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Koc2(dba)

1. 1. Lectures on Lévy Processes and Stochastic Calculus (Koc University) Lecture 2: Lévy Processes David Applebaum School of Mathematics and Statistics, University of Shefﬁeld, UK 6th December 2011Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 1 / 56
2. 2. Deﬁnition: Lévy ProcessLet X = (X (t), t ≥ 0) be a stochastic process deﬁned on a probabilityspace (Ω, F, P).We say that it has independent increments if for each n ∈ N and each 0 ≤ t1 < t2 < · · · < tn+1 < ∞, the random variables (X (tj+1 ) − X (tj ), 1 ≤ j ≤ n) are independent and it has stationary increments if each d X (tj+1 ) − X (tj ) = X (tj+1 − tj ) − X (0). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 2 / 56
3. 3. Deﬁnition: Lévy ProcessLet X = (X (t), t ≥ 0) be a stochastic process deﬁned on a probabilityspace (Ω, F, P).We say that it has independent increments if for each n ∈ N and each 0 ≤ t1 < t2 < · · · < tn+1 < ∞, the random variables (X (tj+1 ) − X (tj ), 1 ≤ j ≤ n) are independent and it has stationary increments if each d X (tj+1 ) − X (tj ) = X (tj+1 − tj ) − X (0). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 2 / 56
4. 4. Deﬁnition: Lévy ProcessLet X = (X (t), t ≥ 0) be a stochastic process deﬁned on a probabilityspace (Ω, F, P).We say that it has independent increments if for each n ∈ N and each 0 ≤ t1 < t2 < · · · < tn+1 < ∞, the random variables (X (tj+1 ) − X (tj ), 1 ≤ j ≤ n) are independent and it has stationary increments if each d X (tj+1 ) − X (tj ) = X (tj+1 − tj ) − X (0). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 2 / 56
5. 5. We say that X is a Lévy process if (L1) Each X (0) = 0 (a.s), (L2) X has independent and stationary increments,(L3) X is stochastically continuous i.e. for all a > 0 and for all s ≥ 0, lim P(|X (t) − X (s)| > a) = 0. t→s Note that in the presence of (L1) and (L2), (L3) is equivalent to the condition lim P(|X (t)| > a) = 0. t↓0Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 3 / 56
6. 6. We say that X is a Lévy process if (L1) Each X (0) = 0 (a.s), (L2) X has independent and stationary increments,(L3) X is stochastically continuous i.e. for all a > 0 and for all s ≥ 0, lim P(|X (t) − X (s)| > a) = 0. t→s Note that in the presence of (L1) and (L2), (L3) is equivalent to the condition lim P(|X (t)| > a) = 0. t↓0Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 3 / 56
7. 7. We say that X is a Lévy process if (L1) Each X (0) = 0 (a.s), (L2) X has independent and stationary increments,(L3) X is stochastically continuous i.e. for all a > 0 and for all s ≥ 0, lim P(|X (t) − X (s)| > a) = 0. t→s Note that in the presence of (L1) and (L2), (L3) is equivalent to the condition lim P(|X (t)| > a) = 0. t↓0Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 3 / 56
8. 8. We say that X is a Lévy process if (L1) Each X (0) = 0 (a.s), (L2) X has independent and stationary increments,(L3) X is stochastically continuous i.e. for all a > 0 and for all s ≥ 0, lim P(|X (t) − X (s)| > a) = 0. t→s Note that in the presence of (L1) and (L2), (L3) is equivalent to the condition lim P(|X (t)| > a) = 0. t↓0Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 3 / 56
9. 9. The sample paths of a process are the maps t → X (t)(ω) from R+ toRd , for each ω ∈ Ω.We are now going to explore the relationship between Lévy processesand inﬁnite divisibility.TheoremIf X is a Lévy process, then X (t) is inﬁnitely divisible for each t ≥ 0. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 4 / 56
