Market Microstructure Effects on Firm Default Risk Evaluation

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...

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Main Authors: Flavia Barsotti, Simona Sanfelici
Format: Article
Language:English
Published: MDPI AG 2016-07-01
Series:Econometrics
Subjects:
Online Access:http://www.mdpi.com/2225-1146/4/3/31
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spelling doaj-b00abe5b3da14ea88a0303b8ea8adbe02020-11-24T21:31:58ZengMDPI AGEconometrics2225-11462016-07-01433110.3390/econometrics4030031econometrics4030031Market Microstructure Effects on Firm Default Risk EvaluationFlavia Barsotti0Simona Sanfelici1Risk Methodologies, Group Financial Risks, Group Risk Management, UniCredit Spa, Piazza Gae Aulenti, Tower A, Floor 20, Milano 20154, ItalyDepartment of Economics, University of Parma, Parma 43125, ItalyDefault 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.http://www.mdpi.com/2225-1146/4/3/31structural modelsdefault probabilitystochastic volatilityjumpsnon-parametric volatility estimationhigh-frequency data
collection DOAJ
language English
format Article
sources DOAJ
author Flavia Barsotti
Simona Sanfelici
spellingShingle Flavia Barsotti
Simona Sanfelici
Market Microstructure Effects on Firm Default Risk Evaluation
Econometrics
structural models
default probability
stochastic volatility
jumps
non-parametric volatility estimation
high-frequency data
author_facet Flavia Barsotti
Simona Sanfelici
author_sort Flavia Barsotti
title Market Microstructure Effects on Firm Default Risk Evaluation
title_short Market Microstructure Effects on Firm Default Risk Evaluation
title_full Market Microstructure Effects on Firm Default Risk Evaluation
title_fullStr Market Microstructure Effects on Firm Default Risk Evaluation
title_full_unstemmed Market Microstructure Effects on Firm Default Risk Evaluation
title_sort market microstructure effects on firm default risk evaluation
publisher MDPI AG
series Econometrics
issn 2225-1146
publishDate 2016-07-01
description 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.
topic structural models
default probability
stochastic volatility
jumps
non-parametric volatility estimation
high-frequency data
url http://www.mdpi.com/2225-1146/4/3/31
work_keys_str_mv AT flaviabarsotti marketmicrostructureeffectsonfirmdefaultriskevaluation
AT simonasanfelici marketmicrostructureeffectsonfirmdefaultriskevaluation
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