报告总结

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极隼万
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1 CFEF RR/04/0 CAViaR

2 RR/04/0 004 CAViaR VaR VaR Engle Manganelli 999 VaR CAViaR Chow CAViaR B Engle Manganelli CAViaR Absrac: Value-a-Risk (VaR) has become a sandard ool o measure marke risk widely employed by financial insiuions boh for inernal and regulaory purposes. The sabiliy of a VaR modeling is usually imporan for predicion and economeric inference in pracice. In his paper, he CAViaR model, proposed recenly by Engle and Manganelli (999) o calculae VaR is inroduced, and he Chow s es for parameer insabiliy is carried on o explore he model sabiliy of CAViaR modeling of he risk of he Chinese sock markes. The empirical resuls show ha he 4 popular CAViaR models due o Engle and Manganelli do no fi well he real siuaion of he Chinese sock markes. The negligence of he srucural changes of he Chinese sock markes may lead o he reducion of inerpreabiliy in modeling of marke risk. VaR Value-a-Risk VaR (Basel accord) (EU capial adequacy direcive) VaR VaR

In his paper, he CAViaR model, proposed recenly by Engle and Manganelli (999) o calculae VaR is inroduced, and he Chow s es for parameer insabiliy is carried on o explore he model sabiliy of CAViaR

3 VaR ( Manganelli and Engle, 00) ) ) Mone Carlo 3) VaR Jorion 997, Manganelli and Engle ( VaR 003 Engle Manganelli 999 VaR VaR VaR CAViaR VaR Manganelli and Engle (00) Mone Carlo CAViaR CAViaR --- ( Hansen 99) VaR CAViaR VaR Mone Carlo VaR GARCH GARCH EWMA( ) GARCH ( J.P. Morgan RiskMerics) h = ( ) + 0 < <. ε h, VaR Beder 995 Engle Manganelli 999 GARCH VaR CAViaR CAViaR VaR 3

--- ( Hansen 99) VaR 9. 94 97 8.0 5.9 CAViaR VaR Mone Carlo VaR GARCH GARCH EWMA( ) GARCH ( J.P.

4 ( x) + = max( x,0) ( x) = min( x,0) Engle Manganelli CAViaR SAV AS : GARCH(,) (Adapive): VaR( ) = + VaR ( ) + 3 y VaR ( ) = + VaR ( ) + ( y ) + ( y ) VaR ( ) = + VaR ( ) + y 3 VaR VaR G y VaR ( ) = ( ) {[ exp( [ ( )])] θ} { y } θ G 0 Koenker Basse(978) G [ I( y VaR ( )) ] I() G θ VaR y SAV GARCH(,) GARCH(,), GARCH(,) VaR y VaR VaR VaR CAViaR CAViaR Fisher Chow (960) θ Chow Chow : H 0 : θ 4

) = ( ) {[ exp( [ ( )])] + + + θ} { y } θ G 0 Koenker Basse(978) G [ I( y VaR ( )) ] I() G θ VaR y

5 H θ Chow Chow Davies Inclan Tiao(994) AR D k AR AIC AR C k = k=,,t, D0 = D T = 0 k Dk C T T C k k = ˆ ε ˆ ε ˆ ˆ = y c by = y = (ln( P) ln( P ))*00 P / c T T Inclan Tiao D W 0 k ma x k T / Dk CAViaR A B B y = (ln( P) ln( P ))*00 5

6 %, 50 B B Inclan Tiao T B D k B (a) / k T D (b) B / T k D 4 6

3840 447 Inclan Tiao T B D 999 5 9 000 3 7 00 k 7 30 00 6 4 B 999

7 53 VaR B % CAViaR 5%, 6 9 5% VaR VaR VaR VaR 3 + CAViaR : x 5 ( 6) 0 5 SAV B B B

8705-0.0837 0.895-0.9843 0.8383 0.957 0.8560 0.337 0.95 -.0066 0.890 0.9533 B 0.7943 0.6050 0.