10. 10. The sample paths of a process are the maps t → X (t)(ω) from R+ toRd , for each ω ∈ Ω.We are now going to explore the relationship between Lévy processesand inﬁnite divisibility.TheoremIf X is a Lévy process, then X (t) is inﬁnitely divisible for each t ≥ 0. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 4 / 56
11. 11. The sample paths of a process are the maps t → X (t)(ω) from R+ toRd , for each ω ∈ Ω.We are now going to explore the relationship between Lévy processesand inﬁnite divisibility.TheoremIf X is a Lévy process, then X (t) is inﬁnitely divisible for each t ≥ 0. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 4 / 56
12. 12. Proof. For each n ∈ N, we can write (n) (n) X (t) = Y1 (t) + · · · + Yn (t)where each Yk (t) = X ( kt ) − X ( (k −1)t ). (n) n n (n)The Yk (t)’s are i.i.d. by (L2). 2From Lecture 1 we can write φX (t) (u) = eη(t,u) for each t ≥ 0, u ∈ Rd ,where each η(t, ·) is a Lévy symbol. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 5 / 56
13. 13. Proof. For each n ∈ N, we can write (n) (n) X (t) = Y1 (t) + · · · + Yn (t)where each Yk (t) = X ( kt ) − X ( (k −1)t ). (n) n n (n)The Yk (t)’s are i.i.d. by (L2). 2From Lecture 1 we can write φX (t) (u) = eη(t,u) for each t ≥ 0, u ∈ Rd ,where each η(t, ·) is a Lévy symbol. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 5 / 56
14. 14. Proof. For each n ∈ N, we can write (n) (n) X (t) = Y1 (t) + · · · + Yn (t)where each Yk (t) = X ( kt ) − X ( (k −1)t ). (n) n n (n)The Yk (t)’s are i.i.d. by (L2). 2From Lecture 1 we can write φX (t) (u) = eη(t,u) for each t ≥ 0, u ∈ Rd ,where each η(t, ·) is a Lévy symbol. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 5 / 56
15. 15. TheoremIf X is a Lévy process, then φX (t) (u) = etη(u) ,for each u ∈ Rd , t ≥ 0, where η is the Lévy symbol of X (1).Proof. Suppose that X is a Lévy process and for each u ∈ Rd , t ≥ 0,deﬁne φu (t) = φX (t) (u)then by (L2) we have for all s ≥ 0, φu (t + s) = E(ei(u,X (t+s)) ) = E(ei(u,X (t+s)−X (s)) ei(u,X (s)) ) = E(ei(u,X (t+s)−X (s)) )E(ei(u,X (s)) ) = φu (t)φu (s) . . . (i) Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 6 / 56
16. 16. TheoremIf X is a Lévy process, then φX (t) (u) = etη(u) ,for each u ∈ Rd , t ≥ 0, where η is the Lévy symbol of X (1).Proof. Suppose that X is a Lévy process and for each u ∈ Rd , t ≥ 0,deﬁne φu (t) = φX (t) (u)then by (L2) we have for all s ≥ 0, φu (t + s) = E(ei(u,X (t+s)) ) = E(ei(u,X (t+s)−X (s)) ei(u,X (s)) ) = E(ei(u,X (t+s)−X (s)) )E(ei(u,X (s)) ) = φu (t)φu (s) . . . (i) Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 6 / 56
17. 17. TheoremIf X is a Lévy process, then φX (t) (u) = etη(u) ,for each u ∈ Rd , t ≥ 0, where η is the Lévy symbol of X (1).Proof. Suppose that X is a Lévy process and for each u ∈ Rd , t ≥ 0,deﬁne φu (t) = φX (t) (u)then by (L2) we have for all s ≥ 0, φu (t + s) = E(ei(u,X (t+s)) ) = E(ei(u,X (t+s)−X (s)) ei(u,X (s)) ) = E(ei(u,X (t+s)−X (s)) )E(ei(u,X (s)) ) = φu (t)φu (s) . . . (i) Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 6 / 56
18. 18. TheoremIf X is a Lévy process, then φX (t) (u) = etη(u) ,for each u ∈ Rd , t ≥ 0, where η is the Lévy symbol of X (1).Proof. Suppose that X is a Lévy process and for each u ∈ Rd , t ≥ 0,deﬁne φu (t) = φX (t) (u)then by (L2) we have for all s ≥ 0, φu (t + s) = E(ei(u,X (t+s)) ) = E(ei(u,X (t+s)−X (s)) ei(u,X (s)) ) = E(ei(u,X (t+s)−X (s)) )E(ei(u,X (s)) ) = φu (t)φu (s) . . . (i) Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 6 / 56
19. 19. TheoremIf X is a Lévy process, then φX (t) (u) = etη(u) ,for each u ∈ Rd , t ≥ 0, where η is the Lévy symbol of X (1).Proof. Suppose that X is a Lévy process and for each u ∈ Rd , t ≥ 0,deﬁne φu (t) = φX (t) (u)then by (L2) we have for all s ≥ 0, φu (t + s) = E(ei(u,X (t+s)) ) = E(ei(u,X (t+s)−X (s)) ei(u,X (s)) ) = E(ei(u,X (t+s)−X (s)) )E(ei(u,X (s)) ) = φu (t)φu (s) . . . (i) Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 6 / 56
