Market Microstructure Effects on Firm Default Risk Evaluation

Abstract

Default probability is a fundamental variable determining the credit worthiness of a firm and equity volatility estimation plays a key role in its evaluation. Assuming a structural credit risk modeling approach, we study the impact of choosing different non parametric equity volatility estimators on default probability evaluation, when market microstructure noise is considered. A general stochastic volatility framework with jumps for the underlying asset dynamics is defined inside a Merton-like structural model. To estimate the volatility risk component of a firm we use high-frequency equity data: market microstructure noise is introduced as a direct effect of observing noisy high-frequency equity prices. A Monte Carlo simulation analysis is conducted to (i) test the performance of alternative non-parametric equity volatility estimators in their capability of filtering out the microstructure noise and backing out the true unobservable asset volatility; (ii) study the effects of different non-parametric estimation techniques on default probability evaluation. The impact of the non-parametric volatility estimators on risk evaluation is not negligible: a sensitivity analysis defined for alternative values of the leverage parameter and average jumps size reveals that the characteristics of the dataset are crucial to determine which is the proper estimator to consider from a credit risk perspective.

Analysis on the Problems and Countermeasures of Accounts Receivable Management in Shanghai Weiming Food Company

“…In the process of continuous development, Collateral as the fourth "C" and condition as the fifth "C" eventually formed the current 5C theory. Flavia Barsotti divided the causes of credit risk of accounts receivable into internal and external causes [6]. To reduce credit risk, companies should not only analyze the impact within the company, but also focus on external factors.…”