Principal Volatility Component Analysis
Bibliographic Data
| ID | 19418263 |
|---|---|
| Authors | Yu-Pin Hu, Yu‐Pin Hu (National Chi Nan University), Ruey S Tsay (0000-0002-4949-4035, University of Chicago) |
| Year | 2014 |
| Volume | 32 |
| Issue | 2 |
| Pages | 153-164 |
| Publication date | 2014-04-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2013.818006 |
| OpenAlex | W2039411708 |
| Language | EN |
| Citations received | 4 |
| References cited | 14 |
Many empirical time series such as asset returns and traffic data exhibit the characteristic of time-varying conditional covariances, known as volatility or conditional heteroscedasticity. Modeling multivariate volatility, however, encounters several difficulties, including the curse of dimensionality. Dimension reduction can be useful and is often necessary. The goal of this article is to extend the idea of principal component analysis to principal volatility component (PVC) analysis. We define a cumulative generalized kurtosis matrix to summarize the volatility dependence of multivariate time series. Spectral analysis of this generalized kurtosis matrix is used to define PVCs. We consider a sample estimate of the generalized kurtosis matrix and propose test statistics for detecting linear combinations that do not have conditional heteroscedasticity. For application, we applied the proposed analysis to weekly log returns of seven exchange rates against U.S. dollar from 2000 to 2011 and found a linear combination among the exchange rates that has no conditional heteroscedasticity
Curse of dimensionality · Econometrics · Heteroscedasticity · Kurtosis · Principal component analysis · Statistics · Complex Systems and Time Series Analysis · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics
Forecasting Conditional Covariance Matrices in High-Dimensional Time Series
Volatility Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Multivariate Volatility
Modeling Multivariate Volatilities via Latent Common Factors
Dynamic Score-Driven Independent Component Analysis
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,4 |
| Citation span | 2016 - 2023 (8) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 4 |