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Forecasting Default Probability without Accounting Data: Evidence from Russia

STOCK MARKET VOLATILITY, Chapman & Hall/CRC Finance Series, pp. 535-556, 2009

Posted: 24 Mar 2009 Last revised: 23 Dec 2011

Dean Fantazzini

Moscow School of Economics, Moscow State University; National Research University Higher School of Economics

Date Written: March 24, 2009

Abstract

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

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

Dean Fantazzini (Contact Author)

Moscow School of Economics, Moscow State University ( email )

GSP-2, Leninskie Gory
Moscow, 119992
Russia
+7 495 5105256 (Phone)
+7 495 5105267 (Fax)

HOME PAGE: https://sites.google.com/site/deanfantazzini/

National Research University Higher School of Economics ( email )

Myasnitskaya street, 20
Moscow, Moscow 119017
Russia

HOME PAGE: http://www.hse.ru/org/persons/11532644

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