A Quantile Analysis of Default Risk for Speculative and Emerging Companies

Abstract

Using quantile regression, this article examines default risk of emerging and speculative companies in Australia and the United States as compared to established and investment entities. We use two datasets for each of the two countries, one speculative and one established. In the US we compare companies from the S&P 500 to those on the Speculative Grade Liquidity Ratings list (Moody's Investor Services, 2010). For Australia, we compare entities from the S&P/ASX 200 to those on the S&P/ASX Emerging Companies Index (EMCOX). We also divide the datasets into GFC and Pre-GFC periods to examine default risk over different economic circumstances. Quantile Regression splits the data into parts or quantiles, thus allowing default risk to be examined at different risk levels. This is especially useful in measuring extreme risk quantiles, when corporate failures are most likely. We apply Monte Carlo simulation to asset returns to calculate Distance to Default using a Merton structural credit model approach. In both countries, the analysis finds substantially higher default risk for speculative as compared to established companies. The spread between speculative company and established company default risk is found to remain constant in Australia through different economic circumstances, but to increase in the US during the GFC as compared to pre-GFC. These findings are important to lenders in understanding, and providing for, default risk for companies of different grades through varying economic cycles.