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on Risk Management |
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Issue of 2006‒07‒28
two papers chosen by |
| By: | John Y. Campbell; Jens Hilscher; Jan Szilagyi |
| Abstract: | This paper explores the determinants of corporate failure and the pricing of financially distressed stocks using US data over the period 1963 to 2003. Firms with higher leverage, lower profitability, lower market capitalization, lower past stock returns, more volatile past stock returns, lower cash holdings, higher market-book ratios, and lower prices per share are more likely to file for bankruptcy, be delisted, or receive a D rating. When predicting failure at longer horizons, the most persistent firm characteristics, market capitalization, the market-book ratio, and equity volatility become relatively more significant. Our model captures much of the time variation in the aggregate failure rate. Since 1981, financially distressed stocks have delivered anomalously low returns. They have lower returns but much higher standard deviations, market betas, and loadings on value and small-cap risk factors than stocks with a low risk of failure. These patterns hold in all size quintiles but are particularly strong in smaller stocks. They are inconsistent with the conjecture that the value and size effects are compensation for the risk of financial distress. |
| JEL: | G1 |
| Date: | 2006–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:12362&r=rmg |
| By: | Giuliano De Rossi; Andrew Harvey |
| Abstract: | A time-varying quantile can be fitted to a sequence of observations by formulating a time series model for the corresponding population quantile and iteratively applying a suitably modified state space signal extraction algorithm. Quantiles estimated in this way provide information on various aspects of a time series, including dispersion, asymmetry and, for financial applications, value at risk. Tests for the constancy of quantiles, and associated contrasts, are constructed using indicator variables; these tests have a similar form to stationarity tests and, under the null hypothesis, their asymptotic distributions belong to the Cramér von Mises family. Estimates of the quantiles at the end of the series provide the basis for forecasting. As such they offer an alternative to conditional quantile autoregressions and, at the same time, give some insight into their structure and potential drawbacks. |
| Keywords: | Dispersion; quantile regression; signal extraction; state space smoother; stationarity tests; value at risk. |
| JEL: | C14 C22 |
| Date: | 2006–07 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:0649&r=rmg |