20. 20. TheoremIf X is a Lévy process, then φX (t) (u) = etη(u) ,for each u ∈ Rd , t ≥ 0, where η is the Lévy symbol of X (1).Proof. Suppose that X is a Lévy process and for each u ∈ Rd , t ≥ 0,deﬁne φu (t) = φX (t) (u)then by (L2) we have for all s ≥ 0, φu (t + s) = E(ei(u,X (t+s)) ) = E(ei(u,X (t+s)−X (s)) ei(u,X (s)) ) = E(ei(u,X (t+s)−X (s)) )E(ei(u,X (s)) ) = φu (t)φu (s) . . . (i) Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 6 / 56
21. 21. xNow φu (0) = 1 . . . (ii) by (L1), and the map t → φu (t) is continuous.However the unique continuous solution to (i) and (ii) is given byφu (t) = etα(u) , where α : Rd → C. Now by Theorem 1, X (1) is inﬁnitelydivisible, hence α is a Lévy symbol and the result follows.2 Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 7 / 56
22. 22. xNow φu (0) = 1 . . . (ii) by (L1), and the map t → φu (t) is continuous.However the unique continuous solution to (i) and (ii) is given byφu (t) = etα(u) , where α : Rd → C. Now by Theorem 1, X (1) is inﬁnitelydivisible, hence α is a Lévy symbol and the result follows.2 Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 7 / 56
23. 23. xNow φu (0) = 1 . . . (ii) by (L1), and the map t → φu (t) is continuous.However the unique continuous solution to (i) and (ii) is given byφu (t) = etα(u) , where α : Rd → C. Now by Theorem 1, X (1) is inﬁnitelydivisible, hence α is a Lévy symbol and the result follows.2 Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 7 / 56
24. 24. xNow φu (0) = 1 . . . (ii) by (L1), and the map t → φu (t) is continuous.However the unique continuous solution to (i) and (ii) is given byφu (t) = etα(u) , where α : Rd → C. Now by Theorem 1, X (1) is inﬁnitelydivisible, hence α is a Lévy symbol and the result follows.2 Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 7 / 56
25. 25. xNow φu (0) = 1 . . . (ii) by (L1), and the map t → φu (t) is continuous.However the unique continuous solution to (i) and (ii) is given byφu (t) = etα(u) , where α : Rd → C. Now by Theorem 1, X (1) is inﬁnitelydivisible, hence α is a Lévy symbol and the result follows.2 Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 7 / 56
26. 26. We now have the Lévy-Khinchine formula for a Lévyprocess X = (X (t), t ≥ 0):- 1 E(ei(u,X (t)) ) = exp{ t i(b, u) − (u, Au) 2 + (ei(u,y ) − 1 − i(u, y )1B (y ))ν(dy ) ˆ },(2.1) Rd −{0}for each t ≥ 0, u ∈ Rd , where (b, A, ν) are the characteristics of X (1).We will deﬁne the Lévy symbol and the characteristics of a Lévyprocess X to be those of the random variable X (1). We will sometimeswrite the former as ηX when we want to emphasise that it belongs tothe process X . Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 8 / 56
27. 27. We now have the Lévy-Khinchine formula for a Lévyprocess X = (X (t), t ≥ 0):- 1 E(ei(u,X (t)) ) = exp{ t i(b, u) − (u, Au) 2 + (ei(u,y ) − 1 − i(u, y )1B (y ))ν(dy ) ˆ },(2.1) Rd −{0}for each t ≥ 0, u ∈ Rd , where (b, A, ν) are the characteristics of X (1).We will deﬁne the Lévy symbol and the characteristics of a Lévyprocess X to be those of the random variable X (1). We will sometimeswrite the former as ηX when we want to emphasise that it belongs tothe process X . Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 8 / 56
