Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations.
|
|
- Rudolph Watson
- 5 years ago
- Views:
Transcription
1 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 1/81 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. Francesco Russo, ENSTA ParisTech STOCHASTIC ANALYSIS, CONTROLLED DYNAMICAL SYSTEMS AND APPLICATIONS Jena, March 9-13th 2015 In honour of Prof. Dr. Hans-Jürgen ENGELBERT Covers joint work with Cristina Di Girolami (Pescara) and Andrea Cosso (Politecnico di Milano and Paris VII).
2 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 2/81 Hans-Jürgen ENGELBERT A great probabilist: a pionner on stochastic differential equations with singular drift. Many souvenirs. One in particular: the conference of Eisenach (DDR) 1986.
3 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 3/81 Outline 1. About a robust representation problem for random variables. 2. Finite dimensional calculus via regularization. 3. Stochastic calculus via regularizations in Banach spaces. 4. Window processes. 5. Towards a robust Clark-Ocone type formula. 6. Kolmogorov path dependent PDEs. The window of diffusion processes. 7. Path-dependent semilinear Kolmogorov equation.
4 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 4/81 Basic survey reference A. Cosso, C. Di Girolami, F. Russo (2014) Calculus via regularizations in Banach spaces and Kolmogorov-type path-dependent equations.
5 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 5/81 Some related references to our work C. Di Girolami, F. Russo (2010). Infinite dimensional calculus via regularizations and applications. Technical Report. C. Di Girolami, F. Russo (2011). Clark-Ocone type formula for non-semimartingales with finite quadratic variation. Comptes Rendus de l Académie des Sciences, Section Mathématiques. Number 3-4, pp Vol C. Di Girolami, F. Russo (2010). Generalized covariation for Banach space valued processes, Itô formula and applications. Osaka Journal of Mathematics 51 (3), 2014 (forthcoming).
6 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 6/81 C. Di Girolami, F. Russo (2011). Generalized covariation and extended Fukushima decompositions for Banach space valued processes. Application to windows of Dirichlet processes. Infinite dimensional analysis, Quantum probability and related topics (IDA-QP) 15(2): , 50, G. Fabbri, F. Russo (2012). Infinite dimensional weak Dirichlet processes, stochastic PDEs and optimal control. Preprint HAL-INRIA,
7 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 7/81 C. Di Girolami, G. Fabbri and F. Russo (2014). The covariation for Banach space valued processes and applications. Metrika 77: DOI /s A. Cosso and F. Russo (2014). A regularization approach to functional Itô calculus and strong viscosity solutions to path-dependent SDEs. Preprint HAL-INRIA
8 Available preprints: russo/ Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 8/81
9 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 9/81 1 About a robust representation problem for random variables. 1.1 Window processes Let X be a continuous process with quadratic variation [X].
10 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 10/81 Definition 1 Let T > 0 and X = (X t ) t [0,T] be a real continuous process prolongated by continuity. Process X( ) defined by X( ) = {X t (u) := X t+u ;u [ T,0]} will be called window process.
11 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 11/81 X( ) is a C([ T, 0])-valued stochastic process. C([ T, 0]) is a typical non-reflexive Banach space. 1.2 The robust representation Is there a reasonnable rich class of functionals H : C := C([ T,0]) R such that the r.v. h := H(X T ( )) admits a representation of the type
12 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 12/81 and h = V 0 + T 0 Z s d X s, V 0 R, Z adapted process with respect to the canonical filtration of X. Possibly we look for explicit expressions of V 0 and Z.
13 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 13/81 Idea: Representation ofh = H(X T ( )) The idea consists in finding functions u,v : [0,T] C R such that h = H(X T ( )) as In particular V t = u(t,x t ( )), Z t = v(t,x t ( )),t [0,T], V t = h T t Z s d X s,t [0,T]. h = u(0,x 0 ( ))+ T 0 v(s,x s ( ))d X s.
14 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 14/81 u, v are related to a deterministic purely analytical tool as a PDE, in order to keep separated probability and analysis. Previous procedure make Clark-Ocone formula robust with respect to the quadratic variation.
15 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 15/81 Natural extensions. V t = h T t Z s d X s + T t F(s,X s ( ),Y s,z s )d[x] s,t [0,T], for some F : [0,T] C([ T,0] R R). [X] = 0 σ2 (s,x s ( ))ds, σ : [0,T] R R.
16 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 16/81 2 Finite dimensional calculus via regularization Definition 2 Let X (resp. Y ) be a continuous (resp. locally integrable) process. Suppose that the random variables t 0 t Y s d X s := lim ǫ 0 0 Y s X s+ǫ X s ǫ ds exists in probability for every t [0,T].
17 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 17/81 If the limiting random function admits a continuous modification, it is denoted by 0 Yd X and called (proper) forward integral of Y with respect to X. (FR-Vallois 1991) If lim t T t 0 Yd X exists in probability we call that limit improper forward integral of Y with respect to X, again denoted by T 0 Yd X.
18 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 18/81 Covariation of real valued processes Definition 3 The covariation of X and Y is defined by 1 [X,Y] t = lim ǫ 0 + ǫ t 0 (X s+ǫ X s )(Y s+ǫ Y s )ds if the limit exists in the ucp sense with respect to t. Obviously [X,Y] = [Y,X]. If X = Y, X is said to be finite quadratic variation process and [X] := [X,X].
19 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 19/81 Connections with semimartingales Let S 1, S 2 be (F t )-semimartingales with decomposition S i = M i +V i, i = 1,2 where M i (F t )-local continuous martingale and V i continuous bounded variation processes. Then [S i ] classical bracket and [S i ] = M i. [S 1,S 2 ] classical bracket and [S 1,S 2 ] = M 1,M 2. If S semimartingale and Y cadlag and predictable 0 Yd S = 0 YdS (Itô)
20 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 20/81 Itô formula for finite quadratic variation processes Theorem 4 Let F : [0,T] R R such that F C 1,2 ([0,T[ R) and X be a finite quadratic variation process. Then t exists and equals 0 x F(s,X s )d X s t F(t,X t ) F(0,X 0 ) 0 s F(s,X s )ds 1 2 t 0 xx F(s,X s )d[x] s
21 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 21/81 3 Stochastic calculus via regularization in Banach spaces A stochastic integral for B -valued integrand with respect to B-valued integrators, which are not necessarily semimartingales. χ-quadratic variation of X A new concept of quadratic variation which generalizes the tensor quadratic variation and which involves a Banach subspace χ of (Bˆ π B).
22 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 22/81 Definition 5 Let X (resp. Y) be a B-valued (resp. a B -valued) continuous stochastic process. Suppose that the random function defined for every fixed t [0,T] by t 0 t B Y s,d X s B := lim ǫ 0 0 B Y s, X s+ǫ X s ǫ B ds in probability exists and admits a continuous version. Then, the corresponding process will be called forward stochastic integral of Y with respect to X.
