Modelling volatility by variance decomposition
By Cristina Amado, Timo Teräsvirta. 2013
142 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/fc13bb7f0c2bf358dece55771746b72a834a686b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Cristina Amado
- Rainer Dahlhaus
- Timo Teräsvirta
- S. Rao
- Eric Zivot
- Maria Pacurar
- K. Hubrich
- Carsten Jentsch
In Finance
- Cristina Amado
- Rainer Dahlhaus
- Timo Teräsvirta
- S. Rao
- Eric Zivot
- Maria Pacurar
- Carsten Jentsch
- Marcelo C. Medeiros
Citations in the corpus
- Modelling changes in the unconditional variance of long stock return series (2012, methodology): “The asymptotic properties of the maximum likelihood estimators of the TV-GJRGARCH model have been established in Amado and Teräsvirta (2011). In this work, the long time series requires some modifications to the estimation algorithm.”
- Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, unknown): “More recently, the focus has shifted to multiplicative component models (see, e.g., Amado & Teräsvirta, 2013, 2017; Engle, Ghysels, & Sohn, 2013; Engle & Rangel, 2008; Han & Kristensen, 2015).”
- Conditional Correlation Models of Autoregressive Conditional Heteroscedasticity With Nonstationary GARCH Equations (2014, methodology): “van Bellegem and von Sachs (2004), Engle and Gonzalo Rangel (2008) and, independently, Amado and Teräsvirta (2011) assumed global nonstationarity and, among other things, developed an approach in which volatility is modelled by a multiplicative decomposition of the variance to a nonstationary and st”
- Locally Stationary Processes (2011, methodology): “(iv) Transition models: Amado and Teräsvirta (2011) have recently used the logistic transition function to model parameter transitions in GARCH-models.”
- Macro-Finance Determinants of the Long-Run Stock-Bond Correlation: The DCC-MIDAS Specification (2016, unknown): “Combining the spline GARCH framework and the volatility decomposing approach (see Ding and Granger, 1996; Engle and Lee, 1999; Bauwens and Storti, 2009; Amado and Teräsvirta, 2013), Engle et al. (2012) introduce the GARCHMIDAS model.”