28. 28. We now have the Lévy-Khinchine formula for a Lévyprocess X = (X (t), t ≥ 0):- 1 E(ei(u,X (t)) ) = exp{ t i(b, u) − (u, Au) 2 + (ei(u,y ) − 1 − i(u, y )1B (y ))ν(dy ) ˆ },(2.1) Rd −{0}for each t ≥ 0, u ∈ Rd , where (b, A, ν) are the characteristics of X (1).We will deﬁne the Lévy symbol and the characteristics of a Lévyprocess X to be those of the random variable X (1). We will sometimeswrite the former as ηX when we want to emphasise that it belongs tothe process X . Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 8 / 56
29. 29. Let pt be the law of X (t), for each t ≥ 0. By (L2), we have for alls, t ≥ 0 that: pt+s = pt ∗ ps . wBy (L3), we have pt → δ0 as t → 0, i.e. limt→0 f (x)pt (dx) = f (0).(pt , t ≥ 0) is a weakly continuous convolution semigroup of probabilitymeasures on Rd .Conversely, given any such semigroup, we can always construct aLévy process on path space via Kolmogorov’s construction. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 9 / 56
30. 30. Let pt be the law of X (t), for each t ≥ 0. By (L2), we have for alls, t ≥ 0 that: pt+s = pt ∗ ps . wBy (L3), we have pt → δ0 as t → 0, i.e. limt→0 f (x)pt (dx) = f (0).(pt , t ≥ 0) is a weakly continuous convolution semigroup of probabilitymeasures on Rd .Conversely, given any such semigroup, we can always construct aLévy process on path space via Kolmogorov’s construction. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 9 / 56
31. 31. Let pt be the law of X (t), for each t ≥ 0. By (L2), we have for alls, t ≥ 0 that: pt+s = pt ∗ ps . wBy (L3), we have pt → δ0 as t → 0, i.e. limt→0 f (x)pt (dx) = f (0).(pt , t ≥ 0) is a weakly continuous convolution semigroup of probabilitymeasures on Rd .Conversely, given any such semigroup, we can always construct aLévy process on path space via Kolmogorov’s construction. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 9 / 56
32. 32. Let pt be the law of X (t), for each t ≥ 0. By (L2), we have for alls, t ≥ 0 that: pt+s = pt ∗ ps . wBy (L3), we have pt → δ0 as t → 0, i.e. limt→0 f (x)pt (dx) = f (0).(pt , t ≥ 0) is a weakly continuous convolution semigroup of probabilitymeasures on Rd .Conversely, given any such semigroup, we can always construct aLévy process on path space via Kolmogorov’s construction. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 9 / 56
33. 33. Let pt be the law of X (t), for each t ≥ 0. By (L2), we have for alls, t ≥ 0 that: pt+s = pt ∗ ps . wBy (L3), we have pt → δ0 as t → 0, i.e. limt→0 f (x)pt (dx) = f (0).(pt , t ≥ 0) is a weakly continuous convolution semigroup of probabilitymeasures on Rd .Conversely, given any such semigroup, we can always construct aLévy process on path space via Kolmogorov’s construction. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 9 / 56
34. 34. Informally, we have the following asymptotic relationship between thelaw of a Lévy process and its Lévy measure: pt ν = lim . t↓0 tMore precisely 1 lim f (x)pt (dx) = f (x)ν(dx), (2.2) t↓0 t Rd Rdfor bounded, continuous functions f which vanish in someneighborhood of the origin. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 10 / 56
35. 35. Informally, we have the following asymptotic relationship between thelaw of a Lévy process and its Lévy measure: pt ν = lim . t↓0 tMore precisely 1 lim f (x)pt (dx) = f (x)ν(dx), (2.2) t↓0 t Rd Rdfor bounded, continuous functions f which vanish in someneighborhood of the origin. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 10 / 56
36. 36. Informally, we have the following asymptotic relationship between thelaw of a Lévy process and its Lévy measure: pt ν = lim . t↓0 tMore precisely 1 lim f (x)pt (dx) = f (x)ν(dx), (2.2) t↓0 t Rd Rdfor bounded, continuous functions f which vanish in someneighborhood of the origin. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 10 / 56