23 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 23/81 Connection with Da Prato-Zabczyk integral Let B = H is separable Hilbert space. Theorem 6 Let W be a H-valued Q-Brownian motion with Q L 1 (H) and Y be H -valued process such that t 0 Y s 2 H ds < a.s. Then, for every t [0,T], t 0 H Y s,d W s H = t 0 Y s dw dz s (Da Prato-Zabczyk integral)
24 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 24/81 Notion of Chi-subspace Definition 7 A Banach subspace χ continuously injected into (Bˆ π B) will be called Chi-subspace (of (Bˆ π B) ). In particular it holds χ (Bˆ π B).
25 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 25/81 Chi-quadratic variation Let X be a B-valued continuous process, χ a Chi-subspace of (Bˆ π B), C([0, T]) space of real continuous processes equipped with the ucp topology. Two processes: 1. [X] : χ C([0,T]); 2. [X] : [0,T] Ω χ with bounded variation.
26 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 26/81 They are (loosely speaking) approached by [X] ǫ be the applications [X] ǫ : χ C([0,T]) defined by φ ( t 0 χ φ, (X s+ǫ X s ) 2 ǫ ) χ ds t [0,T],
27 Definition 8 We say that X admits a global quadratic variation (g.q.v.) if it admits a χ-quadratic variation with χ = (Bˆ π B). Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 27/81
28 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 28/81 When χ = (Bˆ π B) H2 is related to weak convergence in (Bˆ π B). If Ω were a singleton then (H2) would imply [X] ǫ t (Φ) ǫ 0 [X] t (Φ), Φ (Bˆ π B),t [0,T]. The g.q.v. [X] is (Bˆ π B) -valued.
29 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 29/81 Infinite dimensional Itô s formula Let B a separable Banach space Theorem 9 Let X a B-valued continuous process admitting a χ-quadratic variation. Let F : [0,T] B R be C 1,2 Fréchet such that D 2 F : [0,T] B χ (Bˆ π B) continuously Then for every t [0,T] the forward integral t 0 B DF(s,X s),d X s B exists and the following formula holds.
30 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 30/81 F(t,X t ) = F(0,X 0 )+ + t 0 t t 0 s F(s,X s )ds+ B DF(s,X s),d X s B χ D 2 F(s,X s ),d [X] s χ
31 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 31/81 4 Window processes. We fix now the attention on B = C = C([ T,0])-valued window processes. X continuous real valued process and X( ) its window process. X = X( )
32 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 32/81 If X has Hölder continuous paths of parameter γ > 1/2, then X( ) has a zero g.q.v. For instance: X = B H fractional Brownian motion with parameter H > 1/2. X = B H,K bifractional Brownian motion with parameters H ]0,1[, K ]0,1] s.t. HK > 1/2. W( ) does not admit a global quadratic variation.
33 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 33/ About a significant Chi-subspace We shall consider the following Chi-subspace of (C([ T,0])ˆ π C([ T,0])) : χ 0 := ( D 0 L 2 ([ T,0] ) ˆ 2 h Diag, with D 0 := {λδ 0 (dx), λ R}, Diag := {µ(dx,dy) = g(x)δ y (dx)dy;g L ([ T,0])}.
34 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 34/81 Evaluations of χ-quadratic variation for window processes Let X be a real finite quadratic variation process.
35 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 35/81 X( ) has a χ 0 -quadratic variation and [X( )] : χ 0 C[0,T] [X( )] t (µ) = dµ(x,y)[x] t+x, D t where D t = {(x,y) t x = y = 0}.
36 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 36/81 In particular, if µ (D 0 L 2 ([ T,0]) ˆ 2 h, [X( )] t (µ) = µ({0,0})[x] t,
37 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 37/81 The particular case of a finite quadratic variation process X such that[x] t = t 0 σ2 (s,x s ( ))ds. If µ χ 0 then [X,X] t (µ) = D t dµ(x,y) If µ (D 0 L 2 ([ T,0])) ˆ 2 h, then t+x 0 σ 2 (s,x s ( ))ds. [X,X] t = µ({(0,0)}) t 0 σ 2 (s,x s ( ))ds.
38 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 38/81 Itô s formula for the corresponding window process. Let F : [0,T] C R of class C 1,2 such that D 2 F : [0,T] C χ 0 continuous. We denote DF(t,η) = D δ 0 F(t,η)δ 0 (dx)+d F(t,η), with D δ 0 F : [0,T] C R. The application of Itô formula gives F(t,X t ) = F(0,X 0 )+ + t 0 t 0 s F(s,X s )ds, D δ 0 F(s,X s )d X s + t 0 L s F(s,X s )ds
39 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 39/81 L t G(η) = I(G)(t,η) + 1 D 2 dxdyg(η)σ 2 2 (t+x,η(x)), D t I(G)(t,η) = D dx G(η)d η(x), provided that [ t,0] I(G)(t,η) = lim ε 0 [ t,0] D dx G(η) η(x+ε) η(x) ε exists and I fulfills some technical assumptions..
40 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 40/81 5 Towards a Robust Clark-Ocone type formula We set B = C = C([ T,0]). X real continuous stochastic process with values in R. X 0 = 0, [X] t = t 0 σ2 (r,x r ( ))dr, where σ : [0,T] C R is continuous.
41 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 41/81 Representation ofh = H(X T ( )) Conformally to what we mentioned at the beginning, we aim at finding functions u,v : [0,T] C R such that In particular V t = u(t,x t ( )), Z t = v(t,x t ( )), V t = h T t Z s d X s,t [0,T]. h = u(0,x 0 ( ))+ T 0 v(s,x s ( ))d X s.
42 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 42/ A toy model (related to the Black-Scholes formula) Let (S t ) be the price of a financial asset of the type S t = exp(σw t σ2 2 t), σ > 0. Let f : R R be a continuous function and h = f(s T ) = f(w T ) where f(y) = f ( exp(σy ). σ2t) 2
43 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 43/81 Let Ũ : [0,T] R R solving { t Ũ(t,x)+ σ2 2 xxũ(t,x) = 0 Ũ(T,x) = f(x) x R. Applying classical Itô formula we obtain h = Ũ(0,S 0)+ = U(0,W 0 )+ T 0 T for a suitable U : [0,T] R R. 0 x Ũ(s,S s )ds s x U(s,W s )dw s,
44 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 44/81 Robustness with respect to the volatility. Does one have a similar representation ifw is replaced by a finite quadratic variationx such that[x] t t? The answer is positive! because of Theorem 4.
