Firm’s Volatility Risk Under Microstructure Noise

Barsotti, Flavia; Sanfelici, Simona

doi:10.1007/978-3-319-02499-8_5

Cited by 1 publication

(4 citation statements)

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“…The commonly used Realized Volatility estimator is unable to provide reliable estimates for equity volatility in the presence of market microstructure noise, leading to a significant underestimation of both asset volatility and default probabilities. This confirms the results obtained by [16] and extend their validity to the case of stochastic volatility models with jump components for the firm's assets.…”

Section: Discussionsupporting

confidence: 89%

“…The Monte Carlo analysis has been developed for firms belonging to A rating class, revealing that the choice of the non-parametric volatility estimator has a strong influence on risk evaluation. This confirms the results of the analysis in [16] and extends their validity to the case of stochastic volatility models with jump components for the firm's assets.…”

Section: Introductionsupporting

confidence: 85%

“…From an econometric point of view, the commonly used 5-min Realized Volatility estimator is unable to provide reliable estimates for equity volatility in the presence of market microstructure noise, leading to a significant underestimation of both asset volatility and default probabilities and causing a wrong prediction of the real firm's risk profile. Obviously, the Realized Volatility estimator based on noisy 1-min returns would provide even worse estimates, thus confirming results already discussed in [16]. For this reason, in this paper we discuss full results for the 5-min Realized Volatility estimator providing a more fair benchmark for all the other estimates.…”

Section: Introductionsupporting

confidence: 80%

“…In this paper, we carry on the analysis in [16] considering a structural approach à la Merton (see [1]) for defaultable corporate bonds in the presence of market microstructure noise and study a class of models for the underlying firm's assets value dynamics allowing for: (i) stochastic asset return volatility; (ii) jump component. This extension to stochastic volatility and jump component for the underlying assets is deemed to be particularly significant from a credit risk modeling perspective: indeed, pricing and hedging issues related to equity markets suggest that stochastic volatility and jumps are crucial in order to improve credit spreads predictions.…”

Section: Introductionmentioning

confidence: 99%

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Market Microstructure Effects on Firm Default Risk Evaluation

Barsotti

Sanfelici

2016

Econometrics

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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.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionsupporting

confidence: 85%

Section: Introductionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

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