tech.nmfin.algoquant.demo.model.portfoliooptimization

Class Lai2010NPEBModelDemo

java.lang.Object

tech.nmfin.algoquant.demo.model.portfoliooptimization.Lai2010NPEBModelDemo

@Demo
public class Lai2010NPEBModelDemo
extends Object

Using this example. Exercises Lai portfolio optimization with Yahoo estimation data and writes performance and P/L CSV outputs under log. Resampling and optimization settings affect both runtime and the solution; inspect the estimation and evaluation intervals before interpreting the comparison.

Model and publication. Lai, Xing and Chen incorporate uncertainty in estimated moments into predictive portfolio risk and develop a nonparametric empirical-Bayes resampling rule. “2010” in the API is historical naming; the cited journal article appeared in 2011.

Implementation in this tutorial. The active configuration uses Lai2010NPEBModel, a long-only MV optimizer, Ledoit–Wolf moments, an AR resampler, 100 bootstraps, lambda=1.4, a one-year calibration window and quarterly rebalancing. These choices adapt the framework; they do not reproduce all models or tables from the article. Follow the template’s data interval and rebalance strategy separately from the CSV exporters.

See Also:

T. L. Lai, H. Xing and Z. Chen (2011), Mean–variance portfolio optimization when means and covariances are unknown, Annals of Applied Statistics 5(2A), 798–823

Constructor Summary

Constructors

Constructor and Description
Lai2010NPEBModelDemo()

Method Summary

Modifier and Type	Method and Description
static void	main(String[] args)
void	run()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Lai2010NPEBModelDemo

public Lai2010NPEBModelDemo()

Method Detail

main

public static void main(String[] args)

run

public void run()