Arbeitspapier
Spot Variance Path Estimation and its Application to High Frequency Jump Testing
This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the presence of diurnal variance patterns, jumps, leverage effects and microstructure noise. We rely on parametric and nonparametric methods. The estimated spot variance path can be used to extend an existing high frequency jump test statistic, to detect arrival times of jumps and to obtain distributional characteristics of detected jumps. The effectiveness of our approach is explored through Monte Carlo simulations. It is shown that sparse sampling for mitigating the impact of microstructure noise has an adverse effect on both spot variance estimation and jump detection. In our approach we can analyze high frequency price observations that are contaminated with microstructure noise without the need for sparse sampling, say at fifteen minute intervals. An empirical illustration is presented for the intraday EUR/USD exchange rates. Our main finding is that fewer jumps are detected when sampling intervals increase.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 09-110/4
- Klassifikation
-
Wirtschaft
Hypothesis Testing: General
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
General Financial Markets: General (includes Measurement and Data)
Information and Market Efficiency; Event Studies; Insider Trading
- Thema
-
high frequency
intraday periodicity
jump testing
leverage effect
microstructure noise
pre-averaged bipower variation
spot variance
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Bos, Charles S.
Janus, Pawel
Koopman, Siem Jan
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2009
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Bos, Charles S.
- Janus, Pawel
- Koopman, Siem Jan
- Tinbergen Institute
Entstanden
- 2009
Ähnliche Objekte (12)
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production
Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices
Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices
Testing the assumptions behind importance sampling
A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League
Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects
The dynamic factor network model with an application to global credit risk
The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures
Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series
Time Series Modelling of Daily Tax Revenues
On Importance Sampling for State Space Models
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production
Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices
Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices
Testing the assumptions behind importance sampling
A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League
Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects
The dynamic factor network model with an application to global credit risk
The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures
Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series
Time Series Modelling of Daily Tax Revenues
On Importance Sampling for State Space Models
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production
Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices
Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices
Testing the assumptions behind importance sampling
A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League
Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects
The dynamic factor network model with an application to global credit risk
The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures
Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series
Time Series Modelling of Daily Tax Revenues