STOCK MARKET VOLATILITY, Chapman & Hall/CRC Finance Series, pp. 535-556, 2009
Posted: 24 Mar 2009 Last revised: 23 Dec 2011
Date Written: March 24, 2009
The recent default of the multinational giants Enron, Parmalat and Worldcom clearly showed how accounting data can be misleading and far away from the true financial situation of a company. When financial fraud takes place, the models that use accounting data to predict default probabilities cannot be used since their forecasts are completely unreliable. To avoid such problems, we propose a novel approach that uses stock prices only, and allows to model departures from normality in stock returns dynamics, too. The parametric bootstrap, based on a conditional marginal model, is used to estimate the distribution of these estimated probabilities and to construct confidence bands. We show an empirical example with quoted Russian stocks as well as with American, Italian and Russian defaulted stocks, whose financial statements were found to be irregular.
Keywords: Default Probability, Financial Fraud, Zero Price Probability, ZPP, Merton style models, Parametric Bootstrap,
JEL Classification: G12, G30, G32
Suggested Citation: Suggested Citation
Fantazzini, Dean, Forecasting Default Probability without Accounting Data: Evidence from Russia (March 24, 2009). STOCK MARKET VOLATILITY, Chapman & Hall/CRC Finance Series, pp. 535-556, 2009. Available at SSRN: https://ssrn.com/abstract=1367512