Risk measure

In financial mathematics, a risk measure is used to determine the amount of an asset or set of assets (traditionally currency) to be kept in reserve. The purpose of this reserve is to make the risks taken by financial institutions, such as banks and insurance companies, acceptable to the regulator. In recent years attention has turned towards convex and coherent risk measurement. A risk measure is defined as a mapping from a set of random variables to the real numbers. This set of random variables represents portfolio returns. The common notation for a risk measure associated with a random variable is . A risk measure should have certain properties: Normalized Translative Monotone In a situation with -valued portfolios such that risk can be measured in of the assets, then a set of portfolios is the proper way to depict risk. Set-valued risk measures are useful for markets with transaction costs. A set-valued risk measure is a function , where is a -dimensional Lp space, , and where is a constant solvency cone and is the set of portfolios of the reference assets. must have the following properties: Normalized Translative in M Monotone Value at risk Expected shortfall Superposed risk measures Entropic value at risk Drawdown Tail conditional expectation Entropic risk measure Superhedging price Expectile Variance (or standard deviation) is not a risk measure in the above sense. This can be seen since it has neither the translation property nor monotonicity. That is, for all , and a simple counterexample for monotonicity can be found. The standard deviation is a deviation risk measure. To avoid any confusion, note that deviation risk measures, such as variance and standard deviation are sometimes called risk measures in different fields. There is a one-to-one correspondence between an acceptance set and a corresponding risk measure. As defined below it can be shown that and . If is a (scalar) risk measure then is an acceptance set. If is a set-valued risk measure then is an acceptance set.

Uncertainty-aware Flexibility Envelope Prediction in Buildings with Controller-agnostic Battery Models

Colin Neil Jones, Paul Scharnhorst, Rafael Eduardo Carrillo Rangel, Pierre-Jean Alet, Baptiste Schubnel

Buildings are a promising source of flexibility for the application of demand response. In this work, we introduce a novel battery model formulation to capture the state evolution of a single building. Being fully data-driven, the battery model identificat ...

2022

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Uncertainty-aware Flexibility Envelope Prediction in Buildings with Controller-agnostic Battery Models

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Uncertainty-aware Flexibility Envelope Prediction in Buildings with Controller-agnostic Battery Models