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.
Greedy selection works well in a toy model of independent increments.
The paper studies exponential functionals of processes with independent increments and their moments.
For a large class of vanilla contingent claims, we establish an explicit Föllmer-Schweizer decomposition when the underlying is a process with independent increments (PII) and an exponential of a PII process. This allows to provide an efficient algorithm for solving the mean variance hedging problem. Applications to mo…
We consider the problem of maximizing expected utility from terminal wealth in models with stochastic factors. Using martingale methods and a conditioning argument, we determine the optimal strategy for power utility under the assumption that the increments of the asset price are independent conditionally on the factor…
Introduces GIMP processes for multivariate equity derivatives.
We determine the variance-optimal hedge when the logarithm of the underlying price follows a process with stationary independent increments in discrete or continuous time. Although the general solution to this problem is known as backward recursion or backward stochastic differential equation, we show that for this cla…
Anomalous scaling explained by Joseph, Noah, and Moses effects.
The generalized correlation approach, which has been successfully used in statistical radio physics to describe non-Gaussian random processes, is proposed to describe stochastic financial processes. The generalized correlation approach has been used to describe a non-Gaussian random walk with independent, identically d…
We analyze the question whether sliding window time averages applied to stationary increment processes converge to a limit in probability. The question centers on averages, correlations, and densities constructed via time averages of the increment x(t,T)=x(t+T)-x(t)and the assumption is that the increment is distribute…
The fractional Poisson process (FPP) is a counting process with independent and identically distributed inter-event times following the Mittag-Leffler distribution. This process is very useful in several fields of applied and theoretical physics including models for anomalous diffusion. Contrary to the well-known Poiss…
Method predicts LFSM increments from past observations using codifference.
Study of electronic corn futures trading shows discrete price changes and non-Gaussian distributions.
It is well documented that a model for the underlying asset price process that seeks to capture the behaviour of the market prices of vanilla options needs to exhibit both diffusion and jump features. In this paper we assume that the asset price process is Markov with cadlag paths and propose a scheme for computing…
Under the Basel II standards, the Operational Risk (OpRisk) advanced measurement approach is not prescriptive regarding the class of statistical model utilised to undertake capital estimation. It has however become well accepted to utlise a Loss Distributional Approach (LDA) paradigm to model the individual OpRisk loss…
We consider the discretized version of a (continuous-time) two-factor model introduced by Benth and coauthors for the electricity markets. For this model, the underlying is the exponent of a sum of independent random variables. We provide and test an algorithm, which is based on the celebrated Foellmer-Schweizer decomp…
We develop importance sampling based efficient simulation techniques for three commonly encountered rare event probabilities associated with random walks having i.i.d. regularly varying increments; namely, 1) the large deviation probabilities, 2) the level crossing probabilities, and 3) the level crossing probabilities…
Levy processes, which have stationary independent increments, are ideal for modelling the various types of noise that can arise in communication channels. If a Levy process admits exponential moments, then there exists a parametric family of measure changes called Esscher transformations. If the parameter is replaced w…
Improved clustering in mixture models using dependent random measures with independent increments.
We consider a financial market model driven by an R^n-valued Gaussian process with stationary increments which is different from Brownian motion. This driving noise process consists of independent components, and each component has memory described by two parameters. For this market model, we explicitly solve optim…
We introduce incremental variational inference and apply it to latent Dirichlet allocation (LDA). Incremental variational inference is inspired by incremental EM and provides an alternative to stochastic variational inference. Incremental LDA can process massive document collections, does not require to set a learning …
We study the average shape of a fluctuation of a time series x(t), that is the average value <x(t)-x(0)>_T before x(t) first returns, at time T, to its initial value x(0). For large classes of stochastic processes we find that a scaling law of the form <x(t) - x(0)>_T = T^αf(t/T) is obeyed. The scaling function f(s) is…
We discuss martingales, detrending data, and the efficient market hypothesis for stochastic processes x(t) with arbitrary diffusion coefficients D(x,t). Beginning with x-independent drift coefficients R(t) we show that Martingale stochastic processes generate uncorrelated, generally nonstationary increments. Generally,…
Paper proposes a method to learn multiple tasks without forgetting, maintaining model compactness.
Efficiently updates KRR for big streams with minimal redundant computation.
Efficiently processes dynamic inputs in AI writing assistants with incremental computation.
New process from fractional BM and OU process yields simpler variance.
New model learns SDEs without gradient matching for non-uniform time increments.
Develops efficient importance sampling for Lévy process models.
Neural networks learn incrementally from orthogonal data, interpolating with minimal complexity.
A new method samples CGMY processes efficiently by decomposing their time changes.
Incremental ELMVIS learns from large, unorganized datasets.
By applying the multifractal detrended fluctuation analysis to the high-frequency tick-by-tick data from Deutsche Börse both in the price and in the time domains, we investigate multifractal properties of the time series of logarithmic price increments and inter-trade intervals of time. We show that both quantities rev…
Adapts models incrementally for continual changes in environments.
Paper develops a deep neural network for open set incremental learning of new authors.
This paper tackles federated incremental learning with dynamic memory allocation for improved model performance in non-IID data.
In this note we apply the recently established Wiener-Hopf Monte Carlo (WHMC) simulation technique for Levy processes from Kuznetsov et al. [17] to path functionals, in particular first passage times, overshoots, undershoots and the last maximum before the passage time. Such functionals have many applications, for inst…
We consider a square-integrable semimartingale and investigate the convex order relations between its discrete, continuous and predictable quadratic variation. As the main results, we show that if the semimartingale has conditionally independent increments and symmetric jump measure, then its discrete realized variance…
Incremental training method for deep neural networks.
PEC improves class-incremental learning by measuring prediction error.
New method combines population and completion tasks in knowledge graphs.
Reinforcement learning improves uplift modeling's accuracy.
A fast method for decentralized non-convex optimization over networks.
Study shows accuracy of neural networks depends more on error location than percentage of error.
DIVA clusters dynamic data without needing cluster count, outperforming baselines.
Paper introduces a new method for Gaussian Processes that improves prediction and hyper-parameter optimization.
Study on bandits with fading memory, improving regret bounds.
A new method speeds up SVDD training for big data.
Online learning improves state estimation of nonlinear systems.