Jumps

Multipower estimators, widespread for their robustness to the presence of jumps, are also useful for reducing the estimation error of integrated volatility powers even in the absence of jumps. Optimizing linear combinations of multipowers can indeed drastically reduce the variance with respect to traditional estimators. In the case of quarticity, we also prove that the optimal combination is a nearly efficient estimator, being arbitrarily close to the nonparametric efficiency bound as the number of consecutive returns employed diverges. We provide guidance on how to select the optimal number of consecutive returns to minimize mean square error. The implementation on US stock prices corroborates our theoretical findings and further shows that our proposed quarticity estimator noticeably reduces the number of detected jumps, and improves the quality of volatility forecasts.

show abstract

“…where MN denotes a standard mixed normal distribution, and variance. This fact has also been remarked in the paper of Mancini and Calvori (2012).…”

Section: The Case J =supporting

confidence: 72%

Efficient Multipowers*

Kolokolov

Renò

2017

Journal of Financial Econometrics

View full text Add to dashboard Cite

show abstract

“…For a summary see e.g. Mancini & Calvori (2012). So even if the FIEGARCH process could well approximate the true underlying volatility, it is important to study the jump process and use the additional information for forecasts improvement.…”

Section: Fiegarch-jump Modelmentioning

confidence: 99%

“…1) and J t ; t = 1, 2, ..., T is a Jump process modeled as a sum of intraday jumps, whereas the number of intraday jumps in one day follows a Poisson process with parameter λ = 0.028 and their size is drawn from a normal distribution N (0, 0.2). The Jump process is based on Mancini & Calvori (2012), with parameters slightly adjusted (originally λ = 0.014 and sizes follow N (0, 0.25) ) based on analysis of resulting simulations and comparison with real data. Moreover, unlike in the previous Monte Carlo experiment, a non-zero constant is assumed.…”

Section: Fiegarch-jump Modelmentioning

confidence: 99%

Estimation of long memory in volatility using wavelets

Kraicová

Baruník

2017

Studies in Nonlinear Dynamics &Amp; Econometrics

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This work studies wavelet-based Whittle estimator of the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroscedasticity (FIEGARCH) model, often used for modeling long memory in volatility of financial assets. The newly proposed estimator approximates the spectral density using wavelet transform, which makes it more robust to certain types of irregularities in data. Based on an extensive Monte Carlo study, both behaviour of the proposed estimator and its relative performance with respect to traditional estimators are assessed. In addition, we study properties of the estimators in presence of jumps, which brings interesting discussion. We find that wavelet-based estimator may become an attractive robust and fast alternative to the traditional methods of estimation. Terms of use: Documents in AbstractThis work studies wavelet-based Whittle estimator of the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroscedasticity (FIEGARCH) model often used for modeling long memory in volatility of financial assets. The newly proposed estimator approximates the spectral density using wavelet transform, which makes it more robust to certain types of irregularities in data. Based on an extensive Monte Carlo study, both behavior of the proposed estimator and its relative performance with respect to traditional estimators are assessed. In addition, we study properties of the estimators in presence of jumps, which brings interesting discussion. We find that wavelet-based estimator may become an attractive robust and fast alternative to the traditional methods of estimation.

show abstract

“…Testing for jumps and precisely identifying their occurrences is of overwhelming importance in finance since jumps have implications in risk management, portfolio allocation and derivatives pricing (Aït-Sahalia, 2004). Several tests have been proposed in the literature (see Mancini and Calvori, 2012 for a survey). The most popular test is probably the test for finite activity jumps 4 proposed independently by Andersen et al (2007) and Lee and Mykland (2008, LM08 hereafter) and extended by Lee and Mykland (2012, LM12 hereafter) to account for the presence of microstructure noise.…”

Section: Introductionmentioning

confidence: 99%

Volatility estimation and jump detection for drift–diffusion processes

Laurent

Shi

2020

Journal of Econometrics

View full text Add to dashboard Cite

Logarithms of prices of financial assets are conventionally assumed to follow driftdiffusion processes. While the drift term is typically ignored in the infill asymptotic theory and applications, the presence of nonzero drifts is an undeniable fact. The finite sample theory and extensive simulations provided in this paper reveal that the drift component has a nonnegligible impact on the estimation accuracy of volatility and leads to a dramatic power loss of a class of jump identification procedures. We propose an alternative construction of volatility estimators and jump tests and observe significant improvement of both in the presence of nonnegligible drift. As an illustration, we apply the new volatility estimators and jump tests, along with their original versions, to 21 years of 5-minute log-returns of the NASDAQ stock price index.

show abstract

Jumps

Cited by 7 publications

References 0 publications

Efficient Multipowers*

Efficient Multipowers*

Estimation of long memory in volatility using wavelets

Volatility estimation and jump detection for drift–diffusion processes

Contact Info

Product

Resources

About