Model shows financial turbulence similar to turbulence, with wealth cascading from large to small entities.
problem Understanding wealth distribution and dynamics in financial systems.
method Constructed a multiscale model for hierarchical financial structures.
result Found wealth distribution exhibits power law at large scales and Maxwellian at small scales.
We describe financial systems as condensates, similar to Bose-Einstein condensates, and calculate statistical distributions following from the model. The calculated distributions of investments into speculated financial assets are found equivalent to a Pareto distribution, and the calculated distributions of the price …
Deep neural networks forecast financial return distributions accurately.
problem Forecasting probability distributions of financial returns.
method Used 1D CNN and LSTM architectures with custom loss functions to optimize distribution parameters.
result LSTM with skewed Student's t distribution outperformed classical models in multiple evaluation metrics.
The study explains stock return distributions using reaction functions.
problem Stock return distributions often deviate from normal distributions.
method Assumes normal event/information effects, financial over/underreaction, proposes reaction function model.
result Financial markets often underreact to minor events, overreact to significant ones, and react stronger to positive events.
Enhances DyBM for better financial time-series prediction.
problem Limitations of Gaussian DyBM in financial applications.
method Extends DyBM to handle second-order moments and generalized Gaussian distributions.
result Significant performance improvement in predicting financial time-series data.
Paper proposes MMW distribution for better financial risk modeling.
problem Modeling non-normal stock returns for risk estimation.
method Mixture of mirrored Weibull (MMW) distribution for flexible risk modeling.
result MMW model outperforms Gaussian and t-mixture models in VaR estimation.
The thesis models financial returns using mixtures of generalized normal distributions.
problem Estimation issues in financial return analysis.
method Mixtures of generalized normal distributions (MGND), ECM/GEM algorithms, constrained mixture models (CMGND), GND-HMMs.
result Enhanced accuracy and interpretability in financial return modeling.
New method uses DistRL to estimate entire payoff distribution for financial derivatives.
problem Traditional methods focus on expected option value; this tackles risk-aware pricing.
method Reinterprets and proposes a framework using Distributional Reinforcement Learning (DistRL).
result Demonstrates enhanced risk-aware pricing and uncertainty quantification on Asian options.
Study compares various non-Gaussian models for financial returns.
problem Leptokurtic, heavy-tailed financial returns defy Gaussian assumptions.
method Compared and simulated various non-Gaussian models using Monte Carlo.
result Consistency in modeling scaling properties of large price changes.
Improved eigenvalue distribution method for financial data.
problem Noise and complexity in financial markets.
method Matrix H theory, hierarchical structure, informational cascade.
result Captures a larger fraction of data variance in financial markets.
New method uses G-expectation for financial risk measurement.
problem Measuring uncertainty in financial time series.
method Introducing G-normal distribution, applying max-mean estimators, and using autoregressive models.
result G-VaR model outperforms other VaR predictors in risk prediction.
Method calculates financial distributions using recursive relationships.
problem Analyzing the distribution of financial functions at discrete points.
method Recursive method to calculate probability distributions.
result High accuracy demonstrated in numerical experiments.
A new copula minimizes distance between distributions.
problem Arbitrariness in copula choice.
method Minimizes Wasserstein distance; linear programming estimation.
result Natural copula provides parsimonious estimation.
Superstatistics with cut-off tails models financial data with fat tails and cutoffs.
problem Capturing the fat-tailed and cutoff shapes in financial time series.
method Incorporates cut-off effects into superstatistics to model financial data.
result The model accurately describes real financial time series properties.
Develops Bilateral Gamma processes for financial market modeling.
problem Modeling financial market fluctuations with Lévy processes.
method Exploration of bilateral Gamma distributions and their Lévy processes.
result Validates Bilateral Gamma processes on real financial data.
TC-VAE generates robust financial time series data with causal constraints.
problem Generating realistic financial time series data with causal relationships.
method TC-VAE with causality constraint, RealNVP prior, and Wasserstein distance.
result TC-VAE loss controls discrepancy between market and generated distributions.
Novel quantum algorithm for financial market modeling.
problem Accurate quantum state preparation for financial simulation.
method Multi-Split-Steps Quantum Walk (multi-SSQW) with PQC and variational solver.
result Highly accurate modeling of complex financial distributions.
