Entropic value at risk

FRM: Parametric value at risk (VaR): Pros & Cons

Here is a quick explanation of parametric value at risk (VaR) as a means to illustrating its strengths/weaknesses. Please note: The essence of parametric VaR is "no data:" while historical data is surely used to select a distribution and calibrate its parameters, a parametric VaR leans on

From playlist Value at Risk (VaR): Introduction

QRM L1-1: The Definition of Risk

Welcome to Quantitative Risk Management (QRM). In this first class, we define what risk if for us. We will discuss the basic characteristics of risk, underlining some important facts, like its subjectivity, and the impossibility of separating payoffs and probabilities. Understanding the d

From playlist Quantitative Risk Management

FRM: Three approaches to value at risk (VaR)

This is a brief introduction to the three basic approaches to value at risk (VaR): Historical simulation, Monte Carlo simulation, Parametric VaR (e.g., delta normal). For more financial risk videos, visit our website at http://www.bionicturtle.com!

From playlist Value at Risk (VaR): Introduction

What is Value at Risk? VaR and Risk Management

In todays video we learn about Value at Risk (VaR) and how is it calculated? Buy The Book Here: https://amzn.to/37HIdEB Follow Patrick on Twitter Here: https://twitter.com/PatrickEBoyle What Is Value at Risk (VaR)? Value at risk (VaR) is a calculation that aims to quantify the level of

From playlist Risk Management

Risk Management Lesson 5A: Value at Risk

In this first part of Lesson 5, we discuss Value-at-Risk (VaR). Topics: - Definition of VaR - Loss distribution and confidence level - The normal VaR

From playlist Risk Management

Marginal value at risk (marginal VaR)

This is a review which follows Jorion's (Chapter 7) calculation of marginal value at risk (marginal VaR). Marginal VaR requires that we calculate the beta of a position with respect to the portfolio. For more financial risk videos, visit our website! http://www.bionicturtle.com

From playlist Value at Risk (VaR): Introduction

Maxim Raginsky: "A mean-field theory of lazy training in two-layer neural nets"

High Dimensional Hamilton-Jacobi PDEs 2020 Workshop II: PDE and Inverse Problem Methods in Machine Learning "A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean-Vlasov dynamics" Maxim Raginsky - University of Illinois at Urbana-Cham

From playlist High Dimensional Hamilton-Jacobi PDEs 2020

Eighteenth SIAM Activity Group on FME Virtual Talk

Date: Thursday, March 4, 2021, 1PM-2PM Speaker: Marcel Nutz, Columbia University Title: Entropic Optimal Transport Abstract: Applied optimal transport is flourishing after computational advances have enabled its use in real-world problems with large data sets. Entropic regularization is

From playlist SIAM Activity Group on FME Virtual Talk Series

QRM L1-2: The dimensions of risk and friends

Welcome to Quantitative Risk Management (QRM). In this second video, we analyse the dimensions of risk. Risk is in fact an object that we need to consider from different points of view, and that sometimes we cannot even quantify. We will also discuss the importance of statistical thinking

From playlist Quantitative Risk Management

Statistical aspects of stochastic algorithms for entropic (...) - Bigot - Workshop 2 - CEB T1 2019

Jérémie Bigot (Univ. Bordeaux) / 12.03.2019 Statistical aspects of stochastic algorithms for entropic optimal transportation between probability measures. This talk is devoted to the stochastic approximation of entropically regularized Wasserstein distances between two probability measu

From playlist 2019 - T1 - The Mathematics of Imaging

Entropic Optimal Transport - Prof. Marcel Nutz

A workshop to commemorate the centenary of publication of Frank Knight’s "Risk, Uncertainty, and Profit" and John Maynard Keynes’ “A Treatise on Probability” This workshop is organised by the University of Oxford and supported by The Alan Turing Institute. For further details and regular

From playlist Uncertainty and Risk

Adrien Gaidon: "The 3 R's & P's of Autonomous Driving: Robustness, Randomness, & Risk in Percept..."

Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop I: Individual Vehicle Autonomy: Perception and Control "The 3 R's and P's of Autonomous Driving: Robustness, Randomness, and Risk in Perception, Prediction, and Planning" Adrien Gaidon - Toyota Research Instit

From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020

Risk Management Lesson 4A: Volatility

First part of Lesson 4. Topics: - Definitions of volatility - Basic assumptions (do they hold?) - Arch and G-arch models (brief overview)

From playlist Risk Management

Risk Management Lesson 4B: Volatility (second part) and Coherent Risk Measures

This is the second half of Lesson 4. Topics: - Exercise about volatility modeling with G-arch - Coherent risk measures - Are the variance and the standard deviation coherent? A useful document for you is available here: https://www.dropbox.com/s/6pdygf0bw6bcce1/coherence.pdf

From playlist Risk Management

Chiara Cammarota: "High-dimensional cost landscape and gradient descent in Tensor PCA and its ge..."

Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "High-dimensional cost landscape and gradient descent in Tensor PCA and its generalisations" Chiara Cammarota - King's College London Abstract: Tensor PCA is a prototy

From playlist Machine Learning for Physics and the Physics of Learning 2019

Nexus Trimester - František Matúš (Institute of Information Theory and Automation) 1/3

From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme

Polymer Crystallization: New Concepts and Implications by M Muthukumar

Conference and School on Nucleation Aggregation and Growth URL: https://www.icts.res.in/program/NAG2010 DATES: Monday 26 July, 2010 - Friday 06 Aug, 2010 VENUE : Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru DESCRIPTION: Venue: Jawaharlal Nehru Centre for Advance

From playlist Conference and School on Nucleation Aggregation and Growth

Nexus Trimester - František Matúš (Institute of Information Theory and Automation) 2/3

Entropy region and convolution František Matúš (Institute of Information Theory and Automation) February 19, 2016 Abstract: The entropy region is constructed from vectors of random variables by collecting Shannon entropies of all subvectors. We will review results on its shape using poly

From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme

Hans Föllmer: Entropy, energy, and optimal couplings on Wiener space

HYBRID EVENT Recorded during the meeting "Advances in Stochastic Control and Optimal Stopping with Applications in Economics and Finance" the September 12, 2022 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video a

From playlist Probability and Statistics

Risk Management 5B: Value at Risk (continued) and Expected Shortfall

This is the second part of Lesson 5. Topics: - The VaR for empirical distributions - The Expected Shortfall - Coherence of VaR and ES

From playlist Risk Management