In this paper, we obtain the finite-horizon and infinite-horizon ruin probability asymptotics for risk processes with claims of subexponential tails for non-stationary arrival processes that satisfy a large deviation principle. As a result, the arrival process can be dependent, non-stationary and non-renewal. We give t…
Study approximates risk process with non-stationary claims using Hawkes process.
problem Approximating risk process with non-stationary claims.
method Gaussian approximation and functional central limit theorem.
result Established diffusion approximation for ruin probability.
This paper tackles inventory control with general arrival dynamics and post-processing, improving profitability.
problem Inventory control with arbitrary arrival dynamics and post-processing constraints.
method Formulated as an exogenous decision process, incorporating deep generative models for arrivals, and applying supervised learning techniques.
result Improves profitability over production baselines and real-world A/B test data.
This paper extends subordinated models to include stochastic time changes, improving financial modeling.
problem Improving financial models to better capture market features like jump clustering and volatility persistence.
method Subordinated processes with Levy and stochastic arrival mechanisms.
result Strong consistency and asymptotic normality results for VG and VGSA processes under various stochastic arrival models.
Algorithm solves job acceptance problem with random arrivals and values.
problem Decision-making under random job arrivals and values with limited acceptance.
method Proposes Non-Parametric Sequential Allocation (NPSA) algorithm.
result Expected reward converges to optimality as sample size increases.
Deep neural networks improve angle of arrival estimation with lower complexity.
problem Estimating the number of sources and their angles of arrival from a single antenna array observation.
method Apply a deep neural network (DNN) approach to the problem.
result Deep neural networks can attain maximum likelihood performance with feasible complexity and outperform other methods.
Estimates transaction arrival patterns in intraday electricity markets.
problem Estimating transaction arrival processes in intraday electricity markets.
method Model inter-arrivals using multiple time-varying parametric densities based on the generalized F distribution.
result Significant insights into model fit and prediction accuracy evaluated by various metrics.
New framework for sequential experiments with unknown data arrival.
problem Sequential decision-making with unknown information arrival.
method Generalized MAB framework for arbitrary arrival processes.
result Upper and lower bounds on minimax complexities.
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…
SMURF-THP improves Transformer Hawkes process models by providing uncertainty quantification.
problem Uncertainty quantification for Transformer Hawkes process predictions.
method Score matching for learning the score function of event arrival times.
result SMURF-THP outperforms likelihood-based methods in confidence calibration.
Study of bandit problem with Poisson decision times and Lévy processes.
problem Continuous-time multi-armed bandit problem with Poisson decision times.
method Gittins index policy applied to spectrally one-sided Lévy processes.
result Gittins index converges to classical Lévy bandit index.
New method infers and samples point processes from latent diffusion.
problem Modeling point processes with latent diffusion.
method Itô's excursion theory for inference and sampling.
result Proposes a new method to infer and sample point processes.
Study on queues with Hawkes arrivals, proving steady-state behavior and developing an efficient algorithm.
problem Analyzing the steady-state behavior of queues with Hawkes arrivals.
method Novel coupling techniques and exponential convergence results for workload and busy period processes.
result Exponential convergence of queueing processes to their stationary distribution.
Solves online resource allocation problems with budget constraints.
problem Maximizing revenue for e-commerce platforms under budget constraints.
method Integrated online optimization and learning algorithm for non-stationary Poisson processes.
result Effective and efficient solutions for constrained resource allocation problems.
We investigate, focusing on the ruin probability, an adaptation of the Cramer-Lundberg model for the surplus process of an insurance company, in which, conditionally on their intensities, the two mixed Poisson processes governing the arrival times of the premiums and of the claims respectively, are independent. Such a …
A new method estimates nonhomogeneous Poisson process intensities with super-resolution.
problem Estimating cyclic arrival rates of nonhomogeneous Poisson processes.
method Super-resolution estimation using sinusoidal waves with unknown parameters.
result Finite sample guarantees for super-resolution estimation under suitable conditions.
Space debris warnings follow a predictable pattern, allowing timely satellite maneuvers.
problem Estimating when fresh information about space debris will arrive.
method Statistical learning model of the message arrival process, specifically a Bayesian Poisson process.
result The average prediction error for the next message arrival time is smaller than baseline predictions.