Efficient EP algorithm improves smoothing distribution inference in financial models.
problem Computational intractability of smoothing distribution in high dimensions.
method Adapted expectation propagation (EP) algorithms for the unified skew-normal family.
result Accuracy gains in financial illustrations over existing approximate algorithms.
The paper shows how to construct non-Gaussian Martingales using hyperbolic diffusion.
problem The challenge of modeling extreme financial events.
method Constructing Martingale processes with Cauchy distribution in the large volatility limit.
result Financial justification for using non-Gaussian distributions in modeling extreme events.
The study uses Benford's law to test financial data reliability in developing countries.
problem Detecting and removing anomalies in financial reports.
method Benford's law first significant digit and distribution distances tests.
result Financial data distributions better follow Benford's law after anomalies are removed.
We analyze waiting times for price changes in a foreign currency exchange rate. Recent empirical studies of high frequency financial data support that trades in financial markets do not follow a Poisson process and the waiting times between trades are not exponentially distributed. Here we show that our data is well ap…
This paper uses a kinetic approach to model financial agents and their impact on stock prices and wealth distribution.
problem Understanding power-laws in financial data and the causes behind them.
method A kinetic approach inspired by the Levy-Levy-Solomon model, incorporating model predictive control (MPC) for optimization.
result The stock price distribution exhibits power-law behavior for high-frequency traders and lognormal for long-term investors.
Study analyzes financial distributions and inequality in professional cycling teams.
problem Financial inequality and concentration among cycling teams.
method Rank-size law and various inequality indices applied to Tour de France data.
result Financial gains distribution is hyperbolic with a decay exponent of about -1, contrary to Pareto principle.
Quantum walk model captures asymmetry and bimodality in long-term financial returns.
problem Inadequate classical models for long-term financial return distributions.
method Discrete-time quantum walk model.
result Captures bimodal and asymmetric probability distributions.
We study cross-country GDP losses due to financial crises in terms of frequency (number of loss events per period) and severity (loss per occurrence). We perform the Loss Distribution Approach (LDA) to estimate a multi-country aggregate GDP loss probability density function and the percentiles associated to extreme eve…
Expands robust profit opportunities to include distributional uncertainty.
problem Distributional uncertainty in financial markets.
method Formulates infinite dimensional primal problems, simplifies to finite dimensional dual problems using Wasserstein distance.
result Distributional uncertainty can enhance robustness of profit opportunities.
Proposes a new way to represent uncertainty using implied volatility.
problem Uncertainty in financial markets and biological systems.
method Mathematical analysis of various probability distributions.
result Representation of different probability distributions using BSM implied volatility.
This paper uses multivariate probability models to assess financial system risks.
problem Assessing systemic risk in financial systems.
method Computes multivariate conditional probability distributions for elliptical distributions, focusing on Student-t and Normal models.
result Proposes measures of stress impact and systemic risk.
New model predicts financial tail events using RIA-EVT-Copula.
problem Predicting financial tail events for risk management.
method RIA-EVT-Copula framework combining POT, RIA, and copulas.
result Improved accuracy in predicting financial extremes.
Modeling financial market dynamics with noise and fundamentalist agents.
problem Understanding opinion formation and market behavior in financial markets.
method Agent-based model with Erdös-Rényi random graph structure, incorporating anxiety parameter.
result Model accurately reproduces key market features like fat-tailed returns and volatility clustering.
Proposes a method to model financial returns with extreme shocks using flexible tail transformations.
problem Capturing extreme shocks in financial return data.
method Introduces a transformation layer in normalizing flows to model heavy-tailed distributions.
result Trained models can generate synthetic sets of extreme returns.
Defines g-expectation of distributions and its applications.
problem Defining g-expectation of distributions. method Two special cases of nonlinear g and law-invariant g-expectation. result Explicit derivation of g-expectation of distributions. Study volatility spillovers among many financial assets using a t-distributed VAR model.
problem Understanding volatility spillovers among multiple financial assets.
method Used a large t-Vector AutoRegressive (VAR) model with t-distributed errors for a large number of assets.
result Revealed bidirectional volatility spillovers between energy and biofuel, and between energy and agricultural commodities.
