Estimating the structural credit risk model when equity prices are contaminated by trading noises

Duan, Jin‐Chuan; Fülöp, András

doi:10.1016/j.jeconom.2008.12.003

Cited by 57 publications

(68 citation statements)

References 23 publications

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“…If one could observe the efficient (i.e., not contaminated by noise) equity price, than equity volatility could be estimated by the well known Realized Volatility estimator [27] at any desired accuracy level using high frequency data. However, as stressed by [7], the relationship between the unobserved asset and the observed equity value predicted by the pricing formula (3) may be masked by trading noise in reality. Econometric literature suggests that observed equity prices can diverge from their equilibrium value due to illiquidity, asymmetric information, price discreteness and other measurement errors.…”

Section: Market Microstructure Noise On High-frequency Equity Pricesmentioning

confidence: 99%

“…Once the underlying asset value dynamics is generated by model (2), high-frequency equity prices are obtained through the no arbitrage method given by Equation (3). Market microstructure noise is considered, alternatively, for both cases described by Equations (6) and (7). The random shocks η j are i.i.d Gaussian random variables with zero mean and standard deviation equal to 1.4 times the log-equity return standard deviation.…”

Section: Equity Volatility Estimation With High-frequency Datamentioning

confidence: 99%

“…The random shocks η j are i.i.d Gaussian random variables with zero mean and standard deviation equal to 1.4 times the log-equity return standard deviation. We set α = 0.5 in the dependent noise case (7).…”

Section: Equity Volatility Estimation With High-frequency Datamentioning

confidence: 99%

“…the jump parameter in Bates model is studied in Section 6.2. In both cases we consider a sample of 1000 days for the Monte Carlo simulation and a market microstructure effect described by a Correlated noise given in Equation (7). The simulations are based on equispaced intra-day data generated on a 1-min frequency.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…This issue has been analyzed in [7], where the authors extend [8] to explicitly account for trading noise contamination on equity prices: they devise a particle filter-based maximum likelihood method based solely on the time series of observed equity values, showing its robustness with respect to market microstructure effects. The importance of using high frequency data to back out parameters involved in firm's value dynamics has been highlighted in [9]: the authors propose a novel approach to identify the volatility and jump risks components of individual firms from high-frequency observed equity prices, arguing that these new measures can allow to capture the effects of stochastic volatility and jumps in the firm's underlying asset dynamics ( [3,10]).…”

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: Market Microstructure Noise On High-frequency Equity Pricesmentioning

confidence: 99%

Section: Equity Volatility Estimation With High-frequency Datamentioning

confidence: 99%

Section: Equity Volatility Estimation With High-frequency Datamentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Market Microstructure Effects on Firm Default Risk Evaluation

Barsotti

Sanfelici

2016

Econometrics

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Estimating structural credit risk models when market prices are contaminated with noise

Lee

2015

Appl Stoch Models Bus & Ind

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In this paper, sequential estimation on hidden asset value and model parameter estimation is implemented under the Black-Cox model. To capture short-term autocorrelation in the stock market, we assume that market noise follows a mean reverting process. For estimation, Bayesian methods are applied in this paper: the particle filter algorithm for sequential estimation of asset value and the generalized Gibbs and multivariate adapted Metropolis methods for model parameters estimation. The first simulation study shows that sequential hidden asset value estimation using both option price and equity price is more efficient than estimation using equity price alone. The second simulation study shows that, by applying the generalized Gibbs sampling and multivariate adapted Metropolis methods, model parameters can be estimated successfully. In an empirical analysis, the stock market noise for firms with more liquid stock is estimated as having smaller volatility.The mean reverting process assumption is closer to real stock market behavior, which shows short-term dependence. The stock price depends on its previous values, but the shocks to stock prices are all temporary, and stock prices will eventually return to the trend path. Explanations for and empirical testing of the existence of autocorrelation in the stock market are well documented in the studies of Lo and Mackinlay [10,11], Sias and Starks [12], and Chordia and Swaminathan [13]. Following on from the studies of DeBondt and Thaler [14], Poterba [15], and Fama and French [16], who demonstrated the mean reversion property of stock prices, in recent years, there has been increasing evidence supporting the mean reversion hypothesis [17][18][19][20][21].Second, we estimate parameters and unknown asset processes together under the Black-Cox structural model [22]. Previous studies on estimation [6,7] were based on the Merton model, which gives a relatively simple inverse function of equity and asset values. The iterative scheme [23] and the well-known Kealhofer, McQuown and Vasicek (KMV) method (or transformed-data maximum likelihood (ML) method described in the study of Duan [24,25]) are based on the Merton model. However, the study of Jones et al. [26] showed that the predicted bond price for 1977-1981 obtained from the Merton model was over-estimated by 4.52% on average.The Black-Cox model assumes that default can occur at any time prior to the maturity of the bond. It has a default assumption that is more relaxed and closer to the real world, in that under the usual bond contract in practice, a firm needs to pay annual interest to the bond holder, meaning that default can occur at any time before the bond matures. Third, we model the prices of multiple asset classes (debts, equity, and options on equity) in order to gain a better understanding of a firm's capital structure as a whole and also to enhance the precision of the estimation. In the financial market, corporations tend to issue multiple classes of securities to raise their capital. Therefore, the stock marke...

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Volatility forecasting using stochastic conditional range model with leverage effect

Xie

2019

Appl Stoch Models Bus & Ind

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In this paper, we propose a stochastic conditional range model with leverage effect (henceforth SCRL) for volatility forecasting. A maximum likelihood method based on the particle filters is developed to estimate the parameters of the SCRL model. Simulation results show that the proposed methodology performs well. We apply the proposed model and methodology to four stock market indices, the Shanghai Stock Exchange Composite Index of China, the Hang Seng Index of Hong Kong, the Nikkei 225 Index of Japan, and the S&P 500 Index of US. Empirical results highlight the value of incorporating leverage effect into range modeling and forecasting. In particular, the results show that our SCRL model outperforms the conditional autoregressive range model, the conditional autoregressive range model with leverage effect, and the stochastic conditional range model in both in‐sample fit and out‐of‐sample forecast.

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Estimating the structural credit risk model when equity prices are contaminated by trading noises

Cited by 57 publications

References 23 publications

Market Microstructure Effects on Firm Default Risk Evaluation

Market Microstructure Effects on Firm Default Risk Evaluation

Estimating structural credit risk models when market prices are contaminated with noise

Volatility forecasting using stochastic conditional range model with leverage effect

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