45 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 45/81 Proposition 10 Let X such that [X] t = σ 2 t. A1 f : R R continuous and polynomial growth. A2 U C 1,2 ([0,T[ R) C 0 ([0,T] R) such that { t U(t,x)+ σ2 2 xxu(t,x) = 0 v(t,x) = f(x).
46 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 46/81 Then h := f(x T ) = U(0,X 0 )+ T x U(s,X s )d X s 0 }{{} improper forward integral Schoenmakers-Kloeden (1999) Zähle (2002) Coviello-Russo (2006) Bender-Sottinen-Valkeila (2006)
47 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 47/81 Natural question Generalization to the case of path dependent options? As first step we revisit the toy model.
48 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 48/ The toy model revisited Proposition 11 We set B = C = C([ T,0]) and η C and we define Then H : C R, by H(η) := f(η(0)) u : [0,T] C R, by u(t,η) := U(t,η(0)) and solves u C 1,2 ([0,T[ C;R) C 0 ([0,T] C;R)
49 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 49/81 { t u(t,η) + σ2 2 u(t, η) = D t Ddx 2 dy u(t,η) = 0 H(η) (Here D u 0).
50 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 50/81 Proof. u(t,η) = U(T,η(0)) = f(η(0)) = H(η) t U (t,η) = t U (t,η(0)) Du(t,η) = x U (t,η(0)) δ 0 D 2 u(t,η) = xxu 2 (t,η(0)) δ 0 δ 0 t u(t,η)+ 1 2 D2 u(t,η)({0,0}) = 0.
51 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 51/ The general representation The considerations of previous section bring us to the following. Theorem 12 Let us consider the following. H : C R continuous. u C 1,2 ([0,T[ C) C 0 ([0,T] C) For any (t,η) [0,T] C, [ t,0] D dx u(t,η)d η(x) is well-defined (with a technical condition). (t,η) D 2 u(t,η) is continuous from [0,T] C to χ 0.
52 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 52/81 Suppose that u solves the Infinite dimensional PDE { t u(t,η) + L t u(t,η) = 0 u(t, η) = H(η). (1)
53 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 53/81 Then, h = V 0 + T 0 (in the possibly improper sense) with V 0 = u(0,x 0 ( )) Z s = D δ 0 u(s,x s ( )) Z s d X s (2)
54 Definition 13 We call (1) path dependent Kolmogorov PDE. related to X. Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 54/81
55 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 55/81 6 Kolmogorov path dependent PDE. The window of diffusion process Let σ : [0,T] R R continuous and Lipschitz. Consider the stochastic flow (X s,ξ t ) s t T, for s [t,t],ξ R, where, setting X = X s,ξ solves the SDE X t = ξ + t s σ(r,x r )dw r, where (W,F t ) is a classical Wiener process.
56 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 56/81 Remark X is a.s. continuous in all the three variables (s, t, ξ). 2. Let σ is of class C 0,2 ( R), such that σ, x σ and 2 xxσ are Hölder continuous, with = {(s,t) 0 s t T}. Under previous assumptions we have, for k = 0,1,2, sup 0 s T E( sup (k) ξ X s,ξ t p ) M, t [s,t] for every p 1.
57 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 57/81 Associated with X, we link the following functional stochastic flow. For η C, we set { X s,η η(t s+x) : x s t t (x) = : x s t. X s,η(0) t+x (3) Remark 15 If σ = 1 we denote the corresponding window Brownian flow by W s,η t := X s,η t. From now on we will suppose σ to be as in Remark 14.
58 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 58/81 Let h = H(X T ( )), where X = X 0,x 0 T, for some x 0 R. We set V t = E(h F t ). Then, there is u : [0,T] C R such that V t = u(t,x t ( )). (4) It is given by u(t,η) = E(H(X t,η T )).
59 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 59/81 Aim. Under suitable conditions on H, we aim to show that u is the unique solution of the path dependent Kolmogorov equation (1), taking values in C( T,0]). This justifies to say that u defined in (4) is the virtual solution for (1).
60 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 60/81 Definition 16 (Strict solution) u : [0,T] C R of class C 1,2 ([0,T[ C) C 0 ([0,T] C) is said to be a solution of (1) if I(u)(t,η) := ] t,0] D dxu(t,η)d η(x) is well-defined ; (t,η) D 2 u(t,η) is continuous into some Chi-subspace χ. It solves effectively (1).
61 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 61/81 Sufficient conditions to find a strict solution of (1) and σ = 1. Case σ general in a paper in preparation (Cosso-Russo). H has a smooth Fréchet dependence on C([ T,0]). ( T h := H(X T ( )) = f ϕ 0 1(s)d X s,..., T ϕ 0 n(s)d X s ); f : R n R continuous and with linear growth; ϕ i C 2 ([0,T];R), 1 i n. ( ) 0 The determinant of Σ t := ϕ x i(y)ϕ j (y)dy bigger than zero for every x [ T,0[. is
62 Uniqueness for solutions of the Kolmogorov Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 62/81 type PDE. Let u 1,u 2 : [0,T] C R of class C 1,2 ([0,T[ C) C 0 ([0,T] C) being strict solutions of (1) of polynomial growth. If u 1,u 2 solve the Kolmogorov type equation then u 1 = u 2.
63 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 63/81 Sketch of the Proof. We fix (s,η) [0,T] C. We have to prove that u 1 (s,η) = u 2 (s,η). Without restriction of generality we set s = 0. We apply Itô formula to X t = X 0,η t. We take than the expectation and we obtain u i (0,η) = E(H(X 0,η T )). This shows the uniqueness.
64 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 64/81 7 Path-dependent semilinear Kolmogorov equation Here we concentrate on the case σ = 1, the general case being in Cosso-Russo (in preparation).. We study the path-dependent semilinear Kolmogorov equation: { t U +L t U = F(t,η,U,D δ 0 U), (t,η) [0,T[ C, where U(T,η) = G(η), η C,
65 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 65/81 G: C([ T,0]) R F: [0,T] C([ T,0]) R R R are Borel measurable functions. We refer to t U +L t U, as the path-dependent heat operator.
66 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 66/ Strict solutions Definition 17 A function U: [0,T] C([ T,0]) R in C 1,2 ([0,T[ C) C([0,T] C), which solves the path-dependent semilinear Kolmogorov equation, is called a strict solution.
67 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 67/81 Strict solutions: uniqueness Theorem 18 (Uniqueness) Let G: C([ T,0]) R and F: [0,T] C([ T,0]) R R R be Borel measurable functions satisfying, for some positive constants C and m, F(t,η,y,z) F(t,η,y,z ) C ( y y + z z ), G(η) + F(t,η,0,0) C ( 1+ η m ), for all (t,η) [0,T] C([ T,0]), y,y R, and z,z R.
