Examines GARCH intensity model for risk-neutral option pricing.
problem Volatility clustering, leverage effect, and conditional asymmetry in financial returns.
method Risk-neutral option pricing method under GARCH intensity model.
result Flexibility in volatility changes according to probability measure.
Neural Diffusion Intensity Models simplify Cox processes inference.
problem Intractable nonparametric estimation and posterior inference of latent stochastic intensity in Cox processes.
method Variational framework using neural SDEs, with theoretical guarantee of ELBO maximization coinciding with maximum likelihood estimation.
result Accurate recovery of latent intensity dynamics and posterior paths with significant speedup.
Method uses deep learning to estimate traffic intensity.
problem Estimating stochastic intensity of traffic processes.
method Deep neural networks for nonlinear filtering.
result Deep learning method accurately estimates traffic intensity.
The model analyzes order flows in financial markets using Cox-type intensities.
problem Analyzing order dynamics in limit order books for market insights.
method Cox-type model for relative intensities, parameter estimation by quasi likelihood maximization, model selection with information criteria.
result The model provides excellent agreement with empirical data and identifies important factors in order book dynamics.
New models directly model inter-event times without intensity functions.
problem Learning temporal point processes with intensity-based approaches.
method Normalizing flows and mixture models for flexible and efficient modeling.
result Achieves state-of-the-art performance in prediction tasks.
Model predicts bid and ask price dynamics with spread-dependent intensities.
problem Predicting bid and ask price dynamics in high-frequency stock markets.
method Extended Hawkes process with zero intensities, spread-dependent intensities, and negative excitement.
result Spread-narrowing tendency, excitations caused by previous events, impact of flash crashes, and different market participant features.
Bayesian approach for inhomogeneous Poisson process intensity estimation.
problem Intractable integral in likelihood of Gaussian Cox process.
method Joint modeling of intensity and cumulative intensity as transformed Gaussian process; exact MCMC sampler.
result Exact posterior inference without approximations.
Study identifies two borrowing patterns in UK payday loan users.
problem Financial vulnerability of payday loan users.
method Two-state hidden Markov model (HMM) using Open Banking data.
result 36.4% of borrowers experience high-intensity exposure for 12 weeks or more.
Proposes a flexible neural network model for temporal point processes.
problem Limited expressiveness of RNN-based models for temporal point processes.
method Integrates intensity function using a feedforward neural network and calculates it as its derivative.
result Achieves competitive or superior performance compared to previous methods.
Proposes a model for simulating limit order books with state-dependent intensities.
problem Simulating the dynamics of limit order books with varying intensities of order submission.
method Developed a parametric model with state-dependent intensities for limit orders, market orders, and cancellations. Introduced new models for order placement and cancellation selection.
result The proposed model accurately simulates the dynamics of limit order books and outperforms standard Poisson models.
We provide analytical pricing formula of corporate defaultable bond with both expected and unexpected default in the case with stochastic default intensity. In the case with constant short rate and exogenous default recovery using PDE method, we gave some pricing formula of the defaultable bond under the conditions tha…
Developing a semi-analytical approximation for general default intensity models
problem Accurate and efficient pricing of default intensity models
method Path-integral formalism
result Accurate results for the Black-Karasinski model
Model credit risk with hidden states for default times.
problem Credit risk assessment with complex default intensity functions.
method Hidden Markov process for state extraction, filtering method.
result Closed-form formulas for default times distribution.
New method models intensity functions on spheres using normalizing flows.
problem Modeling non-homogeneous Poisson process intensity functions on the sphere.
method Flexible bijective map using normalizing flows to transform intensity functions.
result Normalizing flows provide a flexible way to model intensity functions on spheres.
We propose a novel method for automatic pain intensity estimation from facial images based on the framework of kernel Conditional Ordinal Random Fields (KCORF). We extend this framework to account for heteroscedasticity on the output labels(i.e., pain intensity scores) and introduce a novel dynamic features, dynamic ra…
Introduces ambiguity in credit risk markets using intensity-based models.
problem Uncertainty in default intensity in credit markets.
method Introduces a framework considering ambiguity in default intensity, constructs equivalent martingale measures using Girsanov theorem, and derives no-arbitrage price intervals.
result Derives the interval of no-arbitrage prices for bond prices under ambiguity in default intensity.
