arXiv research

Study on ruin probabilities for Lévy processes with light-tailed jumps.

This paper presents generalized momentum mappings for covariant Hamiltonian field theories. The new momentum mappings arise from a generalization of symplectic geometry to $L_VY$, the bundle of vertically adapted linear frames over the bundle of field configurations $Y$. Specifically, the generalized field momentum obs…

The paper provides a representation for dynamic risk measures and capital allocations.

The paper optimizes utility for switching models using Lévy processes.

Study on ruin probability with investment in risky assets modeled as semimartingales.

Modeling financial markets with a novel order flow model.

Stability of PDEs linked to vector fields on manifolds.

The Wiener-Hopf factorization is obtained in closed form for a phase type approximation to the CGMY Lévy process. This allows, for the approximation, exact computation of first passage times to barrier levels via Laplace transform inversion. Calibration of the CGMY model to market option prices defines the risk neutral…

New model explains low interest rates and large bond market jumps.

Constructs supermartingale couplings with full marginals constraints.

This paper sets baselines for reading comprehension benchmarks, finding simple models often perform well.

The distribution of trade sizes and trading volumes are investigated based on the limit order book data of 22 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. We observe that the size distribution of trades for individual stocks exhibits jumps, which is caused by the number preference…

The study establishes conditions for stratified spaces to satisfy RCD(K, N) curvature-dimension condition.

We provide an empirical investigation aimed at uncovering the statistical properties of intricate stock trading networks based on the order flow data of a highly liquid stock (Shenzhen Development Bank) listed on Shenzhen Stock Exchange during the whole year of 2003. By reconstructing the limit order book, we can extra…

Incorporates matrix exponential into generative flows for improved performance.

New linear flows using exponential of linear transformations improve generative models.

Paper compares two hedging strategies for Lévy models.

Develops SQR models for multivariate exponential families allowing positive dependencies.

New model captures time-varying volatility with stochastic exponential tails.

We provide a classification of graphical models according to their representation as subfamilies of exponential families. Undirected graphical models with no hidden variables are linear exponential families (LEFs), directed acyclic graphical models and chain graphs with no hidden variables, including Bayesian networks …

In a Markovian stochastic volatility model, we consider financial agents whose investment criteria are modelled by forward exponential performance processes. The problem of contingent claim indifference valuation is first addressed and a number of properties are proved and discussed. Special attention is given to the c…

ELNN uses neural networks for improved option pricing.

The paper studies variable annuity benefits using exponential functionals of Levy processes.

Quantum computing speeds up asset pricing models exponentially.

We propose a novel approach for density estimation with exponential families for the case when the true density may not fall within the chosen family. Our approach augments the sufficient statistics with features designed to accumulate probability mass in the neighborhood of the observed points, resulting in a non-para…

A two-network architecture learns intractable exponential family models.

Motivated by the pricing of lookback options in exponential Lévy models, we study the difference between the continuous and discrete supremum of Lévy processes. In particular, we extend the results of Broadie et al. (1999) to jump-diffusion models. We also derive bounds for general exponential Lévy models.

Quantum algorithm improves generative models exponentially.

Exponentially smoothed RNNs improve industrial forecasting.

There has been an explosion of interest in statistical models for analyzing network data, and considerable interest in the class of exponential random graph (ERG) models, especially in connection with difficulties in computing maximum likelihood estimates. The issues associated with these difficulties relate to the bro…

Efficient method for learning continuous exponential families beyond Gaussian.

Develops a semi-supervised learning method using exponential tilt mixture models.

Exponential distribution is ubiquitous in the framework of multi-agent systems. An alternative approach with an economic motivation to derive the exponential distribution in the framework of iterations in the space of distributions is disclosed.

We develop a new Monte Carlo variance reduction method to estimate the expectation of two commonly encountered path-dependent functionals: first-passage times and occupation times of sets. The method is based on a recursive approximation of the first-passage time probability and expected occupation time of sets of a Le…

The paper calculates prices for special options using mixed-exponential jumps.

Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly…

Proposes a new model using exponential smoothing cells for robust time series analysis.

The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions, such as when there is some sparsity pattern of the optimal parameter. This work characterizes a certain strong conve…

The paper introduces a new method for graph embedding using exponential family distributions.

GRM models k-way dependencies in univariate exponential families.

Auto-regressive models improve smoothing efficiency with exponentially tapered windows.

EFDA extends LDA to non-Gaussian models using exponential families.

SMRL uses score matching for efficient RL with exponential family models.

A new method for exponentially weighted moving models using approximations.

In the setting of exponential investors and uncertainty governed by Brownian motions we first prove the existence of an incomplete equilibrium for a general class of models. We then introduce a tractable class of exponential-quadratic models and prove that the corresponding incomplete equilibrium is characterized by a …

Improved Gibbs sampler speeds up Bayesian exponential smoothing model.

CDEFs reduce model complexity and uncover time correlations.

Improves variational inference for sparse models using mixtures of exponential families.