68 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 68/81 Let U: [0,T] C([ T,0]) R be a strict solution to the path-dependent nonlinear Kolmogorov equation, satisfying the polynomial growth condition U(t,η) C ( 1+ η m ), (t,η) [0,T] C([ T,0]).
69 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 69/81 Then, we have U(t,η) = Y t,η t, (t,η) [0,T] C([ T,0]), where (Y t,η s,z t,η s ) s [t,t] = (U(s,W t,η s ),D δ 0 U(s,W t,η s )1 [t,t[ (s)) s [t,t] is the solution to the backward stochastic differential equation, P -a.s.,
70 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 70/81 Y t,η s = G(W t,η T )+ T s F(r,W t,η r,y t,η r,z t,η r )dr T s Z t,η r dw r, for all t s T. In particular, there exists at most one strict solution to the path-dependent nonlinear Kolmogorov equation. Here W is the functional stochastic flow associated with = x+(w t W s ) (classical Wiener process). W s,x t
71 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 71/81 Alternative methods to strict solutions for Kolmogorov path dependent PDEs Dupire-Cont-Fournié (2010). Flandoli-Zanco (2014). Leão-Ohashi-Simas (2014).
72 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 72/ Strong-viscosity solutions: introduction Various definitions of viscosity-type solutions for path-dependent PDEs have been given: (2012) S. PENG. (2013) S. TANG AND F. ZHANG. (2014) I. EKREN, C. KELLER, N. TOUZI, AND J. ZHANG. (2015) R. BUCKDAHN, J. MA AND J. ZHANG.
73 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 73/81 We propose a notion of solution which is not based on the standard definition of viscosity solution given in terms of test functions or jets. 7.3 Strong-viscosity solutions Idea and origin Our notion of solution is defined, in a few words, as the pointwise limit of strict solutions to perturbed equations.
74 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 74/81 Our definition is more similar in spirit to the concept of good solution, which turned out to be equivalent to the definition of L p -viscosity solution for certain fully nonlinear partial differential equations. Our definition is likewise inspired by the notion of strong solution, even though strong solutions are required to be more regular than simply continuous.
75 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 75/81 Definition 19 A function U: [0,T] C([ T,0]) R is called a strong-viscosity solution to the path-dependent nonlinear Kolmogorov equation if there exists a sequence (U n,g n,f n ) n satisfying: (i) U n : [0,T] C([ T,0]) R, G n : C([ T,0]) R, and F n : [0,T] C([ T,0]) R R R are equicontinuous functions such that, for some positive constants C and m, independent of n, F n (t,η,y,z) F n (t,η,y,z ) C( y y + z z ), U n (t,η) + G n (η) + F n (t,η,0,0) C ( 1+ η m ), for all (t,η) [0,T] C([ T,0]), y,y R, and z,z R.
76 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 76/81 (ii) U n is a strict solution to t U n +L t U n = F n (t,η,u n,d δ 0 U n ), (t,η) [0,T) C([ T,0]), U n (T,η) = G n (η), η C([ T,0]). (iii) (U n (t,η),g n (η),f n (t,η,y,z)) (U(t,η),G(η),F(t,η,y,z)), as n tends to infinity, for any (t,η,y,z) [0,T] C([ T,0]) R R.
77 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 77/ Strong-viscosity solutions: uniqueness Theorem 20 (Uniqueness) Let U: [0,T] C([ T,0]) R be a strong-viscosity solution to the path-dependent nonlinear Kolmogorov equation. Then, we have U(t,η) = Y t,η t, (t,η) [0,T] C([ T,0]), where (Ys t,η,zs t,η ) s [t,t], with Ys t,η = U(s,W t,η s ), solves the backward stochastic differential equation, P -a.s.,
78 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 78/81 Y t,η s = G(W t,η T )+ T s F(r,W t,η r,y t,η r,z t,η r )dr T s Z t,η r dw r, for all t s T. In particular, there exists at most one strong-viscosity solution to the path-dependent nonlinear Kolmogorov equation.
79 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 79/81 Strong-viscosity solutions: existence Theorem 21 (Existence) Let F 0 and G: C([ T,0]) R be uniformly continuous and satisfying the polynomial growth condition G(η) C(1+ η m ), η C([ T,0]), for some positive constants C and m. Then, there exists a unique strong-viscosity solution U to the path-dependent heat equation, which is given by U(t,η) = E [ G(W t,η T )], for all (t,η) [0,T] C([ T,0]).
80 Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 80/ Paper in preparation. A. Cosso, F. Russo. Existence for functional dependent Kolmogorov type equation: the strict and strong-viscosity solution case.
81 Thank you for you attention. Calculus via regularizations in Banach spaces: path dependent calculus and Kolmogorov type equations. p. 81/81
Functional vs Banach space stochastic calculus & strong-viscosity solutions to semilinear parabolic path-dependent PDEs.
Functional vs Banach space stochastic calculus & strong-viscosity solutions to semilinear parabolic path-dependent PDEs Andrea Cosso LPMA, Université Paris Diderot joint work with Francesco Russo ENSTA,
More informationReplication and Absence of Arbitrage in Non-Semimartingale Models
Replication and Absence of Arbitrage in Non-Semimartingale Models Matematiikan päivät, Tampere, 4-5. January 2006 Tommi Sottinen University of Helsinki 4.1.2006 Outline 1. The classical pricing model:
More informationStochastic Dynamical Systems and SDE s. An Informal Introduction
Stochastic Dynamical Systems and SDE s An Informal Introduction Olav Kallenberg Graduate Student Seminar, April 18, 2012 1 / 33 2 / 33 Simple recursion: Deterministic system, discrete time x n+1 = f (x
More informationNon-semimartingales in finance
Non-semimartingales in finance Pricing and Hedging Options with Quadratic Variation Tommi Sottinen University of Vaasa 1st Northern Triangular Seminar 9-11 March 2009, Helsinki University of Technology
More informationAre stylized facts irrelevant in option-pricing?