The present paper introduces a jump-diffusion extension of the classical diffusion default intensity model by means of subordination in the sense of Bochner. We start from the bi-variate process (X,D) of a diffusion state variable X driving default intensity and a default indicator process D and time change it wi…
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.
It is well-known from the work of Schönbucher (2005) that the marginal laws of a loss process can be matched by a unit increasing time inhomogeneous Markov process, whose deterministic jump intensity is called local intensity. The Stochastic Local Intensity (SLI) models such as the one proposed by Arnsdorf and Halperin…
This paper explores neural models to improve modeling of Hawkes process intensity functions.
problem Traditional Hawkes process intensity function's parametrized kernel function biases future event predictions.
method Uses neural models to model the kernel function of Hawkes process intensity function.
result Neural models can better capture future event characteristics using past events data.
Paper detects intensity bursts in financial data using Hawkes processes.
problem Detecting and analyzing intensity bursts in high-frequency financial data.
method Proposes a novel Hawkes process-based method for detecting intensity bursts in financial data.
result Demonstrates the effectiveness of the method in detecting intensity bursts in FX markets.
A new kernel method improves Poisson process intensity estimation.
problem Estimating intensity functions of inhomogeneous Poisson processes.
method Kernel method-based intensity estimator using least squares loss.
result K2IE achieves comparable predictive performance with improved efficiency. Generative model evaluates text emotion intensity, outperforming classification.
problem Limitations of discrete emotion classification in applied domains.
method Fine-tuning generative language models to output continuous emotion intensity scores.
result Generative model outperforms classification baselines and reveals generalization capabilities.
The paper extends intensity models for limit order books using marked point processes.
problem Modeling intensity ratios in limit order books with state dependency and clustering.
method Developed a new model combining three multiplicative components for marked point processes.
result The new model outperforms other intensity-based methods in predicting market order signs and aggressiveness.
Extends Hawkes process for flexible residual modeling in point processes.
problem Modeling high-frequency financial data with complex residual distributions.
method Introduces self and mutually exciting point process with discretely Markovian dynamics.
result Flexible residual distributions improve intensity modeling and high-frequency data estimation.
New method models MTPP without predefined intensity functions.
problem Parameterizing conditional joint PDF for MTPP.
method IFIB framework, modeling p∗(m,t) directly. result Superior experimental results on real and synthetic data.
In an asset return series there is a conditional asymmetric dependence between current return and past volatility depending on the current return's sign. To take into account the conditional asymmetry, we introduce new models for asset return dynamics in which frequencies of the up and down movements of asset price hav…
The paper models financial data with multivariate jump processes.
problem Capturing the dynamics of financial data with jumps.
method Defined multivariate point processes driven by stochastic jumps, providing stability conditions.
result Nonlinear models fit financial data best, showing jumps cluster during crises.
Neural networks learn distance-based representations, not just intensity.
problem Understanding how neural networks interpret and learn from internal activations.
method Manipulated ReLU and Absolute Value activations to observe sensitivity to distance and intensity perturbations.
result Neural networks are highly sensitive to small distance-based perturbations, challenging the intensity-based interpretation.
This paper uses Malliavin calculus to price and compute delta of financial derivatives in jump-diffusion models.
problem Pricing and delta computation of financial derivatives in jump-diffusion models with stochastic intensity.
method Utilizes Malliavin calculus to price and compute delta, applying the Euler scheme for convergence analysis.
result Established the convergence of approximated solution, financial derivative, and its delta Greeks.
We consider the intensity-based approach for the modeling of default times of one or more companies. In this approach the default times are defined as the jump times of a Cox process, which is a Poisson process conditional on the realization of its intensity. We assume that the intensity follows the Cox-Ingersoll-Ross …
The utility-based pricing of defaultable bonds in the case of stochastic intensity models of default risk is discussed. The Hamilton-Jacobi- Bellman (HJB) equations for the value functions is derived. A finite difference method is used to solve this problem. The yield-spreads for both buyer and seller are extracted. Th…
New method models Poisson intensity using RKHS for high-dimensional data.
problem Tractable nonparametric modeling of inhomogeneous Poisson intensity functions.
method Reproducing Kernel Hilbert Space (RKHS) formulation for intensity functions.
result Optimization of penalized likelihood can be cast as a tractable finite-dimensional problem.
