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A novel forecast combination and weighted quantile based tail-risk forecasting framework is proposed, aiming to reduce the impact of modelling uncertainty in tail-risk forecasting. The proposed approach is based on a two-step estimation…

Risk Management · Quantitative Finance 2021-07-20 Giuseppe Storti , Chao Wang

The issue related to the quantification of the tail risk of cryptocurrencies is considered in this paper. The statistical methods used in the study are those concerning recent developments in Extreme Value Theory (EVT) for weakly dependent…

Risk Management · Quantitative Finance 2023-11-30 Andrea Teruzzi

Treatment effect estimation is a fundamental problem in causal inference. We focus on designing efficient randomized controlled trials, to accurately estimate the effect of some treatment on a population of $n$ individuals. In particular,…

Machine Learning · Computer Science 2022-10-14 Raghavendra Addanki , David Arbour , Tung Mai , Cameron Musco , Anup Rao

The Count-Min sketch is an important and well-studied data summarization method. It allows one to estimate the count of any item in a stream using a small, fixed size data sketch. However, the accuracy of the sketch depends on…

Data Structures and Algorithms · Computer Science 2018-11-13 Daniel Ting

Reliable forward uncertainty quantification in engineering requires methods that account for aleatory and epistemic uncertainties. In many applications, epistemic effects arising from uncertain parameters and model form dominate prediction…

Computational Engineering, Finance, and Science · Computer Science 2025-12-18 Akash Yadav , Ruda Zhang

Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information. This method is particularly useful in small area problems. The variability of an EBLUP is…

Statistics Theory · Mathematics 2008-12-18 Snigdhansu Chatterjee , Partha Lahiri , Huilin Li

The Coherence Length Diagram and the related maps have been shown to represent a useful tool for image analysis. Setting threshold parameters is one of the most important issues when dealing with such applications, as they affect both the…

Computer Vision and Pattern Recognition · Computer Science 2014-09-12 Roberto Marazzato , Amelia Carolina Sparavigna

Sample-based Bayesian inference provides a route to uncertainty quantification in the geosciences, and inverse problems in general, though is very computationally demanding in the naive form that requires simulating an accurate computer…

Computation · Statistics 2019-04-12 Tiangang Cui , Colin Fox , Michael J O'Sullivan

Stability and error analysis remain challenging for problems that lack regularity properties near solutions, are subject to large perturbations, and might be infinite dimensional. We consider nonconvex optimization and generalized equations…

Optimization and Control · Mathematics 2020-02-25 Johannes O. Royset

A great variety of complex systems ranging from user interactions in communication networks to transactions in financial markets can be modeled as temporal graphs, which consist of a set of vertices and a series of timestamped and directed…

Social and Information Networks · Computer Science 2020-07-29 Jingjing Wang , Yanhao Wang , Wenjun Jiang , Yuchen Li , Kian-Lee Tan

We revisit the classical problem of computing the \emph{contour tree} of a scalar field $f:\mathbb{M} \to \mathbb{R}$, where $\mathbb{M}$ is a triangulated simplicial mesh in $\mathbb{R}^d$. The contour tree is a fundamental topological…

Computational Geometry · Computer Science 2015-12-11 Benjamin Raichel , C. Seshadhri

We provide an elementary proof of the dual representation of Expected Shortfall on the space of integrable random variables over a general probability space. Unlike the results in the extant literature, our proof only exploits basic…

Risk Management · Quantitative Finance 2023-06-27 Martin Herdegen , Cosimo Munari

Bond rating Transition Probability Matrices (TPMs) are built over a one-year time-frame and for many practical purposes, like the assessment of risk in portfolios or the computation of banking Capital Requirements (e.g. the new IFRS 9…

Risk Management · Quantitative Finance 2017-10-17 Greig Smith , Goncalo dos Reis

Although quantile regression to calculate risk measures has been widely established in the financial literature, when considering data observed at mixed--frequency, an extension is needed. In this paper, a model is suggested built on a…

Statistical Finance · Quantitative Finance 2023-03-17 Vincenzo Candila , Giampiero M. Gallo , Lea Petrella

One problem of wide interest involves estimating expected crossing-times. Several tools have been developed to solve this problem beginning with the works of Wald and the theory of sequential analysis. An extension of his approach is…

Methodology · Statistics 2015-06-17 Mark Brown , Victor de la Pena , Tony Sit

Value-at-risk (VaR) and expected shortfall (ES) are two commonly utilized metrics for quantifying financial risk. In this study, we review the widely employed Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. These…

Computation · Statistics 2024-05-14 Kanon Kamronnaher , Andrew Bellucco , Whitney K. Huang , Colin M. Gallagher

A prominent feature of earthquakes is their empirical laws including memory (clustering) in time and space. Several earthquake forecasting models, like the EpidemicType Aftershock Sequence (ETAS) model, were developed based on earthquake…

It is shown that the axioms for coherent risk measures imply that whenever there is an asset in a portfolio that dominates the others in a given sample (which happens with finite probability even for large samples), then this portfolio…

Risk Management · Quantitative Finance 2009-09-29 Imre Kondor , Istvan Varga-Haszonits

Despite of the great efforts during the censuses, occurrence of some nonsampling errors such as coverage error is inevitable. Coverage error which can be classified into two types of under-count and overcount occurs when there is no unique…

Applications · Statistics 2019-10-15 Sepideh Mosaferi

Probabilistic Amplitude Shaping (PAS) is a coded-modulation scheme in which the encoder is a concatenation of a distribution matcher with a systematic Forward Error Correction (FEC) code. For reduced computational complexity the decoder can…

Information Theory · Computer Science 2018-06-05 Rana Ali Amjad