# Market Microstructure Effects on Firm Default Risk Evaluation

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Risk Methodologies, Group Financial Risks, Group Risk Management, UniCredit Spa, Piazza Gae Aulenti, Tower A, Floor 20, Milano 20154, Italy

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Department of Economics, University of Parma, Parma 43125, Italy

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Author to whom correspondence should be addressed.

Academic Editor: Nikolaus Hautsch

Received: 27 February 2016 / Revised: 1 June 2016 / Accepted: 22 June 2016 / Published: 8 July 2016

(This article belongs to the Special Issue Financial High-Frequency Data)

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.