This article offers a new paradigm for analyzing the behavior of uncertain multivariable systems using a set of quantities we call inferential moments. Marginalization is an uncertainty quantification process that averages conditional probabilities to quantify the expected value of a probability...

Stress testing of financial systems has been increasingly important after several financial crises in recent years, thereby drawing keen attention to the choice of appropriate stress scenarios to effectively test the robustness of the systems. To this end, we consider the problem of identifying...

In this paper, we propose an outlier detection algorithm for multivariate data based on their projections on the directions that maximize the Cumulant Generating Function (CGF). We prove that CGF is a convex function, and we characterize the CGF maximization problem on the unit n-circle as a...

We provide a procedure to identify the number of latent factors of stochastic volatility models. The methodology relies on the non-parametric Fourier estimation method introduced by [Malliavin and Mancino, 2002] and applies to high-frequency data. Based on the Fourier analysis, we first estimate...

Gold and Crude oil are very vital global Variables and they are ruling the market. An increase in the worldwide oil prices and gold prices hurts the Indian economy. To know whether there is any dependency of gold and crude oil price fluctuations with respect to Indian market. The interdependence...

The Dynamic Conditional Correlation GARCH (DCC-GARCH) mutation model is considered using a Monte Carlo approach via Markov chains in the estimation of parameters, time-dependence variation is visually demonstrated. Fifteen indices were analyzed from the main financial markets of developed and...

In this paper, we utilize ridge regression and develop a novel set of characteristics-based factors, referred to as "ridge factors". We demonstrate that use of the ridge factors enables the creation of a Bayesian average SDF, which can shrink the relative contribution of low-variance principle...

We implement multivariate, self-exciting Peaks-over-Threshold (POT) methods to measure extremal losses in high-frequency return series of cryptocurrencies. For that purpose, we implement trivariate Hawkes-POT and the autoregressive conditional intensity ACI-POT models for Bitcoin, Ethereum, and...

We introduce a new dynamic clustering method for multivariate panel data char-acterized by time-variation in cluster locations and shapes, cluster compositions, and, possibly, the number of clusters. To avoid overly frequent cluster switching (flickering), we extend standard cross-sectional...