

Study examines stylized facts in DEX markets vs. traditional exchanges.

problem Comparing stylized facts in decentralized exchanges (DEXs) vs. traditional markets.
method Empirical analysis of 24 most active Uniswap v3 pools.
result New statistical regularities in DEX markets, linked to market structure and activity.

Study of 2D Ising model reveals patterns in financial markets.

problem Understanding stylized facts in financial markets using statistical physics.
method 2D Ising model with spin interactions; analysis of spin clusters, persistence, and dynamics.
result Microscopic mechanisms explain stylized facts like sharp peaks in returns and heavy-tailed distributions.

Study compares Web3 tokens to traditional assets, finding similar statistical properties.

problem Understanding statistical properties of Web3 tokens compared to traditional financial assets.
method Statistical analysis of various Web3 tokens across multiple time scales, comparing with traditional financial assets.
result Most Web3 tokens exhibit similar stylized facts to traditional financial assets, including heavy tails and volatility clustering.

Two new models for volatility in Markov-switching environments capture financial time-series properties.

problem Modeling volatility in environments with regime switches and exogenous jumps.
method Generalizations of COGARCH and Barndorff-Nielsen-Shephard models using Markov-modulated generalized Ornstein-Uhlenbeck processes.
result Models inherit properties of original models and capture stylized facts of financial time-series.

In this work we afford the statistical characterization of a linear Stochastic Volatility Model featuring Inverse Gamma stationary distribution for the instantaneous volatility. We detail the derivation of the moments of the return distribution, revealing the role of the Inverse Gamma law in the emergence of fat tails,…

2010-11-27abs ↗pdf ↗

We find a nonlinear dependence between an indicator of the degree of multiscaling of log-price time series of a stock and the average correlation of the stock with respect to the other stocks traded in the same market. This result is a robust stylized fact holding for different financial markets. We investigate this re…

2018-02-04abs ↗pdf ↗

Financial time series have been investigated to follow fat-tailed distributions. Further, an empirical probability distribution sometimes shows cut-off shapes on its tails. To describe this stylized fact, we incorporate the cut-off effect in superstatistics. Then we confirm that the presented stochastic model is capabl…

2018-09-13abs ↗pdf ↗

Prices in financial markets exhibit extreme jumps far more often than can be accounted for by external news. Further, magnitudes of price changes are correlated over long times. These so called stylized facts are quantified by scaling laws similar to, for example, turbulent fluids. They are believed to reflect the comp…

2013-03-08abs ↗pdf ↗

The paper estimates CoVaR with various models for financial risk analysis.

problem Estimating conditional value-at-risk with financial time series data.
method Fitting multivariate parametric models and copula functions to capture stylized facts of equity returns.
result Backtesting shows that certain models provide better risk estimates than others.