Welcome to Quantitative Risk Management (QRM). In this lesson we introduce the axiomatic approach to risk measures. We give the definition of risk measure and we discuss what its uses for us are in terms of reserve capital quantification. We then define coherent and convex measures. The p
From playlist Quantitative Risk Management
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
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
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
Time Varying Volatility and GARCH in Risk Management
These classes are all based on the book Trading and Pricing Financial Derivatives, available on Amazon at this link. https://amzn.to/2WIoAL0 Check out our website http://www.onfinance.org/ Follow Patrick on twitter here: https://twitter.com/PatrickEBoyle In Todays video let's learn abo
From playlist Risk Management
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
FRM: Risk-adjusted performance ratios
RAPMs are variations of: return per unit of risk. Treynor and Sharpe are similar: both are excess return per unit of risk. Treynor defines risk as systematic risk (beta) and is therefore appropriate to well-diversified portfolios (i.e., into such portfolios idiosyncratic risk is eliminated
From playlist Performance measures
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
Risk-Aware Reinforcement Learning for Finance (SIAM FME)
SIAM Activity Group on FME Virtual Talk Series Join us for a series of online talks on topics related to mathematical finance and engineering and running every two weeks until further notice. The series is organized by the SIAM Activity Group on Financial Mathematics and Engineering. Spe
From playlist SIAM Activity Group on FME Virtual Talk Series
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
Stanford Seminar - Robots in Dynamic Tasks: Learning, Risk, and Safety
March 10, 2023 Joel Burdick of Caltech Autonomous robots are increasing applied to tasks that involve complex maneuvers and dynamic environments that are difficult to model a priori. Various types of learning methods have been proposed to fill this modeling gap. To motivate the need for l
From playlist Stanford AA289 - Robotics and Autonomous Systems Seminar
VARIABLES in Statistical Research (2-1)
A variable is any characteristic that can vary. An organized collection of numbers can be a variable. Qualitative variables indicate an attribute or belongingness to a category. Dichotomous variables are discrete variables that can have two and only two values. Quantitative variables indic
From playlist Forming Variables for Statistics & Statistical Software (WK 2 - QBA 237)
Risk Management of Option Books with Arbitrage-Free Neural-SDE Market Models (SIAM FME)
SIAM Activity Group on FME Virtual Talk Series Join us for a series of online talks on topics related to mathematical finance and engineering and running every two weeks until further notice. The series is organized by the SIAM Activity Group on Financial Mathematics and Engineering. Spe
From playlist SIAM Activity Group on FME Virtual Talk Series
Fifteenth SIAM Activity Group on FME Virtual Talk
Date: Thursday, December 10, 1PM-2PM Early Career Talks Speaker 1: Dena Firoozi, HEC Montréal - University of Montreal Title: Belief Estimation by Agents in Major-Minor LQG Mean Field Games Speaker 2: Sveinn Olafsson, Columbia University Title: Personalized Robo-Advising: Enhancing Inves
From playlist SIAM Activity Group on FME Virtual Talk Series
19. Black-Scholes Formula, Risk-neutral Valuation
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Vasily Strela This is a lecture on risk-neutral pricing, featuring the Black-Scholes formula and risk-neutral valuation. License: Creative Commons
From playlist MIT 18.S096 Topics in Mathematics w Applications in Finance
Tamer Başar: "A General Theory for Discrete-Time Mean-Field Games"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop III: Mean Field Games and Applications "A General Theory for Discrete-Time Mean-Field Games" Tamer Başar - University of Illinois at Urbana-Champaign Abstract: In this lecture, I will present a general theory for mean-field games formul
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Analyses of gradient methods for the optimization of wide two layer by Lenaic Chizat
DISCUSSION MEETING : STATISTICAL PHYSICS OF MACHINE LEARNING ORGANIZERS : Chandan Dasgupta, Abhishek Dhar and Satya Majumdar DATE : 06 January 2020 to 10 January 2020 VENUE : Madhava Lecture Hall, ICTS Bangalore Machine learning techniques, especially “deep learning” using multilayer n
From playlist Statistical Physics of Machine Learning 2020
Stanford Seminar - Safe and Robust Perception-Based Control
Sarah Dean UC Berkeley February 21, 2020 Machine learning provides a promising path to distill information from high dimensional sensors like cameras -- a fact that often serves as motivation for merging learning with control. This talk aims to provide rigorous guarantees for systems with
From playlist Stanford AA289 - Robotics and Autonomous Systems Seminar
What is Control Risk? | What is Monitor Risk?
This video talks about: Agenda→Purpose and objective of the monitor and control risk process, critical success factors for the monitor and control risk process tools and techniques for the monitor and control risk process Documenting the results of the monitor and control risk process Clic
From playlist PMI-RMP® Training Videos [2022 Updated]
Gilles Pagès: CVaR hedging using quantization based stochastic approximation algorithm
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Analysis and its Applications