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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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51103154205 · Jun 202019922001200920172026
48 results for Gaussian fluctuations

Spectral clustering performance depends on eigenvector fluctuations, shown to be Gaussian.

problem Predicting the performance of spectral clustering.
method General spike random matrix model and rotational invariance of noise.
result Fluctuations of eigenvector entries are Gaussian in large-dimensional regime.

The paper shows Gaussian fluctuations in eigenvalue statistics of random hyperbolic surfaces.

problem Understanding fluctuations in Laplace eigenvalues of random hyperbolic surfaces.
method Analyzing fluctuations of linear statistics of Laplace eigenvalues over moduli space of surfaces of large genus.
result The distribution of linear statistics tends to a Gaussian as the genus of surfaces increases.

The daily volume of transaction on the New York Stock Exchange and its day-to-day fluctuations are analysed with respect to power-law tails as well long-term trends. We also model the transition to a Gaussian distribution for longer time intervals, like months instead of days.

2006-03-21abs ↗pdf ↗

We analyze the fluctuation of the loss from default around its large portfolio limit in a class of reduced-form models of correlated firm-by-firm default timing. We prove a weak convergence result for the fluctuation process and use it for developing a conditionally Gaussian approximation to the loss distribution. Nume…

2013-04-04abs ↗pdf ↗

Study analyzes fluctuations in Mexican financial market index.

problem Understanding intra-day fluctuations in Mexican financial market index.
method Statistical analysis of high frequency tick-to-tick data, temporal aggregation, and comparison of distributions.
result Intra-day fluctuations do not follow alpha-stable distributions, suggesting autocorrelations.

The paper provides exact multivariate amplitude distributions for non-stationary Gaussian or algebraic fluctuations.

problem Capturing the statistical properties of fluctuating correlations in non-stationary systems.
method Developed a random matrix model to average multivariate amplitude distributions from short time scales to large time scales.
result Explicit multivariate distributions for non-stationary correlation systems are provided, capturing the degree of non-stationarity.

We study the nature of fluctuations in variety of price indices involving companies listed on the New York Stock Exchange. The fluctuations at multiple scales are extracted through the use of wavelets belonging to Daubechies basis. The fact that these basis sets satisfy vanishing moments conditions makes them ideal to …

2012-05-08abs ↗pdf ↗

Truncated Lévy flights are random walks in which the arbitrarily large steps of a Lévy flight are eliminated. Since this makes the variance finite, the central limit theorem applies, and as time increases the probability distribution of the increments becomes Gaussian. Here, truncated Lévy flights with correlated fluct…

1999-06-28abs ↗pdf ↗

The paper analyzes the non-Gaussian behavior of inflation and unemployment over 70 years using multifractal methods.

problem Capturing unusual fluctuations in inflation and unemployment over long periods.
method Coupled multifractal approach to analyze non-Gaussian distributions of inflation and unemployment over 70 years.
result The non-Gaussianity of unemployment is noticeable only for periods smaller than 1 year, while inflation's non-Gaussianity persists across all time scales.

Method extracts stochastic systems with Lévy noise from data.

problem Identifying stochastic dynamical systems with Lévy noise from short data.
method Estimate Lévy jump measure and noise intensity, approximate drift coefficient.
result Accurate and effective method for discovering stochastic laws.

Bayesian models' singular fluctuation is shown to be akin to specific heat, influencing model complexity and generalization.

problem Understanding the thermodynamic interpretation of singular fluctuation in Bayesian models.
method Showed singular fluctuation as the curvature of Bayesian free energy and variance of log-likelihood observable under a Gibbs posterior.
result Singular fluctuation is the statistical analogue of specific heat, controlling model complexity and generalization.

Study uses detrended cross-correlation to analyze cryptocurrency market, revealing robust collective modes and distinguishing interdependencies.

problem Nonstationarity, long-range memory, and heavy-tailed fluctuations obscure traditional correlations in complex systems.
method Constructs detrended correlation matrices using multifractal detrended cross-correlation coefficient ρrρ_r to emphasize different fluctuations.
result Detrending and fluctuation analysis reveal distinct spectral properties from random case, identifying market and sectoral components.

Sornette et al. claimed that the optimal supply does not agree with the average demand, by analyzing a bakery model where a daily demand fluctuates with a uniform distribution. In this note, we extend the model to general probability distributions, and obtain the formula of the optimal supply for Gaussian distribution,…

2005-03-29abs ↗pdf ↗

We consider small-time asymptotics for diffusion processes conditioned by their initial and final positions, under the assumption that the diffusivity has a sub-Riemannian structure, not necessarily of constant rank. We show that, if the endpoints are joined by a unique path of minimal energy, and lie outside the sub-R…

2015-05-13abs ↗pdf ↗

Derives scaling limits and fluctuations for SGD in high dimensions.

problem Understanding SGD behavior in high-dimensional settings with varying noise levels.
method Interacting particle system approach, treating SGD iterates as such, with covariance structure considered.
result Precise three-step phase transition observed in SGD behavior: ballistic, diffusive, then random.

Bayesian neural networks explore rare fluctuations for better feature learning.

problem Understanding rare but dominant fluctuations in Bayesian neural networks.
method Large-deviation theory and joint optimization over predictors and internal kernels.
result Posterior rate function optimization reveals data-dependent kernel selection.

Deep networks with orthogonal weights show stable fluctuations, improving generalization and training speed.

problem Fluctuations in deep networks with Gaussian weights can impair training, especially in networks with depth comparable to width.
method Analytical and numerical studies of fully-connected networks with orthogonal weight initialization and tanh activations.
result Rectangular networks with orthogonal weights have stable fluctuations independent of network depth, leading to better generalization and training speed.

