Introduction

We are pleased to announce that we have developed a Stata code based on the methodologies proposed by Barillas, Robotti, and Shanken (2020). This code implements several statistical tests for comparing asset pricing models. These tests include the basic alpha test for nested model comparison, the direct test for comparing squared Sharpe ratios of non-nested models, and the sequential test which combines the alpha test and the direct test. This tool provides a robust framework for assessing and comparing the performance of different asset pricing models.

The Basic Alpha Test

This test is used for nested model comparison. It checks whether the alphas of the factors excluded from a nested model are jointly zero when regressed on the nested model factors.

The Direct Test

his test is used for comparing squared Sharpe ratios of non-nested models. It checks whether the squared Sharpe ratios of two non-nested models are equal using the asymptotic distribution of the difference in squared Sharpe ratios.

The Sequential Test

This test is used for nested model comparison. It checks whether the alphas of the factors excluded from a nested model are jointly zero when regressed on the nested model factors.

 

Our Stata Code Implementation

We have developed a comprehensive Stata code that replicates the methodology used in Barillas, Robotti, and Shanken’s paper. Our code covers the following analyses:

  • The Basic Alpha Test
  • The Direct Test
  • The Sequential Test

Our Stata code is designed to be user-friendly and easy to understand, even for those who are new to Stata. We provide clear comments and explanations for each part of the code to ensure that you can follow along and understand exactly what is happening at each step.

 

Pricing

We offer the code three distinct packages:

  • Single Measure: This package, priced at 99 GBP, includes the Stata code for either one of the two measures.
  • Two Measures: This package, priced at 179 GBP, includes the Stata code for any two measures. This is equal to a discount of approximately 20%.
  • All Three Measures: This package, priced at 248 GBP, includes the Stata code for any two measures, a discount of approximately 30% compared to buying each measure separately.

Choose the package that best suits your needs and make the most of our offerings.

 

Buy with PayPal Buy Now

Choose Package

 

          Why should you buy the code?

Dr. Attaullah Shah

Created by an Expert

The code is written by Dr. Prof. Attaullah Shah, an experienced finance professional with over 20 years of experience in coding and quantitative modeling. He has published many Stata programs demonstrating his expertise.
Stata Code is cutomizable

Customizable and Extensible

While implementing the core model, the code is structured and commented in a way that makes it easy to customize and extend for your own purposes. Adapt it to your specific needs.
Save time with our Stata code

Save Time and Effort Developing Your Own Code

The code on the page implements the complex KMV Merton model for calculating distance to default and default probabilities. Developing this from scratch would take considerable time and effort.
Our Stata code is tested and validated

Accurate and Well-Tested Implementation

The code has been thoroughly tested and validated to ensure it accurately implements the published KMV Merton model. You can trust it to produce correct results.
Our Stata code is optimized for efficiency

Efficient and Optimized

The code has been optimized to run efficiently on large datasets. It leverages vectorization and other techniques to maximize performance.
We provide support for our Stata code

Expert Support

The code comes with expert support to help you implement and customize it for your needs. Get answers and guidance from the creator.
Our Stata code is affordable

Affordable Pricing

For a very reasonable price, you get reliable, optimized code plus support. Far more affordable than building it yourself.

References

 

Barillas, M., Robotti, C., & Shanken, J. (2020). Statistical tests for comparing asset pricing models. Journal of Financial Econometrics, 18(2), 397-437.

Kan, R., Robotti, C., & Shanken, J. (2013). Pricing model performance and the two‐pass cross‐sectional regression methodology. The Journal of Finance, 68(6), 2617-2649.

Rahman, S., & Schneider, M. J. (2019). Tests of alternative asset pricing models using individual security returns and a new multivariate F-test. Review of Pacific Basin Financial Markets and Policies, 22(01), 1950001.

 

 

See full list of our completed projects