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Modeling exchange rate volatility using GARCH models
ä»åã¯ã以äžã®èšéçµæžåŠã®è«æããåŠãã ããšãã¢ãŠããããããŠãããããšæããŸãð
Modeling exchange rate volatility using GARCH models
Basma Almisshala, Mustafa Emirb
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ãã©ãã£ãªãã£ã®æšèšã¢ãã«(Volatility modelling)
The main purpose of modelling volatility is being able to forecast future trends.
Typically, a volatility model is used to forecast the returnsâ absolute largeness (Engle and Patton, 2001).
The symmetric and asymmetric effect of GARCH family models have been used in this paper in modelling the volatility of the exchange rate return time series of USD/TRY.
Symmetric effect models such as GARCH (p,q) and asymmetric effect have been captured through hiring GJR-GARCH (p,q), EGARCH (p,q), and PGARCH(p.q).
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Conditional variance equation for ARCH (q)
The conditional variance equation is calculated as a constant + the previous value of the squared error:Eq(4).
It should be noted that α1 has to be positive since itself and has to be positive as they are squared terms.
Increasing the value of q in ARCH(q) model where q is the number of lags in conditional variance equation, would eventually remove ARCH effect from residuals, and this probably is not the most parsimonious model.
The parsimonious model however simply means accurately modelling a variableâs DGP with the fewest possible parameters.
Instead of estimating ARCH (7) model for USD and ARCH (3) for EUR, it would be better to estimate GARCH (1,1) model since this is more parsimonious (uses fewer parameters in the conditional variance equation).
ARCH(q)ã¢ãã«ã®æ¡ä»¶ä»ãåæ£æ¹çšåŒã¯ãå®æ° + äºä¹èª€å·®ã®åã®å€ãšããŠä»¥äžã«ç€ºãåŒ(4)ã®ããã«èšç®ãããŸã
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\\The conditional variance equation\\ \\\sigma_t^2=a_0+a_1u_{t-1}^2+\cdots +a_i u_{t-i}^2 (4)
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