A new model for limit order book dynamics with time-dependent arrival rates.
problem Modeling the dynamics of limit order books with time-dependent arrival rates.
method Proposes a stochastic model with endogenous price dynamics and shows the conditional diffusion limit is Brownian meander.
result The model's conditional diffusion limit is the Brownian meander.
Modeling aggressive market order arrivals using Hawkes factor models.
problem Aggressive market order placements and their impact on stock prices.
method Bivariate marked Hawkes process with self-excitation and cross-excitation components.
result The Hawkes model with an exponential kernel produces better calibration than a monotonous exponential kernel.
Study models market volatility with persistent and temporary impacts.
problem Microstructure of rough volatility models driven by Poisson measures.
method Existence and uniqueness of solutions for stochastic path-dependent Volterra equations.
result Volatility process converges to fractional Heston model with spikes.
Optimal fund deployment strategy under uncertain deal arrivals.
problem Deciding when to invest in deals with uncertain future arrivals.
method Formulated as CTMDP, solved via ADP with QMC sampling.
result Developed interpretable acceptance policy outperforming baseline.
Sequential screening and dynamic regret in multi-armed bandits with arriving arms
problem Sequential experimentation with expanding arm set
method UCB-AA with preliminary screening
result Regret bounds depend on arrival process
We introduce a multivariate Hawkes process that accounts for the dynamics of market prices through the impact of market order arrivals at microstructural level. Our model is a point process mainly characterized by 4 kernels associated with respectively the trade arrival self-excitation, the price changes mean reversion…
Proposes a new simulator for complex arrival processes.
problem Modeling and simulating complex arrival processes with non-stationary and multi-dimensional rates.
method Integrates Monte Carlo and GANs to model a broad class of arrival processes.
result Consistent and efficient estimation of the simulator using Wasserstein distance.
Researchers use Hawkes processes to analyze credit trades, revealing self-excitement and volume impacts.
problem Understanding the dynamics of credit market trades and their interactions.
method Simple method for fitting multidimensional Hawkes processes with exponential kernels using maximum likelihood non-convex optimization.
result Quantification of self-excitement and volume impacts in credit trades.
This paper evaluates various bus arrival time prediction models.
problem Improving prediction accuracy of bus arrival times.
method General evaluation framework for various models, including raw data pre-processing.
result Preliminary results show strengths and weaknesses of common models.
We examine the dynamics of the bid and ask queues of a limit order book and their relationship with the intensity of trade arrivals. In particular, we study the probability of price movements and trade arrivals as a function of the quote imbalance at the top of the limit order book. We propose a stochastic model in an …
The paper optimizes portfolios in a market with hidden drift and random expert opinions.
problem Optimizing portfolios in a market with hidden Gaussian drift and random expert signals.
method Modeling the hidden drift using Kalman filters and solving the utility maximization problem with dynamic programming.
result Derivation of optimal portfolio weights and utility maximization under the given market conditions.
Establishes a microstructural foundation for a rough log-normal volatility model.
problem Developing a robust model for financial volatility under microstructural effects.
method Introduced a sequence of order-driven financial market models with Poisson process arrivals and analyzed their convergence to a log-normal rough volatility model.
result Weak convergence of price-volatility process to a log-normal rough volatility model with established weak error rates.
The paper studies how expert opinions improve stock return predictions in a market with a hidden drift.
problem Improving stock return predictions in a market with a hidden Gaussian drift.
method Uses Kalman filter techniques to estimate the hidden drift from noisy expert opinions and stock returns.
result The Kalman filter estimates of the drift converge to the hidden drift as the frequency of expert opinions increases.
Study ruin probabilities in risk processes on stochastic networks.
problem Ruin probabilities in risk processes on stochastic networks.
method Classification of agents by types, Poisson process for loss propagation, explicit ruin probabilities for infinite network size.
result Explicit ruin probabilities for agents of any type in infinite network size.
Modeling high-frequency order book data with Hawkes-Markovian process.
problem Capturing the dynamics of high-frequency order book events.
method Hawkes process with Markovian baseline intensities, LASSO regularization, and Akaike Information Criteria.
result Effective modeling of order book dynamics with reduced parameter redundancy.
Study on Parisian ruin with hybrid observation times.
problem Calculating Parisian ruin under a hybrid observation scheme.
method Hybrid observation scheme with Poisson arrivals and continuous monitoring below zero.
result Improved results and fluctuation identities in terms of second-generation scale functions.
