3 Presentations

• 
  04:00 PM - 04:30 PM

  Using a Bayesian Model for Bankruptcy Prediction: A Comparative Approach

  • Samir Trabelsi, presenter, Brock University
  • Larence He, Brock University
  • Ron He, Brock University

  The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. When tests were conducted with randomly selected samples and all specifications of Type-I costs over Type-II costs are taken into account, the results show that the Mixed Logit model performs slightly better than the Bayesian model and much better than the Hazard model. However, when tests were conducted with business-cycle samples, the Bayesian model has the best performance and much better predictive power in recent business cycles. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors’ concerns with respect to assessing failure risk.

• 
  04:30 PM - 05:00 PM

  Investigating the Market Price of Volatility Risk for Options in a Regime-Switching Market

  • Melissa Mielkie, presenter, Department of Applied Mathematics, The University of Western Ontario
  • Matt Davison, Departments of Applied Mathematics and Statistical & Actuarial Sciences, Richard Ivey School of Business, The University of Western Ontario

  The existence of a risk premium has been proven in previous financial literature through an exploration of market data, quantifying the relationship between implied and realized volatility. To bridge the gap between theoretical option pricing models and observed option prices on exchanges, it is necessary to price the volatility risk inherent in financial markets. Building upon previous work by Mielkie & Davison (2012) where an approximate solution was derived for options written on underlying assets with regime-switching volatility, we analyze the impact of the market price of volatility risk on theoretical option prices. Using financially intuitive constraints, we prove mathematically the necessity of placing restrictions on the market prices of volatility risk in order to get reasonable option prices. In particular, we show that negative state-dependent market prices of volatility risk are necessary in order for the option prices and corresponding hedge ratios to be financially rational. An exploration of the regime-switching option prices and their implied volatilities is given, as well as numerical results and intuition supporting our theoretical proofs.

• 
  05:00 PM - 05:30 PM

  A Dynamic Program for Valuing Corporate Securities

  • Hatem Ben Ameur, presenter, GERAD, HEC Montréal
  • Mohamed Ayadi, Brock University
  • Tarek Fakhfakh, Université de Sfax

  We design and implement a dynamic program for valuing corporate debt portfolios, seen as derivatives on a firm's assets, and computing its term structure of yield spreads and of default probabilities. Our setting accommodates arbitrary corporate debts, multiple seniority classes, sinking funds, American-style embedded options, dividends, tax benefits, bankruptcy costs, alternative reorganization processes, and various Markov dynamics for the state process. This flexibility comes at the expense of a minor loss of efficiency; the analytical approach proposed in the literature is exchanged here for a numerical approach based on dynamic programming coupled with finite elements. We provide several theoretical properties of the debt- and equity-value functions. Finally, to assess our construction, we carry out a numerical investigation along with a sensitivity analysis.