The study tackles modeling high-frequency financial data using continuous distributions, finding them inadequate.
problem Challenges in modeling high-frequency integer price changes with continuous distributions.
method Proposed a modified maximum likelihood estimation procedure to account for the discreteness of high-frequency price changes.
result Traditional GARCH models are not suitable for high-frequency data due to the discreteness of price changes.
This paper characterizes cryptocurrency market behavior using Levy's stable distributions.
problem Modeling price fluctuations in cryptocurrency markets with fat tails and scaling phenomena.
method Characterization using Levy's stable distribution with α≃1.4 under certain time intervals, employing Parseval's relation and GCLT. result Price fluctuations in cryptocurrency markets can be well described by Levy's stable distribution.
Symmetry analysis of financial trends returns reveals bi-modality and multi-scale behavior.
problem Analyzing distributional symmetry in financial trends returns.
method Statistical procedure based on symmetry statistic, applied to daily financial trends returns.
result Daily financial trends returns exhibit bi-modality and multi-scale behavior.
Modeling financial markets with a novel order flow model.
problem Inconsistent parameter values from long-range memory estimators.
method Tsallis q-exponential distribution for limit order cancellation times.
result Improved accuracy in predicting financial market dynamics.
Quantum walks model financial returns with flexibility and asymmetry.
problem Accurate modeling of financial asset price dynamics.
method Discrete-time quantum walks to model asset price evolution.
result Quantum walk models can generate asymmetric return distributions and higher probabilities for extreme events.
Extends extreme value mixture models to identify changepoints in financial extreme regimes.
problem Inference over financial extreme regimes is affected by threshold choice.
method Extends extreme value mixture models to account for distributional extreme changepoints using MCMC algorithms.
result Inclusion of different extreme regimes improves financial applications compared to static and dynamic approaches.
The SV-GARCH-EVT model improves risk assessment in financial markets.
problem Inaccurate risk assessment in financial markets due to fat-tailed and leverage effects.
method Enhanced SV model with EVT for tail distribution, MCMC for parameter estimation.
result SV-EVT models outperform other models in backtesting and out-of-sample analysis.
New measure quantifies financial erratic behavior.
problem Measuring similarity between erratic financial time series.
method Combining probability distributions and Bayesian change point detection.
result Greater similarity among sectors than countries in erratic behavior.
Power laws detected in financial data, modeled with random multipliers.
problem Detecting power laws in financial data.
method Investigated data from financial instruments, proposed a model based on sums of Maxwell-Boltzmann distributions with random multipliers.
result Detected power laws with various exponents in financial data, proposed a universal model.
ANADDH uses deep learning to improve volatility risk management.
problem Traditional Vega hedging strategies are inadequate for rapidly changing markets.
method Combines distributional reinforcement learning with adaptive Nesterov acceleration.
result Significant performance gains over existing hedging techniques.
The probability distribution function (PDF) for prices on financial markets is derived by extremization of Fisher information. It is shown how on that basis the quantum-like description for financial markets arises and different financial market models are mapped by quantum mechanical ones.
Study uses MTD model to optimize portfolios by capturing complex financial asset relationships.
problem Capturing nonlinear and directional relationships in financial markets.
method Directed and weighted financial networks using Mixture Transition Distribution (MTD) model.
result Portfolio optimization with network-based assortativity measures outperforms classical methods.
Measures risk contagion in financial networks using CoVaR.
problem Assessing stability of complex financial systems.
method Financial network model with bipartite graph of institutions and assets, heavy-tailed distributions, copula models, CoVaR and ECI.
result Proposes the Extreme CoVaR Index (ECI) for capturing risk contagion strength.
This paper reviews and compares deep generative models for financial time series and VaR.
problem Forecasting risk factor distribution in financial markets.
method Apply multiple deep generative models (CGAN, CWGAN, Diffusion, Signature WGAN) and propose new methods for conditional time series generation.
result Top performing models are Historical Simulation, GARCH, and CWGAN.
The two phase behavior in financial markets actually means the bifurcation phenomenon, which represents the change of the conditional probability from an unimodal to a bimodal distribution. In this paper, the bifurcation phenomenon in Hang-Seng index is carefully investigated. It is observed that the bifurcation phenom…