.png){kind=icon}
Return
Functional data analysis for volatility
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2011.08.002.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We introduce a functional volatility process for modeling volatility trajectories for high frequency observations in financial markets and describe functional representations and data-based recovery of the process from repeated observations. A study of its asymptotic properties, as the frequency of observed trades increases, is complemented by simulations and an application to the analysis of intra-day volatility patterns of the S&P 500 index. The proposed volatility model is found to be useful to identify recurring patterns of volatility and for successful prediction of future volatility, through the application of functional regression and prediction techniques. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Diffusion model
Functional principal component
Functional regression
High frequency trading
Market returns
Prediction
Volatility process
Trajectories of volatility
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
5.3K
Citations:
3.0W
Organization
Cited Papers
ShareSaveShareSaveThe effects of random and discrete sampling when estimating continuous-time diffusions.png)
ECONOMETRICAOAAI
ECONOMETRICAIF7.1
OAAIShareSave
View more