Are stylized facts irrelevant in option-pricing? Kyiv, June 19-23, 2006 Tommi Sottinen, University of Helsinki Based on a joint work No-arbitrage pricing beyond semimartingales with C. Bender, Weierstrass
More informationSPDE and portfolio choice (joint work with M. Musiela) Princeton University. Thaleia Zariphopoulou The University of Texas at Austin
SPDE and portfolio choice (joint work with M. Musiela) Princeton University November 2007 Thaleia Zariphopoulou The University of Texas at Austin 1 Performance measurement of investment strategies 2 Market
More informationEquivalence between Semimartingales and Itô Processes
International Journal of Mathematical Analysis Vol. 9, 215, no. 16, 787-791 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/1.12988/ijma.215.411358 Equivalence between Semimartingales and Itô Processes
More informationAMH4 - ADVANCED OPTION PRICING. Contents
AMH4 - ADVANCED OPTION PRICING ANDREW TULLOCH Contents 1. Theory of Option Pricing 2 2. Black-Scholes PDE Method 4 3. Martingale method 4 4. Monte Carlo methods 5 4.1. Method of antithetic variances 5
More informationAn overview of some financial models using BSDE with enlarged filtrations
An overview of some financial models using BSDE with enlarged filtrations Anne EYRAUD-LOISEL Workshop : Enlargement of Filtrations and Applications to Finance and Insurance May 31st - June 4th, 2010, Jena
More informationHedging of Contingent Claims under Incomplete Information
Projektbereich B Discussion Paper No. B 166 Hedging of Contingent Claims under Incomplete Information by Hans Föllmer ) Martin Schweizer ) October 199 ) Financial support by Deutsche Forschungsgemeinschaft,
More informationM5MF6. Advanced Methods in Derivatives Pricing
Course: Setter: M5MF6 Dr Antoine Jacquier MSc EXAMINATIONS IN MATHEMATICS AND FINANCE DEPARTMENT OF MATHEMATICS April 2016 M5MF6 Advanced Methods in Derivatives Pricing Setter s signature...........................................
More informationHow to hedge Asian options in fractional Black-Scholes model
How to hedge Asian options in fractional Black-Scholes model Heikki ikanmäki Jena, March 29, 211 Fractional Lévy processes 1/36 Outline of the talk 1. Introduction 2. Main results 3. Methodology 4. Conclusions
More informationFunctional Ito calculus. hedging of path-dependent options
and hedging of path-dependent options Laboratoire de Probabilités et Modèles Aléatoires CNRS - Université de Paris VI-VII and Columbia University, New York Background Hans Föllmer (1979) Calcul d Itô sans
More informationBACHELIER FINANCE SOCIETY. 4 th World Congress Tokyo, Investments and forward utilities. Thaleia Zariphopoulou The University of Texas at Austin
BACHELIER FINANCE SOCIETY 4 th World Congress Tokyo, 26 Investments and forward utilities Thaleia Zariphopoulou The University of Texas at Austin 1 Topics Utility-based measurement of performance Utilities
More informationA No-Arbitrage Theorem for Uncertain Stock Model
Fuzzy Optim Decis Making manuscript No (will be inserted by the editor) A No-Arbitrage Theorem for Uncertain Stock Model Kai Yao Received: date / Accepted: date Abstract Stock model is used to describe
More informationLecture 4. Finite difference and finite element methods
Finite difference and finite element methods Lecture 4 Outline Black-Scholes equation From expectation to PDE Goal: compute the value of European option with payoff g which is the conditional expectation
More informationConstructing Markov models for barrier options
Constructing Markov models for barrier options Gerard Brunick joint work with Steven Shreve Department of Mathematics University of Texas at Austin Nov. 14 th, 2009 3 rd Western Conference on Mathematical
More informationOptimal asset allocation under forward performance criteria Oberwolfach, February 2007
Optimal asset allocation under forward performance criteria Oberwolfach, February 2007 Thaleia Zariphopoulou The University of Texas at Austin 1 References Indifference valuation in binomial models (with
More informationChapter 3: Black-Scholes Equation and Its Numerical Evaluation
Chapter 3: Black-Scholes Equation and Its Numerical Evaluation 3.1 Itô Integral 3.1.1 Convergence in the Mean and Stieltjes Integral Definition 3.1 (Convergence in the Mean) A sequence {X n } n ln of random
More informationExponential utility maximization under partial information
Exponential utility maximization under partial information Marina Santacroce Politecnico di Torino Joint work with M. Mania AMaMeF 5-1 May, 28 Pitesti, May 1th, 28 Outline Expected utility maximization
More informationControl Improvement for Jump-Diffusion Processes with Applications to Finance
Control Improvement for Jump-Diffusion Processes with Applications to Finance Nicole Bäuerle joint work with Ulrich Rieder Toronto, June 2010 Outline Motivation: MDPs Controlled Jump-Diffusion Processes
More informationContinuous-time Stochastic Control and Optimization with Financial Applications
Huyen Pham Continuous-time Stochastic Control and Optimization with Financial Applications 4y Springer Some elements of stochastic analysis 1 1.1 Stochastic processes 1 1.1.1 Filtration and processes 1
More informationStochastic Partial Differential Equations and Portfolio Choice. Crete, May Thaleia Zariphopoulou
Stochastic Partial Differential Equations and Portfolio Choice Crete, May 2011 Thaleia Zariphopoulou Oxford-Man Institute and Mathematical Institute University of Oxford and Mathematics and IROM, The University
More informationIntroduction to Stochastic Calculus With Applications
Introduction to Stochastic Calculus With Applications Fima C Klebaner University of Melbourne \ Imperial College Press Contents Preliminaries From Calculus 1 1.1 Continuous and Differentiable Functions.
