A Calibration Method for Structural Models of Credit Risk with Reporting Bias

We propose a novel calibration methodology based on the maximum likelihood estimator to recover the parameters of a structural model of credit risk which accounts for potential reporting bias. Such bias is introduced by the managers and it is unobserved by outsider investors which can only estimate it. The calibration is performed using a combination of balance sheet, financial indicators and market prices of equities. We apply the calibration algorithm to Tyco, a real case of reporting bias in the United States history. We show that the calibrated model is able to predict the market stock price with a high degree of accuracy

Performance Characterization of Random Proximity Sensor Networks

In this paper, we characterize the localization performance and connectivity of sensors networks consisting of binary proximity sensors using a random sensor management strategy. The sensors are deployed uniformly at random over an area, and to limit the energy dissipation, each sensor node switches between an active and idle state according to random mechanisms regulated by a birth-and-death stochastic process. We first develop an upper bound for the minimum transmitting range which guarantees connectivity of the active nodes in the network with a desired probability. Then, we derive an analytical formula for predicting the mean-squared localization error of the active nodes when assuming a centroid localization scheme. Simulations are used to verify the theoretical claims for various localization schemes that operate only over connected active nodes