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arXiv research

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.

168,657 papers · 148 categories

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109219328437 · Jun 202019922001200920172026
48 results for Markov-modulated Poisson Processes

Enhances count process modelling with Markov-modulated non-homogeneous Poisson process.

problem Count data modelling challenges, especially in complex scenarios.
method Introduces a flexible frequency perturbation measure into Markov-modulated Poisson process framework.
result Natural incorporation of observed event arrivals and latent factors.

New models optimize quotes for automated market makers considering various price dynamics and demand variability.

problem Optimizing quotes for automated market makers in volatile price environments.
method Advanced models incorporating stochastic volatility, jumps, Hawkes processes, and Markov-modulated Poisson processes.
result Optimal quotes can be computed using numerical methods tailored to each model.

We study optimal trade execution strategies in financial markets with discrete order flow. The agent has a finite liquidation horizon and must minimize price impact given a random number of incoming trade counterparties. Assuming that the order flow NN is given by a Poisson process, we give a full analysis of the prop…

2009-02-15abs ↗pdf ↗

Model detects market anomalies using a Hawkes process with hidden Markov chain.

problem Detecting high-frequency market manipulation in cryptocurrency trades.
method Developed a Markov-modulated Hawkes process with piecewise constant excitation kernels.
result Demonstrated the model's effectiveness in detecting suspicious trading activities.

In this paper we propose a semi-Markov modulated model of interest rates. We assume that the switching process is a semi-Markov process with finite state space E and the modulated process is a diffusive process. We derive recursive equations for the higher order moments of the discount factor and we describe a Monte Ca…

2012-10-11abs ↗pdf ↗

Using a Levy process we generalize formulas in Bo et al.(2010) for the Esscher transform parameters for the log-normal distribution which ensure the martingale condition holds for the discounted foreign exchange rate. Using these values of the parameters we find a risk-neural measure and provide new formulas for the di…

2014-02-09abs ↗pdf ↗

Study long-term behavior of semi-Markov modulated processes using integral functions.

problem Analyzing long-term behavior of semi-Markov modulated processes involving integral functions.
method Using ergodic semi-Markovian environment and affine stochastic recurrence equation.
result Mixture type laws emerge in long-term limit for processes.

The study revisits portfolio diversification by relaxing assumptions for skewed, multi-regime, and leptokurtic asset returns.

problem Underestimation of risk in portfolio diversification due to assumptions that are inconsistent with real-world asset returns.
method Calibrated a Markov-modulated Levy process model to equity market data to demonstrate the merits of the approach.
result The calibrated models effectively match empirical moments and show the importance of relaxing assumptions in portfolio diversification.

This chapter is an attempt to present a mathematical theory of compound fractional Poisson processes. The chapter begins with the characterization of a well-known Lévy process: The compound Poisson process. The semi-Markov extension of the compound Poisson process naturally leads to the compound fractional Poisson proc…

2011-03-03abs ↗pdf ↗

The paper proposes a class of financial market models which are based on inhomogeneous telegraph processes and jump diffusions with alternating volatilities. It is assumed that the jumps occur when the tendencies and volatilities are switching. We argue that such a model captures well the stock price dynamics under per…

2008-12-03abs ↗pdf ↗

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…

2011-04-21abs ↗pdf ↗

A deep Neyman-Scott process uses Poisson processes for efficient inference in complex point processes.

problem Efficient inference in complex hierarchical point processes.
method Developed an efficient posterior sampling via Markov chain Monte Carlo for likelihood-based inference.
result More hidden Poisson processes improve likelihood fitting and event prediction.

We present a machine learning model for the analysis of randomly generated discrete signals, modeled as the points of an inhomogeneous, compound Poisson point process. Like the wavelet scattering transform introduced by Mallat, our construction is naturally invariant to translations and reflections, but it decouples th…

2019-02-10abs ↗pdf ↗

A beta-negative binomial (BNB) process is proposed, leading to a beta-gamma-Poisson process, which may be viewed as a "multi-scoop" generalization of the beta-Bernoulli process. The BNB process is augmented into a beta-gamma-gamma-Poisson hierarchical structure, and applied as a nonparametric Bayesian prior for an infi…

2011-12-15abs ↗pdf ↗

Study shows convergence rates for BSDEs approximated by compound Poisson processes.

problem Analyzing convergence rates of BSDEs driven by Lévy processes.
method Approximating Lévy processes by compound Poisson processes and studying BSDEs.
result Optimal convergence rates derived for BSDEs in L2\mathbb L^2-norm and Wasserstein distance.

