Conditional Volatility and Distribution of Exchange Rates: GARCH and FIGARCH Models with NIG Distribution
This paper extends the Fractionally integrated GARCH (FIGARCH) model by incorporating Normal Inverse Gaussian Distribution (NIG). The proposed model is flexible and allows one to model time-variation, long memory, fat tails as well as asymmetry and skewness in the distribution of financial returns. GARCH and FIGARCH models for daily log exchange rate returns with Normal, Student's t and NIG error distributions as well as GARCH/FIGARCH-in-mean models with t errors are estimated and compared both in terms of sample fit as well as out-of-the-sample predictive ability in several dimensions. The FIGARCH model with symmetric and asymmetric NIG errors outperform alternatives both in-sample fit and 1-day and 5-day ahead predictions of the quartiles of the exchange rate return distributions.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Artikel in diesem Heft
- Article
- Conditional Volatility and Distribution of Exchange Rates: GARCH and FIGARCH Models with NIG Distribution
- Detecting Multiple Changes in Persistence
- Complex Dynamics in the Neoclassical Growth Model with Differential Savings and Non-Constant Labor Force Growth
- A Threshold Model of Real U.S. GDP and the Problem of Constructing Confidence Intervals in TAR Models
- Which Are the World's Wobblier Currencies? Reference Exchange Rates and Their Variation
- Wavelet Variance Analysis of Output in G-7 Countries
Artikel in diesem Heft
- Article
- Conditional Volatility and Distribution of Exchange Rates: GARCH and FIGARCH Models with NIG Distribution
- Detecting Multiple Changes in Persistence
- Complex Dynamics in the Neoclassical Growth Model with Differential Savings and Non-Constant Labor Force Growth
- A Threshold Model of Real U.S. GDP and the Problem of Constructing Confidence Intervals in TAR Models
- Which Are the World's Wobblier Currencies? Reference Exchange Rates and Their Variation
- Wavelet Variance Analysis of Output in G-7 Countries
