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.
Bayesian method selects interacting regions in Markov models.
Unified framework for efficient trans-dimensional Bayesian inference using VI and NFs.
Enhances RJMCMC efficiency with non-linear transport-based proposals.
Generative models using PDMPs with explicit jump rates and kernels.
Generative model handles varying data dimensions using jump diffusion processes.
A new model reconciles rough volatility and jumps.
This study bridges discrete and continuous state spaces using the Ehrenfest process and diffusion models.
SJDs unify masked, continuous, and hybrid diffusion models.
New MCMC method for complex models with large variables.
We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated annealing algorithm to optimize radial basis function (RBF) networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis functions. It performs a global search in the joint …
A method to identify new classes of price jumps in financial markets.
Most energy and commodity markets exhibit mean-reversion and occasional distinctive price spikes, which results in demand for derivative products which protect the holder against high prices. To this end, in this paper we present exact and fast methodologies for the simulation of the spot price dynamics modeled as the …
Affine jump-diffusions constitute a large class of continuous-time stochastic models that are particularly popular in finance and economics due to their analytical tractability. Methods for parameter estimation for such processes require ergodicity in order establish consistency and asymptotic normality of the associat…
BINDy uses Bayesian methods to identify nonlinear dynamics from data.
We consider a Markov process , which is the solution of a stochastic differential equation driven by a Lévy process and an independent Wiener process . Under some regularity conditions, including non-degeneracy of the diffusive and jump components of the process as well as smoothness of the Lévy density of $Z…
A new model for short rates using pure-jump processes.
Machine learning infers time-reversible dynamics from data.
New PDMP samplers tackle variable selection in models.
In this article we select the unknown dimension of the feature by re- versible jump MCMC inside a simulated annealing in bayesian set up of collaborative filter. We implement the same in MovieLens small dataset. We also tune the hyper parameter by using a modified empirical bayes. It can also be used to guess an initia…
Generates random persistence diagrams for data analysis.
In this paper we classify maps from a torus phase space to , the space of , non-singular hermitian operators up to equivariant homotopy. The equivariance is with respect to a time-reversal involution on and an involution on defining a certain symmetry class. Furthe…
Improves generative models by adding jump-diffusion noise.
Bayesian symbolic regression uncovers missing physics from data with uncertainty quantification.
We propose a new model for electricity pricing based on the price cap principle. The particularity of the model is that the asset price is an exponential functional of a jump Lévy process. This model can capture both mean reversion and jumps which are observed in electricity market. It is shown that the value of an Eur…
A new two-step MH method for Bayesian EL computation.
Efficient method for pricing European and American options using Markov switching stochastic volatility model.
This paper proposes learning to jump for generative modeling of sparse, skewed, heavy-tailed data.
Non-spanning identification of scheduled event risk in option pricing.
A discrete diffusion model learns denoising, scoring, and bridging in different coordinates.
Hybrid models forecast EPEC energy spot prices.
Develops a method to model multivariate count processes with Cox processes and shot noise intensities.
Breaks circular dependency in synthetic option pricing with a novel model.
We introduce a new model for describing the fluctuations of a tick-by-tick single asset price. Our model is based on Markov renewal processes. We consider a point process associated to the timestamps of the price jumps, and marks associated to price increments. By modeling the marks with a suitable Markov chain, we can…
In electricity markets, it is sensible to use a two-factor model with mean reversion for spot prices. One of the factors is an Ornstein-Uhlenbeck (OU) process driven by a Brownian motion and accounts for the small variations. The other factor is an OU process driven by a pure jump Lévy process and models the characteri…
Online distributional prediction with latent cluster geometry
In this paper we build on previous work which uses inferences techniques, in particular Markov Chain Monte Carlo (MCMC) methods, to solve parameterized control problems. We propose a number of modifications in order to make this approach more practical in general, higher-dimensional spaces. We first introduce a new tar…
We present a non-parametric Bayesian approach to structure learning with hidden causes. Previous Bayesian treatments of this problem define a prior over the number of hidden causes and use algorithms such as reversible jump Markov chain Monte Carlo to move between solutions. In contrast, we assume that the number of hi…
Develops a new trading strategy for renewable producers to manage price volatility.
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…
Bayesian method for knot inference in multivariate spline regression.
We introduce and establish the main properties of QHawkes ("Quadratic" Hawkes) models. QHawkes models generalize the Hawkes price models introduced in E. Bacry et al. (2014), by allowing all feedback effects in the jump intensity that are linear and quadratic in past returns. A non-parametric fit on NYSE stock data sho…
In this paper we modify the model of Itkin, Shcherbakov and Veygman, (2019) (ISV2019), proposed for pricing Quanto Credit Default Swaps (CDS) and risky bonds, in several ways. First, it is known since the Lehman Brothers bankruptcy that the recovery rate could significantly vary right before or at default, therefore, i…
New algorithm broadens BART models applicability.
News might trigger jump arrivals in financial time series. The "bad" and "good" news seems to have distinct impact. In the research, a double exponential jump distribution is applied to model downward and upward jumps. Bayesian double exponential jump-diffusion model is proposed. Theorems stated in the paper enable est…
We quantify how co-jumps impact correlations in currency markets. To disentangle the continuous part of quadratic covariation from co-jumps, and study the influence of co-jumps on correlations, we propose a new wavelet-based estimator. The proposed estimation framework is able to localize the co-jumps very precisely th…
Neural jump model improves option pricing accuracy.
We study the role of co-jumps in the interest rate futures markets. To disentangle continuous part of quadratic covariation from co-jumps, we localize the co-jumps precisely through wavelet coefficients and identify statistically significant ones. Using high frequency data about U.S. and European yield curves we quanti…
We investigate the extension of the multilevel Monte Carlo path simulation method to jump-diffusion SDEs. We consider models with finite rate activity, using a jump-adapted discretisation in which the jump times are computed and added to the standard uniform dis- cretisation times. The key component in multilevel analy…