More informationKing s College London
King s College London University Of London This paper is part of an examination of the College counting towards the award of a degree. Examinations are governed by the College Regulations under the authority
More informationOptimal investments under dynamic performance critria. Lecture IV
Optimal investments under dynamic performance critria Lecture IV 1 Utility-based measurement of performance 2 Deterministic environment Utility traits u(x, t) : x wealth and t time Monotonicity u x (x,
More informationContinuous Time Finance. Tomas Björk
Continuous Time Finance Tomas Björk 1 II Stochastic Calculus Tomas Björk 2 Typical Setup Take as given the market price process, S(t), of some underlying asset. S(t) = price, at t, per unit of underlying
More informationConstructive martingale representation using Functional Itô Calculus: a local martingale extension
Mathematical Statistics Stockholm University Constructive martingale representation using Functional Itô Calculus: a local martingale extension Kristoffer Lindensjö Research Report 216:21 ISSN 165-377
More information1.1 Basic Financial Derivatives: Forward Contracts and Options
Chapter 1 Preliminaries 1.1 Basic Financial Derivatives: Forward Contracts and Options A derivative is a financial instrument whose value depends on the values of other, more basic underlying variables
More informationAn Introduction to Point Processes. from a. Martingale Point of View
An Introduction to Point Processes from a Martingale Point of View Tomas Björk KTH, 211 Preliminary, incomplete, and probably with lots of typos 2 Contents I The Mathematics of Counting Processes 5 1 Counting
More informationMartingale Transport, Skorokhod Embedding and Peacocks
Martingale Transport, Skorokhod Embedding and CEREMADE, Université Paris Dauphine Collaboration with Pierre Henry-Labordère, Nizar Touzi 08 July, 2014 Second young researchers meeting on BSDEs, Numerics
More informationABOUT THE PRICING EQUATION IN FINANCE
ABOUT THE PRICING EQUATION IN FINANCE Stéphane CRÉPEY University of Evry, France stephane.crepey@univ-evry.fr AMAMEF at Vienna University of Technology 17 22 September 2007 1 We derive the pricing equation
More informationRohini Kumar. Statistics and Applied Probability, UCSB (Joint work with J. Feng and J.-P. Fouque)
Small time asymptotics for fast mean-reverting stochastic volatility models Statistics and Applied Probability, UCSB (Joint work with J. Feng and J.-P. Fouque) March 11, 2011 Frontier Probability Days,
More informationLecture 11: Ito Calculus. Tuesday, October 23, 12
Lecture 11: Ito Calculus Continuous time models We start with the model from Chapter 3 log S j log S j 1 = µ t + p tz j Sum it over j: log S N log S 0 = NX µ t + NX p tzj j=1 j=1 Can we take the limit
More informationA NEW NOTION OF TRANSITIVE RELATIVE RETURN RATE AND ITS APPLICATIONS USING STOCHASTIC DIFFERENTIAL EQUATIONS. Burhaneddin İZGİ
A NEW NOTION OF TRANSITIVE RELATIVE RETURN RATE AND ITS APPLICATIONS USING STOCHASTIC DIFFERENTIAL EQUATIONS Burhaneddin İZGİ Department of Mathematics, Istanbul Technical University, Istanbul, Turkey
More informationDrunken Birds, Brownian Motion, and Other Random Fun
Drunken Birds, Brownian Motion, and Other Random Fun Michael Perlmutter Department of Mathematics Purdue University 1 M. Perlmutter(Purdue) Brownian Motion and Martingales Outline Review of Basic Probability
More informationBROWNIAN MOTION Antonella Basso, Martina Nardon
BROWNIAN MOTION Antonella Basso, Martina Nardon basso@unive.it, mnardon@unive.it Department of Applied Mathematics University Ca Foscari Venice Brownian motion p. 1 Brownian motion Brownian motion plays
More informationVolatility Smiles and Yield Frowns
Volatility Smiles and Yield Frowns Peter Carr NYU CBOE Conference on Derivatives and Volatility, Chicago, Nov. 10, 2017 Peter Carr (NYU) Volatility Smiles and Yield Frowns 11/10/2017 1 / 33 Interest Rates
More informationStochastic Control and Algorithmic Trading
Stochastic Control and Algorithmic Trading Nicholas Westray (nicholas.westray@db.com) Deutsche Bank and Imperial College RiO - Research in Options Rio de Janeiro - 26 th November 2011 What is Stochastic
More informationLocal vs Non-local Forward Equations for Option Pricing
Local vs Non-local Forward Equations for Option Pricing Rama Cont Yu Gu Abstract When the underlying asset is a continuous martingale, call option prices solve the Dupire equation, a forward parabolic
More informationMartingale Approach to Pricing and Hedging
Introduction and echniques Lecture 9 in Financial Mathematics UiO-SK451 Autumn 15 eacher:s. Ortiz-Latorre Martingale Approach to Pricing and Hedging 1 Risk Neutral Pricing Assume that we are in the basic
More informationForward Dynamic Utility
Forward Dynamic Utility El Karoui Nicole & M RAD Mohamed UnivParis VI / École Polytechnique,CMAP elkaroui@cmapx.polytechnique.fr with the financial support of the "Fondation du Risque" and the Fédération
More informationOptimal robust bounds for variance options and asymptotically extreme models
Optimal robust bounds for variance options and asymptotically extreme models Alexander Cox 1 Jiajie Wang 2 1 University of Bath 2 Università di Roma La Sapienza Advances in Financial Mathematics, 9th January,
More informationArbitrage of the first kind and filtration enlargements in semimartingale financial models. Beatrice Acciaio
Arbitrage of the first kind and filtration enlargements in semimartingale financial models Beatrice Acciaio the London School of Economics and Political Science (based on a joint work with C. Fontana and
More informationPDE Methods for the Maximum Drawdown
PDE Methods for the Maximum Drawdown Libor Pospisil, Jan Vecer Columbia University, Department of Statistics, New York, NY 127, USA April 1, 28 Abstract Maximum drawdown is a risk measure that plays an
More informationThe stochastic calculus
Gdansk A schedule of the lecture Stochastic differential equations Ito calculus, Ito process Ornstein - Uhlenbeck (OU) process Heston model Stopping time for OU process Stochastic differential equations
More informationDr. Maddah ENMG 625 Financial Eng g II 10/16/06
Dr. Maddah ENMG 65 Financial Eng g II 10/16/06 Chapter 11 Models of Asset Dynamics () Random Walk A random process, z, is an additive process defined over times t 0, t 1,, t k, t k+1,, such that z( t )
More informationarxiv: v2 [q-fin.pr] 23 Nov 2017
VALUATION OF EQUITY WARRANTS FOR UNCERTAIN FINANCIAL MARKET FOAD SHOKROLLAHI arxiv:17118356v2 [q-finpr] 23 Nov 217 Department of Mathematics and Statistics, University of Vaasa, PO Box 7, FIN-6511 Vaasa,
More informationVolatility Smiles and Yield Frowns
Volatility Smiles and Yield Frowns Peter Carr NYU IFS, Chengdu, China, July 30, 2018 Peter Carr (NYU) Volatility Smiles and Yield Frowns 7/30/2018 1 / 35 Interest Rates and Volatility Practitioners and
More informationPATH-PEPENDENT PARABOLIC PDES AND PATH-DEPENDENT FEYNMAN-KAC FORMULA
PATH-PEPENDENT PARABOLIC PDES AND PATH-DEPENDENT FEYNMAN-KAC FORMULA CNRS, CMAP Ecole Polytechnique Bachelier Paris, january 8 2016 Dynamic Risk Measures and Path-Dependent second order PDEs, SEFE, Fred
More informationThe discounted portfolio value of a selffinancing strategy in discrete time was given by. δ tj 1 (s tj s tj 1 ) (9.1) j=1
Chapter 9 The isk Neutral Pricing Measure for the Black-Scholes Model The discounted portfolio value of a selffinancing strategy in discrete time was given by v tk = v 0 + k δ tj (s tj s tj ) (9.) where
More informationHedging under arbitrage
Hedging under arbitrage Johannes Ruf Columbia University, Department of Statistics AnStAp10 August 12, 2010 Motivation Usually, there are several trading strategies at one s disposal to obtain a given
More informationAmerican Option Pricing Formula for Uncertain Financial Market
American Option Pricing Formula for Uncertain Financial Market Xiaowei Chen Uncertainty Theory Laboratory, Department of Mathematical Sciences Tsinghua University, Beijing 184, China chenxw7@mailstsinghuaeducn