Framework predicts nonlinear system responses using GFDT and generative models.

problem Predicting higher-order moments of nonlinear stochastic systems to small perturbations.
method Combining GFDT with generative modeling to estimate score function directly from data.
result Accurately captures nonlinear and non-Gaussian features of system responses.

The question of optimal portfolio is addressed. The conventional Markowitz portfolio optimisation is discussed and the shortcomings due to non-Gaussian security returns are outlined. A method is proposed to minimise the likelihood of extreme non-Gaussian drawdowns of the portfolio value. The theory is called Leptokurti…

2005-04-18abs ↗pdf ↗

Study on price fluctuations and persistence in European electricity spot markets.

problem Analyzing variability and persistence of electricity prices in European spot markets.
method Analysis of hourly, intraday, and 15-min intraday market prices; quantification of fluctuations, correlations, and extreme events; classification into circulation weather types.
result Different time scales in market dynamics; multifractal behavior below 12 hours; anti-correlation and mean reversion above 12 hours; long-term behavior influenced by four-day weather patterns; qq-Gaussian distributions as best fit.

We fit the volatility fluctuations of the S&P 500 index well by a Chi distribution, and the distribution of log-returns by a corresponding superposition of Gaussian distributions. The Fourier transform of this is, remarkably, of the Tsallis type. An option pricing formula is derived from the same superposition of Black…

2007-08-22abs ↗pdf ↗

Factorial moments are convenient tools in nuclear physics to characterize the multiplicity distributions when phase-space resolution (ΔΔ) becomes small. For uncorrelated particle production within ΔΔ, Gaussian statistics holds and factorial moments FqF_q are equal to unity for all orders qq. Correlations between par…

2011-08-29abs ↗pdf ↗

We report on a study of the Tehran Price Index (TEPIX) from 2001 to 2006 as an emerging market that has been affected by several political crises during the recent years, and analyze the non-Gaussian probability density function (PDF) of the log returns of the stocks' prices. We show that while the average of the index…

2007-06-11abs ↗pdf ↗

The paper compares Bayesian uncertainty to MAP estimator in random features regression.

problem Comparing Bayesian uncertainty to MAP estimator in random features regression.
method Analyzing the variance of the posterior predictive distribution and comparing it to the risk of the MAP estimator.
result Asymptotic agreement between Bayesian uncertainty and MAP estimator under specific signal-to-noise ratios and sample sizes.

Detailed empirical studies of publicly traded business firms have established that the standard deviation of annual sales growth rates decreases with increasing firm sales as a power law, and that the sales growth distribution is non-Gaussian with slowly decaying tails. To explain these empirical facts, a theory is dev…

2007-03-02abs ↗pdf ↗

We rigorously prove a central limit theorem for neural network models with a single hidden layer. The central limit theorem is proven in the asymptotic regime of simultaneously (A) large numbers of hidden units and (B) large numbers of stochastic gradient descent training iterations. Our result describes the neural net…

2018-08-28abs ↗pdf ↗

We introduce thermodynamic response functions for singular Bayesian models.

problem Singular Bayesian models violate regular asymptotics due to non-identifiability and degenerate Fisher geometry.
method Posterior tempering induces thermodynamic response functions, linking WAIC, WBIC, and singular fluctuation.
result WAIC, WBIC, and singular fluctuation are unified within a thermodynamic response framework.

Study smooth linear statistics on random covers of hyperbolic surfaces, showing central limit and variance results.

problem Analyzing fluctuations and energy variance of random covers of compact hyperbolic surfaces.
method Examining fluctuations in a small energy window around a fixed energy level, considering the variance of a typical surface, using a double limit where nn and LL go to infinity.
result Distribution of fluctuations tends to a Gaussian with variance of GOE/GUE, and energy variance of a typical random nn-cover is that of GOE/GUE.

From the stock markets of six countries with high GDP, we study the stock indices, S&P 500 (NYSE, USA), SSE Composite (SSE, China), Nikkei (TSE, Japan), DAX (FSE, Germany), FTSE 100 (LSE, Britain) and NIFTY (NSE, India). The daily mean growth of the stock values is exponential. The daily price fluctuations about the me…

2019-06-30abs ↗pdf ↗

The statistics of return distributions on various time scales constitutes one of the most informative characteristics of the financial dynamics. Here we present a systematic study of such characteristics for the Polish stock market index WIG20 over the period 04.01.1999 - 31.10.2005 for the time lags ranging from one m…

2006-03-09abs ↗pdf ↗

We study the dynamics of exchange value in a system composed of many interacting agents. The simple model we propose exhibits cooperative emergence and collapse of global value for individual goods. We demonstrate that the demand that drives the value exhibits non Gaussian "fat tails" and typical fluctuations which gro…

1999-06-18abs ↗pdf ↗

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…

2004-12-07abs ↗pdf ↗

Options are financial instruments that depend on the underlying stock. We explain their non-Gaussian fluctuations using the nonextensive thermodynamics parameter qq. A generalized form of the Black-Scholes (B-S) partial differential equation, and some closed-form solutions are obtained. The standard B-S equation ($q=1…

2002-04-15abs ↗pdf ↗

The paper analyzes variance reduction in stochastic gradient Langevin dynamics.

problem Reducing the variance of stochastic gradient estimators in Langevin dynamics.
method Central limit theorem and Poisson equation analysis for variance characterization.
result Anti-symmetric perturbations can reduce the variance of non-reversible Langevin dynamics.