37. 37. Examples of Lévy ProcessesExample 1, Brownian Motion and Gaussian ProcessesA (standard) Brownian motion in Rd is a Lévy processB = (B(t), t ≥ 0) for which(B1) B(t) ∼ N(0, tI) for each t ≥ 0,(B2) B has continuous sample paths.It follows immediately from (B1) that if B is a standard Brownianmotion, then its characteristic function is given by 1 φB(t) (u) = exp{− t|u|2 }, 2for each u ∈ Rd , t ≥ 0. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 11 / 56
38. 38. Examples of Lévy ProcessesExample 1, Brownian Motion and Gaussian ProcessesA (standard) Brownian motion in Rd is a Lévy processB = (B(t), t ≥ 0) for which(B1) B(t) ∼ N(0, tI) for each t ≥ 0,(B2) B has continuous sample paths.It follows immediately from (B1) that if B is a standard Brownianmotion, then its characteristic function is given by 1 φB(t) (u) = exp{− t|u|2 }, 2for each u ∈ Rd , t ≥ 0. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 11 / 56
39. 39. Examples of Lévy ProcessesExample 1, Brownian Motion and Gaussian ProcessesA (standard) Brownian motion in Rd is a Lévy processB = (B(t), t ≥ 0) for which(B1) B(t) ∼ N(0, tI) for each t ≥ 0,(B2) B has continuous sample paths.It follows immediately from (B1) that if B is a standard Brownianmotion, then its characteristic function is given by 1 φB(t) (u) = exp{− t|u|2 }, 2for each u ∈ Rd , t ≥ 0. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 11 / 56
40. 40. Examples of Lévy ProcessesExample 1, Brownian Motion and Gaussian ProcessesA (standard) Brownian motion in Rd is a Lévy processB = (B(t), t ≥ 0) for which(B1) B(t) ∼ N(0, tI) for each t ≥ 0,(B2) B has continuous sample paths.It follows immediately from (B1) that if B is a standard Brownianmotion, then its characteristic function is given by 1 φB(t) (u) = exp{− t|u|2 }, 2for each u ∈ Rd , t ≥ 0. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 11 / 56
41. 41. We introduce the marginal processes Bi = (Bi (t), t ≥ 0) where eachBi (t) is the ith component of B(t), then it is not difﬁcult to verify that theBi ’s are mutually independent Brownian motions in R. We will callthese one-dimensional Brownian motions in the sequel.Brownian motion has been the most intensively studied Lévy process.In the early years of the twentieth century, it was introduced as amodel for the physical phenomenon of Brownian motion by Einsteinand Smoluchowski and as a description of the dynamical evolution ofstock prices by Bachelier. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 12 / 56
42. 42. We introduce the marginal processes Bi = (Bi (t), t ≥ 0) where eachBi (t) is the ith component of B(t), then it is not difﬁcult to verify that theBi ’s are mutually independent Brownian motions in R. We will callthese one-dimensional Brownian motions in the sequel.Brownian motion has been the most intensively studied Lévy process.In the early years of the twentieth century, it was introduced as amodel for the physical phenomenon of Brownian motion by Einsteinand Smoluchowski and as a description of the dynamical evolution ofstock prices by Bachelier. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 12 / 56
43. 43. We introduce the marginal processes Bi = (Bi (t), t ≥ 0) where eachBi (t) is the ith component of B(t), then it is not difﬁcult to verify that theBi ’s are mutually independent Brownian motions in R. We will callthese one-dimensional Brownian motions in the sequel.Brownian motion has been the most intensively studied Lévy process.In the early years of the twentieth century, it was introduced as amodel for the physical phenomenon of Brownian motion by Einsteinand Smoluchowski and as a description of the dynamical evolution ofstock prices by Bachelier. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 12 / 56