Methodology for estimating marked Hawkes processes with neural networks.
problem Estimating conditional intensity of marked Hawkes processes.
method Proposes two models: Shallow Neural Hawkes with marks and Neural Network for Non-Linear Hawkes with Marks.
result Validation on synthetic datasets and real-world cryptocurrency order book data.
Anomaly detection aids in labeling fast-running processes for machine learning.
problem Manual labeling of fast-running processes for machine learning models.
method Anomaly detection to assist in labeling data, specific metrics for model validation.
result Possibility to manually classify data for training machine learning models.
We describe a simple and efficient procedure for approximating the Lévy measure of a Gamma(α,1) random variable. We use this approximation to derive a finite sum-representation that converges almost surely to Ferguson's representation of the Dirichlet process based on arrivals of a homogeneous Poisson process.…
Study optimal periodic dividend strategies for risky businesses with transaction costs.
problem Optimal periodic dividend strategies for spectrally positive Lévy risk processes with fixed transaction costs.
method Investigates periodic (bu,bl) strategies for a Poisson arrival process of decision times. result A periodic (bu,bl) strategy is optimal with lump sum dividends net of transaction costs. Proposes a neural network solution for queuing dynamics.
problem Modeling service system dynamics with limited parametric assumptions.
method Combines recurrent neural networks and generative adversarial networks.
result Evaluates solution on various datasets, demonstrating effectiveness.
The claim arrival process to an insurance company is modeled by a compound Poisson process whose intensity and/or jump size distribution changes at an unobservable time with a known distribution. It is in the insurance company's interest to detect the change time as soon as possible in order to re-evaluate a new fair v…
Unified framework for intermittent demand forecasting using renewal processes.
problem Intermittency in demand forecasting.
method Unified framework based on extensions of discrete-time renewal processes.
result Efficacy demonstrated in forecasting practice with favorable predictive accuracy.
New model optimizes assortment and pricing with dynamic customer arrivals.
problem Suboptimal decisions in classical models due to fixed arrival rates.
method Poisson-MNL model with UCB algorithm for dynamic decisions.
result Efficient algorithm achieves near optimal cumulative revenue.
NeuroMemFPP uses LSTM to estimate FPP parameters with high accuracy.
problem Estimating parameters of fractional Poisson process with memory and long-range dependence.
method Recurrent Neural Network (RNN), specifically Long Short-Term Memory (LSTM), for parameter estimation.
result The LSTM-based approach reduces MSE by about 55.3% compared to traditional MOM method.
The paper proposes a time-dependent Markov model for a limit order book.
problem Understanding the convergence of a limit order book to a more complex diffusion.
method A simple time-dependent Markov model is proposed, describing the arrival of different orders.
result Empirical studies verify the validity of the modeling assumptions for certain stocks.
The paper defines and analyzes Poissonian occupation times for negative Lévy processes.
problem Analyzing the time spent below zero for Lévy processes with interruptions.
method Introduces Poissonian occupation times for spectrally negative Lévy processes.
result Extends results on continuous observation to interrupted observation.
We propose a Conditional Density Filtering (C-DF) algorithm for efficient online Bayesian inference. C-DF adapts MCMC sampling to the online setting, sampling from approximations to conditional posterior distributions obtained by propagating surrogate conditional sufficient statistics (a function of data and parameter …
A scalable GP model for online uncertainty quantification over graphs.
problem Scalable uncertainty quantification over graphs with dynamic data.
method Graph-aware parametric Gaussian process model using random features and online conformal prediction.
result Improved coverage and efficient prediction sets over existing methods.
New approach to disentangle utility from impulse in recommendation systems.
problem Difficulty in inferring user utility from engagement signals.
method Generative model based on self-exciting Hawkes process to infer utility from return probability.
result It is possible to disentangle System-1 and System-2 decision processes to optimize content based on user utility.
Study optimal dividend and capital injection in insurance portfolios with self-exciting claim arrivals.
problem Optimal dividend and capital injection in insurance portfolios with Hawkes process claim arrivals.
method Analytical properties, explicit threshold, HJB variational inequality, finite-difference scheme, policy-gradient, actor-critic methods.
result Learned strategies closely match the PDE benchmark and remain stable across initial conditions.