Unstable volatility: the break-preserving local linear estimator
The objective of this paper is to introduce the break-preserving local linear (BPLL) estimator for the estimation of unstable volatility functions for independent and asymptotically independent processes. Breaks in the structure of the conditional mean and/or the volatility functions are common in Finance. Nonparametric estimators are well suited for these events due to the flexibility of their functional form and their good asymptotic properties. However, the local polynomial kernel estimators are not consistent at points where the volatility function has a break. The estimator presented in this paper generalises the classical local linear (LL). The BPLL estimator maintains the desirable properties of the LL estimator with regard to the bias and the boundary estimation while it estimates the breaks consistently. An extensive Monte Carlo study is shown as well as detailed proofs of the estimator asymptotic behaviour.
Volume (Year): 24 (2012)
Issue (Month): 4 (December)
|Contact details of provider:|| Web page: http://www.tandfonline.com/GNST20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/GNST20|
When requesting a correction, please mention this item's handle: RePEc:taf:gnstxx:v:24:y:2012:i:4:p:883-904. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst)
If references are entirely missing, you can add them using this form.