44. 44. We introduce the marginal processes Bi = (Bi (t), t ≥ 0) where eachBi (t) is the ith component of B(t), then it is not difﬁcult to verify that theBi ’s are mutually independent Brownian motions in R. We will callthese one-dimensional Brownian motions in the sequel.Brownian motion has been the most intensively studied Lévy process.In the early years of the twentieth century, it was introduced as amodel for the physical phenomenon of Brownian motion by Einsteinand Smoluchowski and as a description of the dynamical evolution ofstock prices by Bachelier. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 12 / 56
45. 45. We introduce the marginal processes Bi = (Bi (t), t ≥ 0) where eachBi (t) is the ith component of B(t), then it is not difﬁcult to verify that theBi ’s are mutually independent Brownian motions in R. We will callthese one-dimensional Brownian motions in the sequel.Brownian motion has been the most intensively studied Lévy process.In the early years of the twentieth century, it was introduced as amodel for the physical phenomenon of Brownian motion by Einsteinand Smoluchowski and as a description of the dynamical evolution ofstock prices by Bachelier. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 12 / 56
46. 46. We introduce the marginal processes Bi = (Bi (t), t ≥ 0) where eachBi (t) is the ith component of B(t), then it is not difﬁcult to verify that theBi ’s are mutually independent Brownian motions in R. We will callthese one-dimensional Brownian motions in the sequel.Brownian motion has been the most intensively studied Lévy process.In the early years of the twentieth century, it was introduced as amodel for the physical phenomenon of Brownian motion by Einsteinand Smoluchowski and as a description of the dynamical evolution ofstock prices by Bachelier. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 12 / 56
47. 47. The theory was placed on a rigorous mathematical basis by NorbertWiener in the 1920’s.We could try to use the Kolmogorov existence theorem to constructone-dimensional Brownian motion from the following prescription oncylinder sets of the formItH,...,tn = {ω ∈ Ω; ω(t1 ) ∈ [a1 , b1 ], . . . , ω(tn ) ∈ [an , bn ]} where 1H = [a1 , b1 ] × · · · [an , bn ] and we have taken Ω to be the set of allmappings from R+ to R: P(ItH,...,tn ) 1 2 1 1 x1 (x2 − x1 )2 = n exp − + + ·· H (2π) 2 t1 (t2 − t1 ) . . . (tn − tn−1 ) 2 t1 t2 − t1 (xn − xn−1 )2 + dx1 · · · dxn . tn − tn−1However there there is then no guarantee that the paths arecontinuous. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 13 / 56
48. 48. The theory was placed on a rigorous mathematical basis by NorbertWiener in the 1920’s.We could try to use the Kolmogorov existence theorem to constructone-dimensional Brownian motion from the following prescription oncylinder sets of the formItH,...,tn = {ω ∈ Ω; ω(t1 ) ∈ [a1 , b1 ], . . . , ω(tn ) ∈ [an , bn ]} where 1H = [a1 , b1 ] × · · · [an , bn ] and we have taken Ω to be the set of allmappings from R+ to R: P(ItH,...,tn ) 1 2 1 1 x1 (x2 − x1 )2 = n exp − + + ·· H (2π) 2 t1 (t2 − t1 ) . . . (tn − tn−1 ) 2 t1 t2 − t1 (xn − xn−1 )2 + dx1 · · · dxn . tn − tn−1However there there is then no guarantee that the paths arecontinuous. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 13 / 56
49. 49. The theory was placed on a rigorous mathematical basis by NorbertWiener in the 1920’s.We could try to use the Kolmogorov existence theorem to constructone-dimensional Brownian motion from the following prescription oncylinder sets of the formItH,...,tn = {ω ∈ Ω; ω(t1 ) ∈ [a1 , b1 ], . . . , ω(tn ) ∈ [an , bn ]} where 1H = [a1 , b1 ] × · · · [an , bn ] and we have taken Ω to be the set of allmappings from R+ to R: P(ItH,...,tn ) 1 2 1 1 x1 (x2 − x1 )2 = n exp − + + ·· H (2π) 2 t1 (t2 − t1 ) . . . (tn − tn−1 ) 2 t1 t2 − t1 (xn − xn−1 )2 + dx1 · · · dxn . tn − tn−1However there there is then no guarantee that the paths arecontinuous. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 13 / 56