We consider a continuous-time model for inventory management with Markov modulated non-stationary demands. We introduce active learning by assuming that the state of the world is unobserved and must be inferred by the manager. We also assume that demands are observed only when they are completely met. We first derive t…

2012-06-27abs ↗pdf ↗

New methods improve estimation of nonhomogeneous Poisson processes from limited data.

problem Estimating nonhomogeneous Poisson processes from limited data.
method Formulated as a learning generalization problem, proposed adaptive and data-driven binning methods.
result Improved estimation of nonhomogeneous Poisson processes with limited data.

Study shows Merton model limits to Poisson process with log-normal intensity, improving default portfolio prediction.

problem Improving prediction of default portfolios using complex models.
method Applying Merton model with log-normal intensity function to Poisson process, discussing temporal correlation effects.
result Power decay model provides better generalization for long-term default portfolio data.

Proposes a model for predicting events from event streams.

problem Predicting events like part replacement and failure in manufacturing and teleservice systems.
method Non-parametric prognostic framework using MGCP modulated Poisson processes.
result MGCP prior facilitates sharing of information and analysis of flexible event patterns.

Two new models for volatility in Markov-switching environments capture financial time-series properties.

problem Modeling volatility in environments with regime switches and exogenous jumps.
method Generalizations of COGARCH and Barndorff-Nielsen-Shephard models using Markov-modulated generalized Ornstein-Uhlenbeck processes.
result Models inherit properties of original models and capture stylized facts of financial time-series.

We introduce an algorithm for the segmentation of a class of regime switching processes. The segmentation algorithm is a non parametric statistical method able to identify the regimes (patches) of the time series. The process is composed of consecutive patches of variable length, each patch being described by a station…

2010-01-14abs ↗pdf ↗

We present a probabilistic model of events in continuous time in which each event triggers a Poisson process of successor events. The ensemble of observed events is thereby modeled as a superposition of Poisson processes. Efficient inference is feasible under this model with an EM algorithm. Moreover, the EM algorithm …

2012-03-15abs ↗pdf ↗

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.

A new Bayesian optimization method using Poisson process for better noise robustness.

problem Estimating relative rankings of candidates in noisy environments.
method Poisson process-based ranking surrogate model and tailored acquisition functions.
result PoPBO framework shows lower computation costs and better robustness to noise compared to GP-BO.

Study of lengths of cycles in large genus random maps converging to Poisson process.

problem Understanding the distribution of cycle lengths in large genus random maps.
method Teichmüller theory approach for uniformly random metric maps (ribbon graphs).
result The length spectrum converges to a Poisson point process with an explicit intensity as genus tends to infinity.

Despite the fundamental nature of the inhomogeneous Poisson process in the theory and application of stochastic processes, and its attractive generalizations (e.g. Cox process), few tractable nonparametric modeling approaches of intensity functions exist, especially when observed points lie in a high-dimensional space.…

2016-10-27abs ↗pdf ↗

Optimal dividends strategy in a two-state regime-switching environment.

problem Maximizing profits from dividends until bankruptcy in a company with fluctuating cash surplus and regime changes in drift, volatility, and bankruptcy levels.
method Analyzes the optimal dividend payout strategy considering four factors: Brownian fluctuations in cash surplus, regime changes in drift, volatility, and bankruptcy levels.
result Rich structure of the optimal strategy, which can be either barrier-type or liquidation-barrier type, depending on model parameters.

We study the geometric quantization process for twisted Poisson manifolds. First, we introduce the notion of Lichnerowicz-twisted Poisson cohomology for twisted Poisson manifolds and we use it in order to characterize their prequantization bundles and to establish their prequantization condition. Next, we introduce a p…

2007-04-23abs ↗pdf ↗

The seemingly disjoint problems of count and mixture modeling are united under the negative binomial (NB) process. A gamma process is employed to model the rate measure of a Poisson process, whose normalization provides a random probability measure for mixture modeling and whose marginalization leads to an NB process f…

2012-09-15abs ↗pdf ↗

Study on critical faces convergence in a Poisson point process.

problem Convergence of point processes associated with critical faces in a Čech filtration.
method Established convergence in M0\mathcal M_0-topology for critical faces above vanishing threshold.
result Obtained limit theorems for positive and negative critical faces.