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 meta-analysis model detects and accommodates outliers.
The paper derives formulas for moments of a Student t distribution and applies them to quantify -quantiles.
European options can be priced when returns follow a Student's t-distribution, provided that the asset is capped in value or the distribution is truncated. We call pricing of options using a log Student's t-distribution a Gosset approach, in honour of W.S. Gosset. In this paper, we compare the greeks for Gosset and Bla…
Cluster GARCH model improves multivariate GARCH for high-dimensional asset returns.
TDistNNs improve prediction intervals for neural networks by using t-distributions.
This paper improves PPCA robustness using -distributions.
Accumulated stock returns exhibit tempered skew t-distribution.
Modified Jones-Faddy skew t-distribution captures asymmetry in stock returns.
Graphical Gaussian models have proven to be useful tools for exploring network structures based on multivariate data. Applications to studies of gene expression have generated substantial interest in these models, and resulting recent progress includes the development of fitting methodology involving penalization of th…
The distribution of the returns for a stock are not well described by a normal probability density function (pdf). Student's t-distributions, which have fat tails, are known to fit the distributions of the returns. We present pricing of European call or put options using a log Student's t-distribution, which we call a …
Generative Adversarial Networks (GANs) have a great performance in image generation, but they need a large scale of data to train the entire framework, and often result in nonsensical results. We propose a new method referring to conditional GAN, which equipments the latent noise with mixture of Student's t-distributio…
Analyzes multi-day stock returns, showing linear volatility and mean dependence.
RFPCA improves robustness of FPCA for matrix data.
Optimal option portfolios under Sharpe Ratio maximization with skew-elliptical t-distributed returns
Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal or repeated measures. This paper develops an Expectation-Maximization algorithm for discriminant analysis and classification with matrix-var…
A new operator based on t-distributions improves NN classifiers' robustness to out-of-distribution samples.
Adaptive t-distribution estimates nonstationary time series using moving moments.
A new filter adapts to heavy-tailed data without tuning, improving performance in challenging conditions.
Graphical Gaussian models have proven to be useful tools for exploring network structures based on multivariate data. Applications to studies of gene expression have generated substantial interest in these models, and resulting recent progress includes the development of fitting methodology involving penalization of th…
Improved image reconstruction using VAEs with Student's t-prior.
The probability distribution of log-returns of financial time series, sampled at high frequency, is the basis for any further developments in quantitative finance. In this letter, we present experimental results based on a large set of time series on futures. Then, we show that the t-distribution with gives…
Proposes a robust factor analysis for matrix data.
Paper studies t-SNE convergence with generalized kernels.
Study connects covariance cleaning theory to information theory for heavy-tailed distributions.
Improved VAE for heavy-tailed data using Student's t-distributions.
A parsimonious model reduces over-parameterization in skewed matrix variate mixtures.
We propose a robust method to estimate heteroscedastic noise models using Student's t-distribution.
New diffusion models capture heavy-tailed distributions better.
We show how to reduce the problem of computing VaR and CVaR with Student T return distributions to evaluation of analytical functions of the moments. This allows an analysis of the risk properties of systems to be carefully attributed between choices of risk function (e.g. VaR vs CVaR); choice of return distribution (p…
Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could affect the volatility of another asset tomorrow. These lagged effects among volatilities - which we call volatility spillovers - are studied using the Vector AutoRegressive (VAR) model. We account for the p…
A new update rule for deep reinforcement learning reduces learning variance and variance in reference signals.
Econometric framework integrates heavy-tailed distributions with behavioral probability weighting for better asset pricing.
Explains SNE, t-SNE, and their variants for manifold learning.
We present a Kalman smoothing framework based on modeling errors using the heavy tailed Student's t distribution, along with algorithms, convergence theory, open-source general implementation, and several important applications. The computational effort per iteration grows linearly with the length of the time series, a…
The study compares VaR and ES models for tail risk of electricity futures, finding AR(1)-GARCH(1,1) with Student-t distribution best.
Flow cytometry is a high-throughput technology used to quantify multiple surface and intracellular markers at the level of a single cell. This enables to identify cell sub-types, and to determine their relative proportions. Improvements of this technology allow to describe millions of individual cells from a blood samp…
The study tackles modeling high-frequency financial data using continuous distributions, finding them inadequate.
Optimizes option portfolios for skewed-t returns using VaR and variance measures.
A homogeneously saturated equation for the time development of the price of a financial asset is presented and investigated for the pricing of European call options using noise that is distributed as a Student's t-distribution. In the limit that the saturation parameter of the equation equals zero, the standard model o…
New method infers co-expression networks robustly from multiple studies.
Visualizing high-dimensional data is an essential task in Data Science and Machine Learning. The Centroid-Encoder (CE) method is similar to the autoencoder but incorporates label information to keep objects of a class close together in the reduced visualization space. CE exploits nonlinearity and labels to encode high …
A new method uses a product of experts with Dirichlet variables to approximate complex distributions.
The time development of the price of a financial asset is considered by constructing and solving Langevin equations for a homogeneously saturated model, and for comparison, for a standard model and for a logistic model. The homogeneously saturated model uses coupled rate equations for the money supply and for the price…
In this paper, we generalize the parametric delta-VaR method from portfolios with normally distributed risk factors to portfolios with elliptically distributed ones. We treat both the expected shortfall and the Value-at-Risk of such portfolios. Special attention is given to the particular case of a multivariate t-distr…
I explicitly work out closed form solutions for the optimal hedging strategies (in the sense of Bouchaud and Sornette) in the case of European call options, where the underlying is modeled by (unbiased) iid additive returns with Student-t distributions. The results may serve as illustrative examples for option pricing …
Improved normalising flows using Student's t-distribution for robust training.
T-distributed stochastic neighbour embedding (t-SNE) is a widely used data visualisation technique. It differs from its predecessor SNE by the low-dimensional similarity kernel: the Gaussian kernel was replaced by the heavy-tailed Cauchy kernel, solving the "crowding problem" of SNE. Here, we develop an efficient imple…
In this paper, we generalize the parametric Delta-VaR methods from portfolios with elliptic distributed risk factors to portfolios with mixture of elliptically distributed ones. We treat both the Expected Shortfall and the Value-at-Risk of such portfolios. Special attention is given to the particular case of the mixtur…