Introduction

We are excited to introduce our Stata code designed to calculate the risk of stock market crashes. This code is based on the methodologies described in the paper “Forecasting Crashes: Trading Volume, Past Returns, and Conditional Skewness in Stock Prices” by Joseph Chen, Harrison Hong, and Jeremy C. Stein. The authors developed a series of cross-sectional regression specifications to forecast skewness in the daily returns of individual stocks. They discovered that negative skewness is most pronounced in stocks that have experienced an increase in trading volume relative to trend over the prior six months, aligning with the model of Hong and Stein (1999), and positive returns over the prior 36 months, fitting with several theories, most notably Blanchard and Watson’s (1982) interpretation of stock-price bubbles. Similar results were also found when attempting to forecast the skewness of the aggregate stock market, although the statistical power in this case was limited. Our Stata code incorporates these findings, providing a robust tool for assessing crash risk in stock returns.

 

The NCSKEW measure

It is calculated by taking the negative of the third moment of daily returns, and dividing it by the standard deviation of daily returns raised to the third power. This measure is used to identify stocks that are more ‘crash prone’ – i.e., having a more left-skewed distribution.

 

DUVOL Methodology

In addition to NCSKEW, we also work with a second measure of return asymmetries that does not involve third moments, and hence is less likely to be overly influenced by a handful of extreme days. This alternative measure, DUVOL, is computed by separating all the days with returns below the period mean (‘down’ days) from those with returns above the period mean (‘up’ days), and computing the standard deviation for each of these subsamples separately. We then take the log of the ratio of the standard deviation on the down days to the standard deviation on the up days.

 

Our Stata Code Implementation

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

  •  NCSKEW: Negative Coefficient of Skewness
  •  DUVOL: Down-to-Up Volatility
  • Other parts of the paper code can be developed on demand.
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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.

 

benefit of the package content integration Pricing

We offer the code for NSKEW and DUVOL with distinct packages:

  • Single Measure: This package, priced at 99 GBP, includes the Stata code for either one of the two measures.
  • Both Measures: This package, priced at 169 GBP, includes the Stata code for both measures.

 

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

 

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          Why should you buy the code?


Dr. Attaullah Shah
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