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 effects on the default probability evaluation of choosing different non parametric equity volatility estimators, when market microstructure noise is taken into account. A general stochastic volatility framework with jumps for the underlying assets dynamics is defined by considering a Merton-like structural model with firm's asset evolution described by Bates [8] model. In order to estimate the volatility risk component of a firm, we follow the idea in [28] and use high-frequency equity prices, but differently from [28] we consider a more general framework where microstructure noise is introduced as direct effect of observing noisy high-frequency equity prices. A Monte Carlo simulation analysis is conducted to i) analyze 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.

Firm Volatility Risk and Default Probability Estimation under Market Microstructure Effects, Working Paper / Flavia Barsotti; Maria Elvira Mancino; Simona Sanfelici. - (2014), pp. 1-19.

Firm Volatility Risk and Default Probability Estimation under Market Microstructure Effects, Working Paper

SANFELICI, Simona
2014

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 effects on the default probability evaluation of choosing different non parametric equity volatility estimators, when market microstructure noise is taken into account. A general stochastic volatility framework with jumps for the underlying assets dynamics is defined by considering a Merton-like structural model with firm's asset evolution described by Bates [8] model. In order to estimate the volatility risk component of a firm, we follow the idea in [28] and use high-frequency equity prices, but differently from [28] we consider a more general framework where microstructure noise is introduced as direct effect of observing noisy high-frequency equity prices. A Monte Carlo simulation analysis is conducted to i) analyze 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2761130
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