000	02194nam  2200313za 4500
001	9.856758
003	CaOODSP
005	20221107155615
007	cr |||||||||||
008	180518s2018    oncd    ob   f000 0 eng d
040	|aCaOODSP|beng
041	|aeng|bfre
086	1 |aFB3-5/2018-21E-PDF
100	1 |aGoldman, Elena.
245	10|aAnalysis of asymmetric GARCH volatility models with applications to margin measurement |h[electronic resource] / |cby Elena Goldman and Xiangjin Shen.
260	|a[Ottawa] : |bBank of Canada, |c2018.
300	|aii, 54 p. : |bcol. charts.
490	1 |aBank of Canada staff working paper, |x1701-9397 ; |v2018-21
500	|a"May 2018."
504	|aIncludes bibliographical references.
520	3 |a"We explore properties of asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models in the threshold GARCH (GTARCH) family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both autoregressive conditional heteroscedasticity (ARCH) and GARCH terms. Based on maximum likelihood estimation of S&P 500 returns, S&P/TSX returns and Monte Carlo numerical example, we find that the proposed more general asymmetric volatility model has better fit, higher persistence of negative news, higher degree of risk aversion and significant effects of macroeconomic variables on the low-frequency volatility component. We then apply a variety of volatility models in setting initial margin requirements for a central clearing counterparty (CCP). Finally, we show how to mitigate procyclicality of initial margins using a three-regime threshold autoregressive model"--Abstract, p. ii.
546	|aIncludes abstract in French.
693	4|aClearinghouses (Banking)
693	4|aPayment
693	4|aEconometrics
700	1 |aShen, Xiangjin.
710	2 |aBank of Canada.
830	#0|aStaff working paper (Bank of Canada)|x1701-9397 ; |v2018-21.|w(CaOODSP)9.806221
856	40|qPDF|s737 KB|uhttps://publications.gc.ca/collections/collection_2018/banque-bank-canada/FB3-5-2018-21-eng.pdf