The paper provides a formula for pricing volatility swaps with stochastic volatility, jumps, and stochastic intensity.
problem Valuation of volatility swaps in markets with stochastic volatility, jumps, and stochastic intensity.
method The paper uses the stochastic volatility model with jumps and stochastic intensity, and the Feynman-Kac theorem to derive a partial integral differential equation. Discrete and continuous sampled volatility swap pricing formulas are obtained using transform techniques.
result The paper delivers a pricing formula for volatility swaps under stochastic volatility with jumps and stochastic intensity.
A new model for predicting market order book dynamics using a buffer Hawkes process.
problem Predicting the evolution of limit order books in financial markets.
method Introducing a Markovian single point process with a buffer mechanism and self-exciting effect.
result The model accurately predicts market order book dynamics and converges to Brownian motion.
Paper proposes a new method for point process modeling without intensity function.
problem Limitations of current point process modeling methods, especially in multi-modal distributions.
method Intensity-free approach using Wasserstein distance for likelihood-free learning.
result Superior performance on various synthetic and real-world data compared to conventional methods.
Develops a method to model multivariate count processes with Cox processes and shot noise intensities.
problem Modeling and estimating dependent count processes using granular data.
method Multivariate Cox process with shot noise intensities, connected via Lévy copulas.
result Allows for over-dispersion, auto-correlation, and realistic features in count processes.
HYVINT generates hypergraphs with intensity-driven incidence formation and variational learning.
problem Challenges in generating hypergraphs with mechanistic interpretation and limited latent space.
method HYVINT uses intensity-driven incidence formation and a lower-bound variational estimator for latent representations.
result HYVINT achieves strong fidelity and novelty on synthetic and real-world hypergraphs.
Estimates point process intensity with many covariates for prediction.
problem Estimating intensity of point processes with many covariates.
method Additive model with linear combinations of unknown functions for covariates.
result Optimal rates of convergence for large number of active covariates.
This paper measures the intensity of implicit government guarantees using PMC index model.
problem Excessive local government debt due to implicit government guarantees.
method Text mining of policy documents related to municipal investment bonds, PMC index model.
result Recent policies have reduced the intensity of implicit government guarantees.
Introduces a new Hawkes model with CARMA(p,q) intensity to better model dependence structures.
problem Modeling dependence structures in time series data with realistic autocorrelation functions.
method Develops a Hawkes process with CARMA(p,q) intensity to capture more complex dependencies.
result The CARMA(p,q)-Hawkes model can reproduce more realistic dependence structures and is stationary and positive.
New model estimates higher-order interactions in stochastic processes using lower-dimensional projections.
problem Estimating higher-order interaction effects in stochastic processes with limited data.
method Additive Poisson Process (APP) combines information geometry and generalized additive models to model intensity functions in lower dimensions.
result The model can estimate higher-order intensity functions with sparse data.
New model predicts credit spreads using stochastic CIR++ intensities.
problem Lack of continuous stochastic credit spread models and limited term structure models.
method Stochastic CIR++ model for default intensities in risk-neutral space.
result Model produces realistic credit spread term structure curves and consistent diffusion over time.
Study adaptive sensing of Cox processes using posterior sampling and positive bases.
problem Adaptive sensing of Cox point processes with intensity function modeling.
method Model intensity function as truncated Gaussian process in positive basis, use Langevin dynamics and posterior sampling.
result Demonstrated improved sensing compared to classical Bayesian experimental design.
The classical literature on optimal liquidation, rooted in Almgren-Chriss models, tackles the optimal liquidation problem using a trade-off between market impact and price risk. Therefore, it only answers the general question of the optimal liquidation rhythm. The very question of the actual way to proceed with liquida…
A model predicts building damage locations in near real-time using intensity-based features.
problem Accurate and timely damage diagnosis of building structures after extreme events.
method Support vector machines and Bayesian optimization for probabilistic hazard intensity determination.
result The model achieves 83.1% accuracy in identifying damage locations in a reinforced concrete moment frame.
Bayesian nonparametric Hawkes process model with EM-variational inference.
problem Limited model flexibility in classical Hawkes processes.
method Gaussian process modulated Hawkes process with EM-variational inference.
result Recover underlying baseline intensity and triggering kernel without parametric restriction.
New method to predict adversarial perturbation intensity for logistic regression.
problem Adversarial attacks on machine learning models.
method Probabilistic definition of adversarial examples using logistic regression's asymptotic properties.
result Derive a closed-form expression for adversarial perturbation intensity.