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Paper / 2020SMQ / 267370786367158
Banking supervision, monetary policy and risk-taking: Big data evidence from 15 credit registers
Carlos Altavilla / Frank Smets / José-Luis Peydró / Miguel Boucinha
banking / causal identificationResource overview

Banking supervision, monetary policy and risk-taking: Big data evidence from 15 credit registers

Paper2020

by Carlos Altavilla, Frank Smets, José-Luis Peydró, Miguel Boucinha

Tags
bankingcausal identification

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Understand this paper

Suggested foundations for credit and counterparty risk and risk measurement and stress. Specialist prerequisites vary by paper.

Before you read

  1. Credit Instruments, PD/LGD/EAD, and Rating Migration

    Separate default probability, loss severity and exposure.

  2. Conditional Expectation

    Read conditional default and exposure quantities.

  3. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  4. Expected Shortfall and Other Tail Risk Measures

    Distinguish a loss quantile from the average loss in the tail.

Go further

  1. Threshold Models and the Gaussian Copula for Portfolio Default

    Study dependence between defaults.

  2. CVA and DVA

    Connect exposure and counterparty losses to valuation adjustments.

  3. VaR Estimation in Practice: Variance-Covariance, Historical Simulation, Monte Carlo

    Compare risk estimators and their assumptions.

Explore a related reading path

Lesson links open public previews. Access to full lessons depends on the topic and your membership.

Explore Counterparty Credit Risk and CVA Explore Quantitative Risk Management

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