Bootstrapping
Multi-factor default correlation model estimation: enhancement with bootstrapping
The authors propose using a three-factor Merton model to allow more accurate quantification when investigating the credit risk of portfolios.
The statistics of capture ratios
This paper investigates the statistical problem of estimating the capture ratio based on a finite number of observations of a fund’s returns.
Test for fractional degree stochastic dominance with applications to stock preferences for China and the United States
This paper develops the test statistics for fractional degree stochastic dominance and introduces a bootstrap method for determining the critical values of the tests.
Extreme value theory for operational risk in insurance: a case study
This study aims to test the sufficiency of the solvency capital requirement approach for calculating operational risk using the standard formula as defined in Solvency II.
Fake data can help backtesters, up to a point
Synthetic data made with machine learning will struggle to capture the caprice of financial markets
In fake data, quants see a fix for backtesting
Traditionally quants have learnt to pick data apart. Soon they might spend more time making it up
Extremal risk management: ES value verification
In this paper, we refer to the axiomatic theory of risk and investigate the problem of formal verification of the expected shortfall (ES) model based on a sample ES. Recognizing the infeasibility of parametric methods, they explore the bootstrap…
Dark materials: how one academic is delving into data
David Hand shines a light on dark data and the dangers of distortion by absence
A study on window-size selection for threshold and bootstrap value-at-risk models
This paper investigates the effects of window-size selection on various models for value-at-risk (VaR) forecasting using high-performance computing.
Resampling ‘slashes’ credit risk VAR underestimates
Academics claim Vasicek model’s underestimation tendency can be slashed to near-zero