smq/directoryAn evolving quant resource archive
Back to directoryResource
Paper / 2026SMQ / 62437303916302
The Innovation Tax: Generative AI Adoption, Productivity Paradox, and Systemic Risk in the U.S. Banking Sector
Tatsuru Kikuchi
banking / causal inferenceResource overview

The Innovation Tax: Generative AI Adoption, Productivity Paradox, and Systemic Risk in the U.S. Banking Sector

Paper2026

by Tatsuru Kikuchi

Tags
bankingcausal inferencecontagioneconometricssystemic risk

Access resource

Open original source Understand this paper

Continue on SelfMadeQuant

Understand this paper

Suggested foundations for risk measurement and stress and financial time series. Specialist prerequisites vary by paper.

Before you read

  1. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  2. Expected Shortfall and Other Tail Risk Measures

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

  3. Stationarity and Unit Roots

    Check whether a relationship can be estimated consistently over time.

  4. Linear Regression

    Read the fitted relationship and its residuals.

Go further

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

    Compare risk estimators and their assumptions.

  2. Backtesting VaR and ES: Coverage Tests and Model Validation

    Check forecasts against realised losses.

  3. Cointegration and Error Correction

    Study long-run relationships without confusing them with correlation.

Explore a related reading path

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

Explore Quantitative Risk Management Explore Econometrics and Financial Time Series

Keep exploring

Related resources

More ideas from similar topics.

6

From the same author

Tatsuru Kikuchi

4