50. 50. The theory was placed on a rigorous mathematical basis by NorbertWiener in the 1920’s.We could try to use the Kolmogorov existence theorem to constructone-dimensional Brownian motion from the following prescription oncylinder sets of the formItH,...,tn = {ω ∈ Ω; ω(t1 ) ∈ [a1 , b1 ], . . . , ω(tn ) ∈ [an , bn ]} where 1H = [a1 , b1 ] × · · · [an , bn ] and we have taken Ω to be the set of allmappings from R+ to R: P(ItH,...,tn ) 1 2 1 1 x1 (x2 − x1 )2 = n exp − + + ·· H (2π) 2 t1 (t2 − t1 ) . . . (tn − tn−1 ) 2 t1 t2 − t1 (xn − xn−1 )2 + dx1 · · · dxn . tn − tn−1However there there is then no guarantee that the paths arecontinuous. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 13 / 56
51. 51. The theory was placed on a rigorous mathematical basis by NorbertWiener in the 1920’s.We could try to use the Kolmogorov existence theorem to constructone-dimensional Brownian motion from the following prescription oncylinder sets of the formItH,...,tn = {ω ∈ Ω; ω(t1 ) ∈ [a1 , b1 ], . . . , ω(tn ) ∈ [an , bn ]} where 1H = [a1 , b1 ] × · · · [an , bn ] and we have taken Ω to be the set of allmappings from R+ to R: P(ItH,...,tn ) 1 2 1 1 x1 (x2 − x1 )2 = n exp − + + ·· H (2π) 2 t1 (t2 − t1 ) . . . (tn − tn−1 ) 2 t1 t2 − t1 (xn − xn−1 )2 + dx1 · · · dxn . tn − tn−1However there there is then no guarantee that the paths arecontinuous. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 13 / 56
52. 52. The literature contains a number of ingenious methods for constructingBrownian motion. One of the most delightful of these (originally due toPaley and Wiener) obtains this, in the case d = 1, as a random Fourierseries for 0 ≤ t ≤ 1: √ ∞ 2 sin(πt(n + 1 )) 2 B(t) = 1 ξ(n), π n+ 2 n=0for each t ≥ 0, where (ξ(n), n ∈ N) is a sequence of i.i.d. N(0, 1)random variables. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 14 / 56
53. 53. The literature contains a number of ingenious methods for constructingBrownian motion. One of the most delightful of these (originally due toPaley and Wiener) obtains this, in the case d = 1, as a random Fourierseries for 0 ≤ t ≤ 1: √ ∞ 2 sin(πt(n + 1 )) 2 B(t) = 1 ξ(n), π n+ 2 n=0for each t ≥ 0, where (ξ(n), n ∈ N) is a sequence of i.i.d. N(0, 1)random variables. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 14 / 56
54. 54. The literature contains a number of ingenious methods for constructingBrownian motion. One of the most delightful of these (originally due toPaley and Wiener) obtains this, in the case d = 1, as a random Fourierseries for 0 ≤ t ≤ 1: √ ∞ 2 sin(πt(n + 1 )) 2 B(t) = 1 ξ(n), π n+ 2 n=0for each t ≥ 0, where (ξ(n), n ∈ N) is a sequence of i.i.d. N(0, 1)random variables. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 14 / 56
55. 55. We list a number of useful properties of Brownian motion in the cased = 1. Brownian motion is locally Hölder continuous with exponent α for every 0 < α < 1 i.e. for every T > 0, ω ∈ Ω there exists 2 K = K (T , ω) such that |B(t)(ω) − B(s)(ω)| ≤ K |t − s|α , for all 0 ≤ s < t ≤ T . The sample paths t → B(t)(ω) are almost surely nowhere differentiable. For any sequence, (tn , n ∈ N) in R+ with tn ↑ ∞, lim inf B(tn ) = −∞ a.s. lim sup B(tn ) = ∞ a.s. n→∞ n→∞ The law of the iterated logarithm:- B(t) P lim sup 1 =1 = 1. t↓0 (2t log(log( 1 ))) 2 t Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 15 / 56
