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.
Optimal portfolios are formed by combining momentum, size, and volatility characteristics, enhancing utility for all investors.
A neural network method estimates densities from characteristic functions.
New methods for estimating ARMA and GARCH models with stable noise.
A new model for stock price fluctuations is proposed, based upon an analogy with the motion of tracers in Gaussian random fields, as used in turbulent dispersion models and in studies of transport in dynamically disordered media. Analytical and numerical results for this model in a special limiting case of a single-sca…
Using high frequency data, we have studied empirically the change of volatility, also called volatility derivative, for various time horizons. In particular, the correlation between the volatility derivative and the volatility realized in the next time period is a measure of the response function of the market particip…
Time-subordinated Brownian motion models improve financial market stochastic distribution.
For any strictly positive martingale for which has a characteristic function, we provide an expansion for the implied volatility. This expansion is explicit in the sense that it involves no integrals, but only polynomials in the log strike. We illustrate the versatility of our expansion by computing t…
Changes (returns) in stock index prices and exchange rates for currencies are argued, based on empirical data, to obey a stable distribution with characteristic exponent for short sampling intervals and a Gaussian distribution for long sampling intervals. In order to explain this phenomenon, an Ehrenfest model…
Calibrating a Lévy process usually requires characterizing its jump distribution. Traditionally this problem can be solved with nonparametric estimation using the empirical characteristic functions (ECF), assuming certain regularity, and results to date are mostly in 1D. For multivariate Lévy processes and less smooth …
A non-parametric method for evaluation of the aggregate loss distribution (ALD) by combining and numerically inverting the empirical characteristic functions (CFs) is presented and illustrated. This approach to evaluate ALD is based on purely non-parametric considerations, i.e., based on the empirical CFs of frequency …
The paper tackles drift identification in Lévy α-stable stochastic systems, proposing a Fourier space approach.
The multivariate version of the Mixed Tempered Stable is proposed. It is a generalization of the Normal Variance Mean Mixtures. Characteristics of this new distribution and its capacity in fitting tails and capturing dependence structure between components are investigated. We discuss a random number generating procedu…
We propose a class of nonparametric two-sample tests with a cost linear in the sample size. Two tests are given, both based on an ensemble of distances between analytic functions representing each of the distributions. The first test uses smoothed empirical characteristic functions to represent the distributions, the s…
We address the problem of long-range memory in the financial markets. There are two conceptually different ways to reproduce power-law decay of auto-correlation function: using fractional Brownian motion as well as non-linear stochastic differential equations. In this contribution we address this problem by analyzing e…
Unified model for financial derivatives pricing with stochastic interest rates.
ECS evaluates synthetic CXR images' distributional fidelity.
Generative neural network simulates characteristic functions.
Systemic risks of default contagion in the Russian interbank market are investigated. The analysis is based on considering the bow-tie structure of the weighted oriented graph describing the structure of the interbank loans. A probabilistic model of interbank contagion explicitly taking into account the empirical bow-t…
Using a large database of 8 million institutional trades executed in the U.S. equity market, we establish a clear crossover between a linear market impact regime and a square-root regime as a function of the volume of the order. Our empirical results are remarkably well explained by a recently proposed dynamical theory…
The paper explains how to predict returns based on firm characteristics.
Study finds managers' tenure and education influence their choice between in-court and out-of-court restructuring.
A novel version of the Continuous-Time Random Walk (CTRW) model with memory is developed. This memory means the dependence between arbitrary number of successive jumps of the process, while waiting times between jumps are considered as i.i.d. random variables. The dependence was found by analysis of empirical histogram…
Proposes a new method to describe graph vertex features using characteristic functions.
A coloring scheme improves graph neural networks for node disambiguation.
In this study, the effects of eight representation regularization methods are investigated, including two newly developed rank regularizers (RR). The investigation shows that the statistical characteristics of representations such as correlation, sparsity, and rank can be manipulated as intended, during training. Furth…
Online minimization of an unknown convex function over the interval is considered under first-order stochastic bandit feedback, which returns a random realization of the gradient of the function at each query point. Without knowing the distribution of the random gradients, a learning algorithm sequentially choo…
Proves a generalized table theorem for odd Euler characteristic surfaces.
Improved bounds for Monte Carlo Rademacher Averages using self-bounding functions.
This paper connects functional data analysis with machine learning techniques.
Scene understanding and semantic segmentation are at the core of many computer vision tasks, many of which, involve interacting with humans in potentially dangerous ways. It is therefore paramount that techniques for principled design of robust models be developed. In this paper, we provide analytic and empirical evide…
Decentralized learning for GLMs with feature distribution and network connectivity.
The paper defines the time function of stock prices using a mathematical model.
We report an empirical study of Tehran Price Index (TEPIX). To analyze our data we use various methods like as, rescaled range analysis (), modified rescaled range analysis (Lo's method), Detrended Fluctuation Analysis (DFA) and generalized Hurst exponents analysis. Based on numerical results, the scaling range of…
Study compares parametric and Hermite-based models for option pricing.
This paper presents the asymptotic behavior of a linear instrumental variables (IV) estimator that uses a ridge regression penalty. The regularization tuning parameter is selected empirically by splitting the observed data into training and test samples. Conditional on the tuning parameter, the training sample creates …
In this note, we derive the characteristic function expansion for logarithm of the underlying asset price in corrected Heston model as proposed by Fouque and Lorig.
A new trading strategy using reinforcement learning for statistical arbitrage.
We derive a general multivariate theory for realised characteristics of `model-free discretisation-invariant swaps', so-called because the standard no-arbitrage assumption of martingale forward prices is sufficient to derive fair-value swap rates for such characteristics which have no jump or discretisation errors. Thi…
Develops a dynamic latent-factor model for high-dimensional asset characteristics.
Improved eigenvalue distribution method for financial data.
We discuss a notion of integration with respect to the Euler characteristic in the projectivization $¶{\cal O}_{\C^n,0}$ of the ring ${\cal O}_{\C^n,0}$ of germs of functions on and show that the Alexander polynomial and the zeta-function of a plane curve singularity can be expressed as certain integrals over $¶{…
We derive a higher-order expansion for rough volatility models.
The paper simulates Lévy processes and their extremum and hitting time.
The CGMY model's ATM call-price asymptotics are derived using characteristic function.
We review the complex differential geometry of the space of oriented affine lines in and give a description of Hamilton's characteristic functions for reflection in an oriented C surface in terms of this geometry.
This paper improves fund net value prediction using ARIMA-LSTM hybrid model.
Study provides explicit pricing formula for options with volatility dependent on short rate.
The paper develops a hybrid model for optimal order execution in markets with heterogeneous market makers.