Research Seminar: Department of Econometrics and Business Statistics
The Department of Econometrics and Business Statistics invites you to a research seminar 'Volatility Estimation and Jump Detection for drift-diffusion Processes' presented by Dr Shuping Shi from Macquarie University.
No RSVP is required.
Abstract summary
Logarithms of prices of financial assets are conventionally assumed to follow drift-diffusion 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.
A copy of the paper can be downloaded here.
Event Details
- Date:
- 19 October 2018 at 2:00 pm – 3:15 pm
- Venue:
- H4.87, Caulfield campus, 900 Dandenong Road, Caulfield East
- Categories:
- Econometrics and Business Statistics
Description
The Department of Econometrics and Business Statistics invites you to a research seminar 'Volatility Estimation and Jump Detection for drift-diffusion Processes' presented by Dr Shuping Shi from Macquarie University.
No RSVP is required.
Abstract summary
Logarithms of prices of financial assets are conventionally assumed to follow drift-diffusion 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.
A copy of the paper can be downloaded here.