More informationMinimal Variance Hedging in Large Financial Markets: random fields approach
Minimal Variance Hedging in Large Financial Markets: random fields approach Giulia Di Nunno Third AMaMeF Conference: Advances in Mathematical Finance Pitesti, May 5-1 28 based on a work in progress with
More informationHedging under Arbitrage
Hedging under Arbitrage Johannes Ruf Columbia University, Department of Statistics Modeling and Managing Financial Risks January 12, 2011 Motivation Given: a frictionless market of stocks with continuous
More informationOption Pricing Formula for Fuzzy Financial Market
Journal of Uncertain Systems Vol.2, No., pp.7-2, 28 Online at: www.jus.org.uk Option Pricing Formula for Fuzzy Financial Market Zhongfeng Qin, Xiang Li Department of Mathematical Sciences Tsinghua University,
More informationVolatility. Roberto Renò. 2 March 2010 / Scuola Normale Superiore. Dipartimento di Economia Politica Università di Siena
Dipartimento di Economia Politica Università di Siena 2 March 2010 / Scuola Normale Superiore What is? The definition of volatility may vary wildly around the idea of the standard deviation of price movements
More informationMath 416/516: Stochastic Simulation
Math 416/516: Stochastic Simulation Haijun Li lih@math.wsu.edu Department of Mathematics Washington State University Week 13 Haijun Li Math 416/516: Stochastic Simulation Week 13 1 / 28 Outline 1 Simulation
More informationLocal Volatility Dynamic Models
René Carmona Bendheim Center for Finance Department of Operations Research & Financial Engineering Princeton University Columbia November 9, 27 Contents Joint work with Sergey Nadtochyi Motivation 1 Understanding
More informationModel-independent bounds for Asian options
Model-independent bounds for Asian options A dynamic programming approach Alexander M. G. Cox 1 Sigrid Källblad 2 1 University of Bath 2 CMAP, École Polytechnique University of Michigan, 2nd December,
More informationA new approach for scenario generation in risk management
A new approach for scenario generation in risk management Josef Teichmann TU Wien Vienna, March 2009 Scenario generators Scenarios of risk factors are needed for the daily risk analysis (1D and 10D ahead)
More informationMASM006 UNIVERSITY OF EXETER SCHOOL OF ENGINEERING, COMPUTER SCIENCE AND MATHEMATICS MATHEMATICAL SCIENCES FINANCIAL MATHEMATICS.
MASM006 UNIVERSITY OF EXETER SCHOOL OF ENGINEERING, COMPUTER SCIENCE AND MATHEMATICS MATHEMATICAL SCIENCES FINANCIAL MATHEMATICS May/June 2006 Time allowed: 2 HOURS. Examiner: Dr N.P. Byott This is a CLOSED
More informationIntroduction to Probability Theory and Stochastic Processes for Finance Lecture Notes
Introduction to Probability Theory and Stochastic Processes for Finance Lecture Notes Fabio Trojani Department of Economics, University of St. Gallen, Switzerland Correspondence address: Fabio Trojani,
More informationParameter sensitivity of CIR process
Parameter sensitivity of CIR process Sidi Mohamed Ould Aly To cite this version: Sidi Mohamed Ould Aly. Parameter sensitivity of CIR process. Electronic Communications in Probability, Institute of Mathematical
More informationFrom Discrete Time to Continuous Time Modeling
From Discrete Time to Continuous Time Modeling Prof. S. Jaimungal, Department of Statistics, University of Toronto 2004 Arrow-Debreu Securities 2004 Prof. S. Jaimungal 2 Consider a simple one-period economy
More informationGreek parameters of nonlinear Black-Scholes equation
International Journal of Mathematics and Soft Computing Vol.5, No.2 (2015), 69-74. ISSN Print : 2249-3328 ISSN Online: 2319-5215 Greek parameters of nonlinear Black-Scholes equation Purity J. Kiptum 1,
More informationLimit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies
Limit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies George Tauchen Duke University Viktor Todorov Northwestern University 2013 Motivation
More informationHow to hedge Asian options in fractional Black-Scholes model
How to hedge Asian options in fractional Black-Scholes model Heikki ikanmäki St. Petersburg, April 12, 211 Fractional Lévy processes 1/26 Outline of the talk 1. Introduction 2. Main results 3. Conclusions
More informationStochastic Integral Representation of One Stochastically Non-smooth Wiener Functional
Bulletin of TICMI Vol. 2, No. 2, 26, 24 36 Stochastic Integral Representation of One Stochastically Non-smooth Wiener Functional Hanna Livinska a and Omar Purtukhia b a Taras Shevchenko National University
More informationSTOCHASTIC CALCULUS AND BLACK-SCHOLES MODEL
STOCHASTIC CALCULUS AND BLACK-SCHOLES MODEL YOUNGGEUN YOO Abstract. Ito s lemma is often used in Ito calculus to find the differentials of a stochastic process that depends on time. This paper will introduce
More informationLast Time. Martingale inequalities Martingale convergence theorem Uniformly integrable martingales. Today s lecture: Sections 4.4.1, 5.
MATH136/STAT219 Lecture 21, November 12, 2008 p. 1/11 Last Time Martingale inequalities Martingale convergence theorem Uniformly integrable martingales Today s lecture: Sections 4.4.1, 5.3 MATH136/STAT219
More informationParameters Estimation in Stochastic Process Model
Parameters Estimation in Stochastic Process Model A Quasi-Likelihood Approach Ziliang Li University of Maryland, College Park GEE RIT, Spring 28 Outline 1 Model Review The Big Model in Mind: Signal + Noise
More informationEnlargement of filtration
Enlargement of filtration Bernardo D Auria email: bernardo.dauria@uc3m.es web: www.est.uc3m.es/bdauria July 6, 2017 ICMAT / UC3M Enlargement of Filtration Enlargement of Filtration ([1] 5.9) If G is a
More informationFractional Brownian Motion as a Model in Finance
Fractional Brownian Motion as a Model in Finance Tommi Sottinen, University of Helsinki Esko Valkeila, University of Turku and University of Helsinki 1 Black & Scholes pricing model In the classical Black
More informationConditional Full Support and No Arbitrage
Gen. Math. Notes, Vol. 32, No. 2, February 216, pp.54-64 ISSN 2219-7184; Copyright c ICSRS Publication, 216 www.i-csrs.org Available free online at http://www.geman.in Conditional Full Support and No Arbitrage
More information13.3 A Stochastic Production Planning Model
13.3. A Stochastic Production Planning Model 347 From (13.9), we can formally write (dx t ) = f (dt) + G (dz t ) + fgdz t dt, (13.3) dx t dt = f(dt) + Gdz t dt. (13.33) The exact meaning of these expressions
More informationLogarithmic derivatives of densities for jump processes
Logarithmic derivatives of densities for jump processes Atsushi AKEUCHI Osaka City University (JAPAN) June 3, 29 City University of Hong Kong Workshop on Stochastic Analysis and Finance (June 29 - July
More informationBasic Arbitrage Theory KTH Tomas Björk
Basic Arbitrage Theory KTH 2010 Tomas Björk Tomas Björk, 2010 Contents 1. Mathematics recap. (Ch 10-12) 2. Recap of the martingale approach. (Ch 10-12) 3. Change of numeraire. (Ch 26) Björk,T. Arbitrage
More information1 Mathematics in a Pill 1.1 PROBABILITY SPACE AND RANDOM VARIABLES. A probability triple P consists of the following components:
1 Mathematics in a Pill The purpose of this chapter is to give a brief outline of the probability theory underlying the mathematics inside the book, and to introduce necessary notation and conventions
More informationFundamentals of Stochastic Filtering
Alan Bain Dan Crisan Fundamentals of Stochastic Filtering Sprin ger Contents Preface Notation v xi 1 Introduction 1 1.1 Foreword 1 1.2 The Contents of the Book 3 1.3 Historical Account 5 Part I Filtering
More informationPortfolio optimization problem with default risk
Portfolio optimization problem with default risk M.Mazidi, A. Delavarkhalafi, A.Mokhtari mazidi.3635@gmail.com delavarkh@yazduni.ac.ir ahmokhtari20@gmail.com Faculty of Mathematics, Yazd University, P.O.
