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.
A new model forecasts Value-at-Risk using NIG distribution and dynamic scores.
Improved VB algorithm for NIG mixtures outperforms Gaussian mixtures for non-Gaussian data.
Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations. Univariate NIG mixtures and multivariate NIG mixtures are considered. The use of variational Bayes approximations here is a substantial departure fr…
In this paper, an application of three GARCH-type models (sGARCH, iGARCH, and tGARCH) with Student t-distribution, Generalized Error distribution (GED), and Normal Inverse Gaussian (NIG) distribution are examined. The new development allows for the modeling of volatility clustering effects, the leptokurtic and the skew…
The NIG model outperforms others in pricing S&P 500 index options.
The paper integrates behavioral distortions into portfolio optimization using implied probability weighting functions.
Proposes a simpler method for quantifying uncertainty in time-series with volatility clustering.
In this work, we study the value of an Asian option in the case of exponential Levy markets. More specifically, we are interested in the NIG (normal inverse Gaussian) the VG (variance gamma) models. The exponential Levy models produce incomplete markets. There are therefore an infinite number of equivalent martingale m…
Computed tomography (CT) equivalent information is needed for attenuation correction in PET imaging and for dose planning in radiotherapy. Prior work has shown that Gaussian mixture models can be used to generate a substitute CT (s-CT) image from a specific set of MRI modalities. This work introduces a more flexible cl…
In this paper, we obtain sharp asymptotic formulas with error estimates for the Mellin convolution of functions, and use these formulas to characterize the asymptotic behavior of marginal distribution densities of stock price processes in mixed stochastic models. Special examples of mixed models are jump-diffusion mode…
Closed-form formulas for path-independent options in a specific Lévy model.
In this paper we propose a transform method to compute the prices and greeks of barrier options driven by a class of Levy processes. We derive analytical expressions for the Laplace transforms in time of the prices and sensitivities of single barrier options in an exponential Levy model with hyper-exponential jumps. In…
We provide an integral representation for the (implied) copulas of dependent random variables in terms of their moment generating functions. The proof uses ideas from Fourier methods for option pricing. This representation can be used for a large class of models from mathematical finance, including Lévy and affine proc…
We introduce a wavelet-domain functional analysis of variance (fANOVA) method based on a Bayesian hierarchical model. The factor effects are modeled through a spike-and-slab mixture at each location-scale combination along with a normal-inverse-Gamma (NIG) conjugate setup for the coefficients and errors. A graphical mo…
In this paper we present an application of the use of autocopulas for modelling financial time series showing serial dependencies that are not necessarily linear. The approach presented here is semi-parametric in that it is characterized by a non-parametric autocopula and parametric marginals. One advantage of using au…
We apply multilevel Monte Carlo for option pricing problems using exponential Lévy models with a uniform timestep discretisation to monitor the running maximum required for lookback and barrier options. The numerical results demonstrate the computational efficiency of this approach. We derive estimates of the convergen…
Quantum computing speeds up option pricing for multiple assets.
One popular approach to option pricing in Lévy models is through solving the related partial integro differential equation (PIDE). For the numerical solution of such equations powerful Galerkin methods have been put forward e.g. by Hilber et al. (2013). As in practice large classes of models are maintained simultaneous…
We describe general multilevel Monte Carlo methods that estimate the price of an Asian option monitored at fixed dates. Our approach yields unbiased estimators with standard deviation in expected time for a variety of processes including the Black-Scholes model, Merton's jump-diffusion mod…
We introduce a simple model for equity index derivatives. The model generalizes well known Lèvy Normal Tempered Stable processes (e.g. NIG and VG) with time dependent parameters. It accurately fits Equity index implied volatility surfaces in the whole time range of quoted instruments, including small time horizon (few …
Extends option pricing framework without risk-free asset using Levy jumps.
This thesis develops a new framework for modelling price processes in finance, such as an equity price or foreign exchange rate. This can be related to the conventional Ito calculus-based framework through the time integral of a price's squared volatility, or `cumulative variance'. In the new framework, corresponding p…
The paper explores solutions to the distributional Bellman equation in reinforcement learning.
Proposes vMF distribution for skewed elliptical distributions.
Study calculates tail risk for various mixture distributions.
We realise the first and second Grushin distributions as symmetry reductions of the 3-dimensional Heisenberg distribution and 4-dimensional Engel distribution respectively. Similarly, we realise the Martinet distribution as an alternative symmetry reduction of the Engel distribution. These reductions allow us to derive…
Recent work has shown that deep generative models assign higher likelihood to out-of-distribution inputs than to training data. We show that a factor underlying this phenomenon is a mismatch between the nature of the prior distribution and that of the data distribution, a problem found in widely used deep generative mo…
Method uses optimal transport to complete distributional matrices.
Income and wealth distribution affect stability of a society to a large extent and high inequality affects it negatively. Moreover, in the case of developed countries, recently has been proven that inequality is closely related to all negative phenomena affecting society. So far, Econophysics papers tried to analyse in…
Study clusters distributions with known or unknown clusters using distribution testing.
Gradually Truncated Log-normal distribution - Size distribution of firms Abstract Many natural and economical phenomena are described through power law or log- normal distributions. In these cases, probability decreases very slowly with step size compared to normal distribution. Thus it is essential to cut-off these di…
A new distribution family extends the -stable distribution with a degree of freedom parameter.
Paper develops a new method to improve model calibration under distribution shifts.
New class of heavy-tailed distributions shows weighted averages dominate individual variables.
Researchers derived formulas for joint moments of elliptical distributions.
Paper proposes a new method for designing materials using deep learning.
One-shot algorithm for feature-distributed kernel PCA reduces communication costs.
Paper finds how many neurons are needed to approximate histogram distributions.
Paper analyzes origami slope gaps and their distribution, finding a unique pattern.
A new distributed clustering framework using distributional kernel.
Paper introduces a new distributional successor measure for reinforcement learning.
The paper extends distributions by singular curves, revealing structural equivalences.
Random matrix ensembles yield uniform distributions on manifolds.
New distributions allow greedy arm selection in sparse bandit problems.
Study error bounds in evaluating distributional computational graphs.
Study on continuous sequence classification with distribution uncertainty.
The paper shows strong correlation between in-distribution and out-of-distribution performance in various machine learning models.
Study explores geometric structure and prior for beta-logistic distribution.