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.
Paper presents characteristic function of Tsallis q-Gaussian and its applications.
The paper models stock returns using -Gaussians and negative binomials.
New Stein identity for q-Gaussians reduces gradient variance in machine learning.
This work models financial market returns with asymmetric Tsallis distributions, improving fit over symmetric q-Gaussians.
Fractional porous media equations yield q-Gaussian solutions for stock price returns.
This paper presents an empirical investigation of the intraday Brazilian stock market price fluctuations, considering q-Gaussian distributions that emerge from a non-extensive statistical mechanics. Our results show that, when returns are measured over intervals less than one hour, the empirical distributions are well …
In this communication, we describe some interrelations between generalized -entropies and a generalized version of Fisher information. In information theory, the de Bruijn identity links the Fisher information and the derivative of the entropy. We show that this identity can be extended to generalized versions of en…
We provide evidence that cumulative distributions of absolute normalized returns for the American companies with the highest market capitalization, uncover a critical behavior for different time scales . Such cumulative distributions, in accordance with a variety of complex --and financial-- systems, can be m…
Researchers explore gauge freedom in entropies of -Gaussian measures.
We analyze the Standard & Poor's 500 stock market index from the last 22 years. The probability density function of price returns exhibits two well-distinguished regimes with self-similar structure: the first one displays strong super-diffusion together with short-time correlations, and the second one corresponds to we…
A new algorithm enhances minority class representation in imbalanced datasets.
Optimal control in latent factor models uses Tsallis entropy for exploration.
Estimate relaxation times in nonextensive systems using gradient flow for Tsallis entropy maximization.
The paper shows that detrended stock price returns are stationary.
New method calibrates reference distributions for bounded support.
The behavior of stock market returns over a period of 1-60 days has been investigated for S&P 500 and Nasdaq within the framework of nonextensive Tsallis statistics. Even for such long terms, the distributions of the returns are non-Gaussian. They have fat tails indicating that the stock returns do not follow a random …
Study on price fluctuations and persistence in European electricity spot markets.
We propose a modified -divergence, give some of its properties, and show that this leads to the definition of a generalized Fisher information. We give generalized Cramér-Rao inequalities, involving this Fisher information, an extension of the Fisher information matrix, and arbitrary norms and power of the estimat…
This research improves value-at-risk estimation during financial crises using non-extensive statistical methods.
We present a systematic study of various statistical characteristics of high-frequency returns from the foreign exchange market. This study is based on six exchange rates forming two triangles: EUR-GBP-USD and GBP-CHF-JPY. It is shown that the exchange rate return fluctuations for all the pairs considered are well desc…
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…
Engle's ARCH algorithm is a generator of stochastic time series for financial returns (and similar quantities) characterized by a time-dependent variance. It involves a memory parameter ( corresponds to {\it no memory}), and the noise is currently chosen to be Gaussian. We assume here a generalized noise, name…
There is convincing evidence showing that the probability distributions of stock returns in mature markets exhibit power-law tails and both the positive and negative tails conform to the inverse cubic law. It supports the possibility that the tail exponents are universal at least for mature markets in the sense that th…
The study analyzes river water quality using statistical and machine learning methods.
The algorithm is the most renowned generalisation of Engle's original proposal for modelising {\it returns}, the process. Both cases are characterised by presenting a time dependent and correlated variance or {\it volatility}. Besides a memory parameter, , (present in ) and an independent and id…
Analyzes financial return distributions over various time scales.
Recently, Mike and Farmer have constructed a very powerful and realistic behavioral model to mimick the dynamic process of stock price formation based on the empirical regularities of order placement and cancelation in a purely order-driven market, which can successfully reproduce the whole distribution of returns, not…
The study of dexterous manipulation has provided important insights in humans sensorimotor control as well as inspiration for manipulation strategies in robotic hands. Previous work focused on experimental environment with restrictions. Here we describe a method using the deformation and color distribution of the finge…
In a recent paper [\textit{M. Cristelli, A. Zaccaria and L. Pietronero, Phys. Rev. E 85, 066108 (2012)}], Cristelli \textit{et al.} analysed relation between skewness and kurtosis for complex dynamical systems and identified two power-law regimes of non-Gaussianity, one of which scales with an exponent of 2 and the oth…
New method uses asymmetric Tsallis relative entropy for better risk assessment in financial portfolios.
Analyzed Bitcoin market index volatility changes over two distinct periods using anomalous diffusion and multifractal analysis.
Study uses detrended cross-correlation to analyze cryptocurrency market, revealing robust collective modes and distinguishing interdependencies.
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…
Forecasting stock market decline and recovery post-COVID-19.
The paper explores solutions to the distributional Bellman equation in reinforcement learning.
Proposes vMF distribution for skewed elliptical distributions.
This paper examines how the choice of prior distribution affects likelihoods of out-of-distribution inputs in deep generative models.
Study calculates tail risk for various mixture distributions.
We realise the first and second Grushin distributions as symmetry reductions of the 3-dimensional Heisenberg distribution and 4-dimensional Engel distribution respectively. Similarly, we realise the Martinet distribution as an alternative symmetry reduction of the Engel distribution. These reductions allow us to derive…
Method uses optimal transport to complete distributional matrices.
Income and wealth distribution affect stability of a society to a large extent and high inequality affects it negatively. Moreover, in the case of developed countries, recently has been proven that inequality is closely related to all negative phenomena affecting society. So far, Econophysics papers tried to analyse in…
Study clusters distributions with known or unknown clusters using distribution testing.
Gradually Truncated Log-normal distribution - Size distribution of firms Abstract Many natural and economical phenomena are described through power law or log- normal distributions. In these cases, probability decreases very slowly with step size compared to normal distribution. Thus it is essential to cut-off these di…
A new distribution family extends the -stable distribution with a degree of freedom parameter.
Paper develops a new method to improve model calibration under distribution shifts.
A new clustering method preserves data distribution.
New class of heavy-tailed distributions shows weighted averages dominate individual variables.
Researchers derived formulas for joint moments of elliptical distributions.