arXiv research

Optimal reinsurance strategy analyzed for dynamic risk model with self- and externally-excited jumps.

We provide a general probabilistic framework within which we establish scaling limits for a class of continuous-time stochastic volatility models with self-exciting jump dynamics. In the scaling limit, the joint dynamics of asset returns and volatility is driven by independent Gaussian white noises and two independent …

We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation. Our new point process allows better approximation in application domains where events and intensities accelerate each other with correlated levels of contagion. We generalize a recent algorithm f…

Paper forecasts financial trading durations using a new point process model.

New model shows negative resilience can improve trading efficiency.

A machine learning framework predicts self-induced stochastic resonance in neurons.

Study optimizes investment strategies in markets with contagious price jumps.

The paper develops a new model for order book dynamics using Hawkes processes.

MAntRA combines machine learning and Bayesian methods for time-dependent reliability analysis of unknown systems.

Targeting a better understanding of credit market dynamics, the authors have studied a stochastic model named the Hawkes process. Describing trades arrival times, this kind of model allows for the capture of self-excitement and mutual interactions phenomena. The authors propose here a simple yet conclusive method for f…

When an online learning algorithm is used to estimate the unknown parameters of a model, the signals interacting with the parameter estimates should not decay too quickly for the optimal values to be discovered correctly. This requirement is referred to as persistency of excitation, and it arises in various contexts, s…

We introduce and show the existence of a Hawkes self-exciting point process with exponentially-decreasing kernel and where parameters are time-varying. The quantity of interest is defined as the integrated parameter $T^{-1}\int_0^Tθ_t^*dt$, where $θ_t^*$ is the time-varying parameter, and we consider the high-frequency…

Researchers develop methods to learn neuron dynamics from colored noise.

The generalized 5D Black-Scholes differential equation with stochastic volatility is derived. The projections of the stochastic evolutions associated with the random variables from an enlarged space or superspace onto an ordinary space can be achieved via higher-dimensional operators. The stochastic nature of the secur…

Develops a new model for multi-currency volatility using CBI-time-changed Lévy processes.

This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech synthesis. First, we predict fundamental frequency and voicing information from MFCCs with an autoreg…

We introduce a new stochastic model for the variations of asset prices at the tick-by-tick level in dimension 1 (for a single asset) and 2 (for a pair of assets). The construction is based on marked point processes and relies on linear self and mutually exciting stochastic intensities as introduced by Hawkes. We associ…

Machine learning speeds up quantum chemical calculations of excited states.

New self-exciting random evolutions (SEREs) for modeling traffic and transport processes.

Researchers prove a new measure for a financial volatility model.

NNNH uses neural networks to model complex event patterns.

Neural waveform models such as the WaveNet are used in many recent text-to-speech systems, but the original WaveNet is quite slow in waveform generation because of its autoregressive (AR) structure. Although faster non-AR models were recently reported, they may be prohibitively complicated due to the use of a distillin…

Paper proposes a neural network for non-parametric Hawkes process kernel estimation.

The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to ge…

The paper models financial data with multivariate jump processes.

Machine learning aids excited-state molecular dynamics studies.

Modeling price formation with interacting Hawkes processes leading to stochastic volatility with leverage.

Paper presents a method for estimating Hawkes process parameters.

In Levin-Wen (LW) models, a wide class of exactly solvable discrete models, for two dimensional topological phases, it is relatively easy to describe only single fluxon excitations, but not the charge and dyonic as well as many-fluxon excitations. To incorporate charged and dyonic excitations in (doubled) topological p…

3D ConvNets improved with Project & Excite for medical imaging segmentation.

We provide existence, uniqueness and stability results for affine stochastic Volterra equations with $L^1$-kernels and jumps. Such equations arise as scaling limits of branching processes in population genetics and self-exciting Hawkes processes in mathematical finance. The strategy we adopt for the existence part is b…

Study of coupled Hawkes processes with rough-volatility limits.

Paper explores ML for UV spectra, showing transferability in chemical space.

Deep QMC method accurately computes electronic excited states.

A new model predicts discrete events with flexible, nonparametric baseline and excitation.

VR methods improve SGD for faster machine learning.

Study models market volatility with persistent and temporary impacts.

New model for clustering dependent community Hawkes processes in temporal networks.

Price changes are induced by aggressive market orders in stock market. We introduce a bivariate marked Hawkes process to model aggressive market order arrivals at the microstructural level. The order arrival intensity is marked by an exogenous part and two endogenous processes reflecting the self-excitation and cross-e…

Lower bounds and upper bounds on sample complexity for identifying linear dynamical systems.

First-passage times in random walks have a vast number of diverse applications in physics, chemistry, biology, and finance. In general, environmental conditions for a stochastic process are not constant on the time scale of the average first-passage time, or control might be applied to reduce noise. We investigate mome…

Paper analyzes coexisting hidden and self-excited attractors in an economic system.

Optimal noise excitation for linear system identification reduces sample complexity.

Hawkes processes are a particularly interesting class of stochastic process that have been applied in diverse areas, from earthquake modelling to financial analysis. They are point processes whose defining characteristic is that they 'self-excite', meaning that each arrival increases the rate of future arrivals for som…

Excited-state dynamics simulations are a powerful tool to investigate photo-induced reactions of molecules and materials and provide complementary information to experiments. Since the applicability of these simulation techniques is limited by the costs of the underlying electronic structure calculations, we develop an…

New algorithm learns LQR with $O(\sqrt{T})$ regret using Langevin dynamics and excitation.

Study on estimating unstable open-loop matrices from state trajectories.

Recent years have witnessed exciting progress in the study of stochastic variance reduced gradient methods (e.g., SVRG, SAGA), their accelerated variants (e.g, Katyusha) and their extensions in many different settings (e.g., online, sparse, asynchronous, distributed). Among them, accelerated methods enjoy improved conv…