Estimation in Financial Models - RiskLab

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We can use the previous results to estimate from possibly incomplete observations of fX ti g n i=0 of the type given by (3.28). Note that in many applications, the non-random elements D ti (3.40), S ti (3.41) and V ti (3.39), can not be calculated exactly. The solution to (3.37) may be unknown. In that case fX ti g n i=0 can be approximated by the Euler approximation (see Appendix B.1). But even if the solution to (3.37) is known, S ti and V ti often can not be calculated exactly, and hence have to be approximated; this can be done by dierent methods depending on the concrete application. Martingale estimating functions The following approach is proposed by Bibby and Srensen [8]. We consider one-dimensional diusion processes dened by the stochastic dierential equations dX t = b(X t ; ) dt + (X t ; ) dW t ; (3.42) where X 0 = x 0 and t 0. Besides the usual assumptions on b and in (3.1), such that (3.42) has a unique solution for all in an open subset IR, the functions b and are supposed to be twice continuously dierentiable with respect to both arguments and is assumed to be positive. In contrast to (3.1), for convenience here we only consider the time-homogeneous case. To simplify the exposition further, assume that we can observe fX t g at discrete equidistant time points, say ; 2;:::;n. Later we will give an extension to the case where X and are multi-dimensional. Our goal is to estimate the parameter from these discrete observations X ;X 2 ;:::;X n of fX t g. Inference from discrete time observations can be based on an approximation of the score function of the continuous loglikelihood function l t (). Denote this approximation by ~ _ ln (). For the denition of the continuous log-likelihood see 3.1.1, p.17. In the case where does not depend on the continuous time log-likelihood function is l t () = Z t 0 b(X s ; ) 2 (X s ) dX s , 1 2 Z t 0 b 2 (X s ; ) 2 (X s ) ds; (3.43) see also (3.5). If we replace the Lebesgue integrals and the Itô integrals by Riemann-Itô sums and dierentiate with respect to we get the approximate score function nX _b(X (i,1) ; ) nX _~l n () = i=1 2 (X (i,1) ) (X b(X (i,1) ; ) b(X i , X (i,1) ) , _ (i,1) ; ) : i=1 2 (X (i,1) ) (3.44) 33
If depends on we use the same estimating function nX _b(X (i,1) ; ) _~l n () = i=1 2 (X (i,1) ; ) (X b(X (i,1) ; ) b(X i , X (i,1) ) , _ (i,1) ; ) : i=1 2 (X (i,1) ; ) (3.45) By using this approach for the estimation of the problem of inconsistency arises as already mentioned in the introduction of this section, p.23. To avoid this problem we can use martingale estimating functions of which we will construct four dierent types. The idea is to modify the discretized scorefunction ~ _ ln () in suchaway that a zero-mean P -martingale is obtained. Then the estimator can be shown to be consistent and asymptotically normal. (1) Our rst approach is to compensate _ ~ ln , so that a martingale ~G n is obtained. By substracting from _ ~ ln () its compensator we get a zero-mean P -martingale with respect to the ltration dened by F i = (X ;:::;X i ), i = 1; 2;:::. The compensator is: nX E _ ~ li () , ~ _ li,1 ()jF i,1 i=1 where = nX i=1 , nX _b(X (i,1) ; ) 2 (X (i,1) ; ) (F (X (i,1); ) , X (i,1) ) nX i=1 b(X (i,1) ; ) b(X _ (i,1) ; ) ; (3.46) 2 (X (i,1) ; ) F (X (i,1) ; ) E (X i jX (i,1) ): (3.47) Thus we obtain a zero-mean martingale estimating function of the form ~G n () = nX i=1 _b(X (i,1) ; ) 2 (X (i,1) ; ) Xi , F (X (i,1) ; ) : (3.48) (2) Alternatively we consider the general class of zero-mean P - martingale estimating functions G n () = nX g i,1 (X (i,1) ; ) X i , F (X (i,1) ; ) ; (3.49) i=1 where for i =1;:::;n, the function g i,1 is F i,1 -measurable and continuously dierentiable in . The optimal estimating function within the class (3.49) in the asymptotic sense of Godambe and Heyde [33] is G n() = nX i=1 _ F (X (i,1) ; ) (X (i,1) ; ) 34 Xi , F (X (i,1) ; ) ; (3.50)
Page 1 and 2: Estimation in Financial Models RISK
Page 3 and 4: 5 Some Diusion Models with Explicit
Page 5 and 6: ing functions or methods based on a
Page 7 and 8: wonder why people still use the Bla
Page 9 and 10: Chapter 2 Approximations 2.1 Diusio
Page 11 and 12: 1) fX kh g: the family of discrete
Page 13 and 14: By means of Theorem 1we now obtain
Page 15 and 16: 2.2 Discrete time models by diusion
Page 17 and 18: Chapter 3 Parameter Estimation 3.1
Page 19 and 20: which in the case (3.6) has the for
Page 21 and 22: (see [49], p.249f). Asymptotic norm
Page 23 and 24: a convergence in probability result
Page 25 and 26: time between observations is bounde
Page 27 and 28: For xed 0 s < t, x 2 IR d , 2 an
Page 29 and 30: After these theoretical considerati
Page 31 and 32: Considering the system (3.26) we di
Page 33: An Application As an application of
Page 37 and 38: The asymptotic properties of the es
Page 39 and 40: Corollary 2 For we have F (X (i,1)
Page 41 and 42: disadvantage of ~G n and G n motiv
Page 43 and 44: 3.2 Discrete models In section 1.2
Page 45 and 46: The sequence hMi n is called the sq
Page 47 and 48: of time varying variances and covar
Page 49 and 50: The Hessian is @ 2 l t @@ 0 = , 1 @
Page 51 and 52: 3.2.3 The Bayesian estimation metho
Page 53 and 54: f() with respect to (jx). If 1 ; 2
Page 55 and 56: p( 1 j 2 ) and we have the a priori
Page 57 and 58: Chapter 4 Nonparametric Estimation
Page 59 and 60: for model (4.2) and the estimator ^
Page 61 and 62: Chapter 5 Some Diusion Models with
Page 63 and 64: and thus X t = t;t0 X t0 + Z t t 0
Page 65 and 66: If we do not specialize at this poi
Page 67 and 68: where we assume the choice of b 1 b
Page 69 and 70: where h is given by (5.42). Again w
Page 71 and 72: Appendix A The Kalman-Bucy Filter A
Page 73 and 74: A.2 The discrete case For reasons o
Page 75 and 76: we can write the Euler scheme (B.1)
Page 77 and 78: Appendix C The Itô Formula C.1 The
Page 79 and 80: Appendix D The Radon-Nikodym Theore
Page 81 and 82: [10] Billingsley,P., 1968, \Converg
Page 83 and 84: [38] Ibragimov, I.A. and R.Z. Has'm
Page 85:
[63] Pedersen, A.R., 1994, \Statist
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