8 5 CAViaR VaR + VaR y regime shif VaR VaR VaR 3 AS B B B B GARCH(,) B B B Adapive

3549 0.559 0.846 0.694.663-0.3857 0.666 0.309 0.35 B 0.5547 0.5340 0.7009 0.07 4 GARCH(,) 3 3 4 5 0.7496 5.65-0.3068 3.0574.04 0.0800 0.80 6.450-0.3530-0.03.6 0.00 B 3.8489 4.0477 4.8553 0.834 0.88-0.

9 SAV B B B B VaR y VaR VaR VaR VaR VaR I( y VaR ) θ > 0 5 % B AS B B B B GARCH(,) 9

566 VaR y VaR VaR VaR VaR VaR I( y VaR ) θ > 0 5 % B 0 3 0 000 3 7 00 6 4.774.65 5 7 AS 3 4 5 0.439 0.34 0.583 0.55 0.94 0.8 0.37 0.9 0.584 0.9855 0.0380-0.006 B 0.857 0.74 0.0954 0.45 0.869 0.053 0.

10 B B B Adapive B SAV GARCH(,) GARCH SAV AS Taylor(986), Schwer(988) Engle and Maganelli (999) 3 AS GARCH(,) AS Bea() ( 0.063) GARCH(,) Bea() (.404) CI 0

3840 0.3947 0.4337 0.335 9 Adapive 5 3 4 5 0.695 0.0779.07 0.0453 0.0384 0.4305 0.936 0 0.93 0.046 0.0 0.687 B 0.8463 0.96 0.699 0.

11 AS 3 5, AS

12 GARCH(,) VaR y y VaR VaR y B A CAViaR B B B B B B A 4 CAViaR Chow Inclan Tiao CAViaR A B ( Hamilon, 994) 003 EWMA

13 [] Andrews, Donald. W. K., Tess For Parameer Insabiliy And Srucural Change Wih Unknown Change Poin. Economerica. 993, Vol.6, No. 6, [] Basle Commiee On Banking Supervision, Amendmen To The Capial Accord To Incorporae Marke Risks [3] Beder, T. S., VaR: Seducive Bu Dangerous, Financial Analyss Journal. 995,Sep./Oc.,-4. [4] Chow, G. C., Tess Of Equaliy Beween Ses Of Coefficiens In Two Linear Regressions, Economerica. 960,Vol.8, No., [5] Davies, R. B., Hypohesis Tesing When A Nuisance Parameer Is Presen Only Under The Alernaive, Biomerika. 977, Vol. 64, No., [6] Davies, R. B., Hypohesis Tesing When A Nuisance Parameer Is Presen Only Under The Alernaive, Biomerika. 987, Vol. 74, No., [7] Engle, R. F. and Manganelli S., CAViaR: Condiional Auoregressive Value A Risk By Regression Quaniles. NBER, Working Paper 734, 999. [8] Hamilon J. D., Time Series Analysis, Princeon Universiy Press, New Jersey [9] Hansen B.E., Tesing For Parameer Insabiliy In Linear Models, Journal of Policy Modeling. 99,Vol.4, No.4, [0], VaR 003, Vol. 5, No.7, [] Inclan, C. and Tiao, G. C., Use Of Cumulaive Sums Of Squares For Rerospecive Deecion Of Changes Of Variances. J. Amer. Sais. Assoc. 994, Vol.89, No.3, [] Jorion, P., Value A Risk:The New Benchmark For Conrolling Marke Risk. Chicago: Irwin Professional Publishing [3] J.P. Morgan, Risk Merics Monior. nd Quarer,996. [4]Koenker,R. and Basse J., Regression Quaniles, Economerica. 978, Vol.46, No., [5] Manganelli, S. and Engle, R.F., Value A Risk Models In Finance, working paper No. 75, European Cenral Bank.00. [6] Schwer, G. W., Why Does Sock Marke Volailiy Change Over Time? Journal of Finance. 988,Vol.XLIV, No.5, [7] Taylor, S., Modeling Financial Time Series. John Wiley and Sons, London jcdonglc@gscas.ac.cn 3

[4] Chow, G. C., Tess Of Equaliy Beween Ses Of Coefficiens In Two Linear Regressions, Economerica. 960,Vol.8, No.,59-605. [5] Davies, R. B., Hypohesis Tesing When A Nuisance Parameer Is Presen Only Under The Alernaive, Biomerika.

深圳股票市场稳定性研究报告

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