More informationPAPER 27 STOCHASTIC CALCULUS AND APPLICATIONS
MATHEMATICAL TRIPOS Part III Thursday, 5 June, 214 1:3 pm to 4:3 pm PAPER 27 STOCHASTIC CALCULUS AND APPLICATIONS Attempt no more than FOUR questions. There are SIX questions in total. The questions carry
More informationStochastic Calculus, Application of Real Analysis in Finance
, Application of Real Analysis in Finance Workshop for Young Mathematicians in Korea Seungkyu Lee Pohang University of Science and Technology August 4th, 2010 Contents 1 BINOMIAL ASSET PRICING MODEL Contents
More informationLecture 3: Review of mathematical finance and derivative pricing models
Lecture 3: Review of mathematical finance and derivative pricing models Xiaoguang Wang STAT 598W January 21th, 2014 (STAT 598W) Lecture 3 1 / 51 Outline 1 Some model independent definitions and principals
More informationSTOCHASTIC INTEGRALS
Stat 391/FinMath 346 Lecture 8 STOCHASTIC INTEGRALS X t = CONTINUOUS PROCESS θ t = PORTFOLIO: #X t HELD AT t { St : STOCK PRICE M t : MG W t : BROWNIAN MOTION DISCRETE TIME: = t < t 1
More informationD MATH Departement of Mathematics Finite dimensional realizations for the CNKK-volatility surface model
Finite dimensional realizations for the CNKK-volatility surface model Josef Teichmann Outline 1 Introduction 2 The (generalized) CNKK-approach 3 Affine processes as generic example for the CNNK-approach
More informationIntroduction to Affine Processes. Applications to Mathematical Finance
and Its Applications to Mathematical Finance Department of Mathematical Science, KAIST Workshop for Young Mathematicians in Korea, 2010 Outline Motivation 1 Motivation 2 Preliminary : Stochastic Calculus
More information"Pricing Exotic Options using Strong Convergence Properties
Fourth Oxford / Princeton Workshop on Financial Mathematics "Pricing Exotic Options using Strong Convergence Properties Klaus E. Schmitz Abe schmitz@maths.ox.ac.uk www.maths.ox.ac.uk/~schmitz Prof. Mike
More informationThe Black-Scholes Equation using Heat Equation
The Black-Scholes Equation using Heat Equation Peter Cassar May 0, 05 Assumptions of the Black-Scholes Model We have a risk free asset given by the price process, dbt = rbt The asset price follows a geometric
More informationStructural Models of Credit Risk and Some Applications
Structural Models of Credit Risk and Some Applications Albert Cohen Actuarial Science Program Department of Mathematics Department of Statistics and Probability albert@math.msu.edu August 29, 2018 Outline
More informationValuation of derivative assets Lecture 6
Valuation of derivative assets Lecture 6 Magnus Wiktorsson September 14, 2017 Magnus Wiktorsson L6 September 14, 2017 1 / 13 Feynman-Kac representation This is the link between a class of Partial Differential
More informationOPTION PRICE WHEN THE STOCK IS A SEMIMARTINGALE
DOI: 1.1214/ECP.v7-149 Elect. Comm. in Probab. 7 (22) 79 83 ELECTRONIC COMMUNICATIONS in PROBABILITY OPTION PRICE WHEN THE STOCK IS A SEMIMARTINGALE FIMA KLEBANER Department of Mathematics & Statistics,
More informationKing s College London
King s College London University Of London This paper is part of an examination of the College counting towards the award of a degree. Examinations are governed by the College Regulations under the authority
More informationI Preliminary Material 1
Contents Preface Notation xvii xxiii I Preliminary Material 1 1 From Diffusions to Semimartingales 3 1.1 Diffusions.......................... 5 1.1.1 The Brownian Motion............... 5 1.1.2 Stochastic
More informationIntroduction to Stochastic Calculus
Introduction to Stochastic Calculus Director Chennai Mathematical Institute rlk@cmi.ac.in rkarandikar@gmail.com Introduction to Stochastic Calculus - 1 The notion of Conditional Expectation of a random
More informationSample Path Large Deviations and Optimal Importance Sampling for Stochastic Volatility Models
Sample Path Large Deviations and Optimal Importance Sampling for Stochastic Volatility Models Scott Robertson Carnegie Mellon University scottrob@andrew.cmu.edu http://www.math.cmu.edu/users/scottrob June
More informationModel-independent bounds for Asian options
Model-independent bounds for Asian options A dynamic programming approach Alexander M. G. Cox 1 Sigrid Källblad 2 1 University of Bath 2 CMAP, École Polytechnique 7th General AMaMeF and Swissquote Conference
More informationNo-arbitrage theorem for multi-factor uncertain stock model with floating interest rate
Fuzzy Optim Decis Making 217 16:221 234 DOI 117/s17-16-9246-8 No-arbitrage theorem for multi-factor uncertain stock model with floating interest rate Xiaoyu Ji 1 Hua Ke 2 Published online: 17 May 216 Springer
More informationThe British Binary Option
The British Binary Option Min Gao First version: 7 October 215 Research Report No. 9, 215, Probability and Statistics Group School of Mathematics, The University of Manchester The British Binary Option
More informationConvergence Analysis of Monte Carlo Calibration of Financial Market Models
Analysis of Monte Carlo Calibration of Financial Market Models Christoph Käbe Universität Trier Workshop on PDE Constrained Optimization of Certain and Uncertain Processes June 03, 2009 Monte Carlo Calibration
More information