Premium
Modeling and forecasting intraday VaR of an exchange rate portfolio
Author(s) -
Abbara Omar,
Zevallos Mauricio
Publication year - 2018
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2540
Subject(s) - vine copula , econometrics , economics , value at risk , portfolio , autoregressive model , exchange rate , us dollar , vector autoregression , stock exchange , autoregressive conditional heteroskedasticity , copula (linguistics) , financial economics , volatility (finance) , finance , risk management
The main task of this work was to predict, for the next 15 minutes, the value‐at‐risk (VaR) of an equally weighted portfolio composed of four exchange rates against the American dollar: Japanese yen, euro, Australian dollar and Swiss franc. The dataset consists of transaction prices of each asset recorded every 15 minutes, from January 7, 2013 to December 31, 2013. For each time series, the multiplicative‐component generalized autoregressive conditional heteroskedasticity model of Engle and Sokalska ( Journal of Financial Econometrics , 2012, 10 , 54–83) is fitted, and the dependence among the series is modeled by a D‐vine pair‐copula. VaR predictions are estimated based on simulated observations of the fitted model following the proposal of Berg and Aas ( European Journal of Finance , 2009, 15 , 639–659). The proposed method presents good results in terms of out‐of‐sample intraday VaR forecasting.