Noise Fit, Estimation Error and a Sharpe Information Criterion
When the in-sample Sharpe ratio is obtained by optimizing over a k-dimensional parameter space, it is a biased estimator for what can be expected on unseen data (out-of-sample). We derive (1) an unbiased estimator adjusting for both sources of bias: noise fit and estimation error. We then show (2) how to use the adjusted Sharpe ratio as model selection criterion analogously to the Akaike Information Criterion (AIC). Selecting a model with the highest adjusted Sharpe ratio selects the model with the highest estimated out-of-sample Sharpe ratio in the same way as selection by AIC does for the log-likelihood as measure of fit.
Code (0)
등록된 구현이 없습니다.
Tasks
Model SelectionSimilar Papers 제목 키워드 기반
Diffusion Maximum Correntropy Criterion Algorithms for Robust Distributed Estimation
Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC), including adaptation to combination MCC and combination to adaptation MCC, are developed to deal with the distributed esti…
Channel Parameter Estimation in the Presence of Phase Noise Based on Maximum Correntropy Criterion
Oscillator output generally has phase noise causing the output power spectral density (PSD) to disperse around a Dirac delta function. In this paper, the AWGN channel is considered, where the sent signal accompanying wit…
parameter estimationConditional inference on the asset with maximum Sharpe ratio
We apply the procedure of Lee et al. to the problem of performing inference on the signal-noise ratio of the asset which displays maximum sample Sharpe ratio over a set of possibly correlated assets. We find a multivaria…
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
We consider outlier-robust and sparse estimation of linear regression coefficients, when the covariates and the noises are contaminated by adversarial outliers and noises are sampled from a heavy-tailed distribution. Our…
regressionA Unified Descriptive-Complexity Framework for Model Selection under Correlated Designs
Model selection becomes particularly challenging under strong predictor dependence and model-class uncertainty, especially when there are exponentially many models. We propose a Descriptive-Complexity Information Criteri…