56. 56. We list a number of useful properties of Brownian motion in the cased = 1. Brownian motion is locally Hölder continuous with exponent α for every 0 < α < 1 i.e. for every T > 0, ω ∈ Ω there exists 2 K = K (T , ω) such that |B(t)(ω) − B(s)(ω)| ≤ K |t − s|α , for all 0 ≤ s < t ≤ T . The sample paths t → B(t)(ω) are almost surely nowhere differentiable. For any sequence, (tn , n ∈ N) in R+ with tn ↑ ∞, lim inf B(tn ) = −∞ a.s. lim sup B(tn ) = ∞ a.s. n→∞ n→∞ The law of the iterated logarithm:- B(t) P lim sup 1 =1 = 1. t↓0 (2t log(log( 1 ))) 2 t Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 15 / 56
57. 57. We list a number of useful properties of Brownian motion in the cased = 1. Brownian motion is locally Hölder continuous with exponent α for every 0 < α < 1 i.e. for every T > 0, ω ∈ Ω there exists 2 K = K (T , ω) such that |B(t)(ω) − B(s)(ω)| ≤ K |t − s|α , for all 0 ≤ s < t ≤ T . The sample paths t → B(t)(ω) are almost surely nowhere differentiable. For any sequence, (tn , n ∈ N) in R+ with tn ↑ ∞, lim inf B(tn ) = −∞ a.s. lim sup B(tn ) = ∞ a.s. n→∞ n→∞ The law of the iterated logarithm:- B(t) P lim sup 1 =1 = 1. t↓0 (2t log(log( 1 ))) 2 t Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 15 / 56
58. 58. We list a number of useful properties of Brownian motion in the cased = 1. Brownian motion is locally Hölder continuous with exponent α for every 0 < α < 1 i.e. for every T > 0, ω ∈ Ω there exists 2 K = K (T , ω) such that |B(t)(ω) − B(s)(ω)| ≤ K |t − s|α , for all 0 ≤ s < t ≤ T . The sample paths t → B(t)(ω) are almost surely nowhere differentiable. For any sequence, (tn , n ∈ N) in R+ with tn ↑ ∞, lim inf B(tn ) = −∞ a.s. lim sup B(tn ) = ∞ a.s. n→∞ n→∞ The law of the iterated logarithm:- B(t) P lim sup 1 =1 = 1. t↓0 (2t log(log( 1 ))) 2 t Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 15 / 56
59. 59. We list a number of useful properties of Brownian motion in the cased = 1. Brownian motion is locally Hölder continuous with exponent α for every 0 < α < 1 i.e. for every T > 0, ω ∈ Ω there exists 2 K = K (T , ω) such that |B(t)(ω) − B(s)(ω)| ≤ K |t − s|α , for all 0 ≤ s < t ≤ T . The sample paths t → B(t)(ω) are almost surely nowhere differentiable. For any sequence, (tn , n ∈ N) in R+ with tn ↑ ∞, lim inf B(tn ) = −∞ a.s. lim sup B(tn ) = ∞ a.s. n→∞ n→∞ The law of the iterated logarithm:- B(t) P lim sup 1 =1 = 1. t↓0 (2t log(log( 1 ))) 2 t Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 15 / 56
60. 60. 1.2 0.8 0.4 0 −0.4 −0.8 −1.2 −1.6 −2.0 0 1 2 3 4 5Simulation of standard Brownian motion Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 16 / 56
61. 61. Let A be a non-negative symmetric d × d matrix and let σ be a squareroot of A so that σ is a d × m matrix for which σσ T = A. Now let b ∈ Rdand let B be a Brownian motion in R m . We construct a processC = (C(t), t ≥ 0) in Rd by C(t) = bt + σB(t), (2.3)then C is a Lévy process with each C(t) ∼ N(tb, tA). It is not difﬁcult toverify that C is also a Gaussian process, i.e. all its ﬁnite dimensionaldistributions are Gaussian. It is sometimes called Brownian motionwith drift. The Lévy symbol of C is 1 ηC (u) = i(b, u) − (u, Au). 2In fact a Lévy process has continuous sample paths if and only if it is ofthe form (2.3). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 17 / 56
62. 62. Let A be a non-negative symmetric d × d matrix and let σ be a squareroot of A so that σ is a d × m matrix for which σσ T = A. Now let b ∈ Rdand let B be a Brownian motion in R m . We construct a processC = (C(t), t ≥ 0) in Rd by C(t) = bt + σB(t), (2.3)then C is a Lévy process with each C(t) ∼ N(tb, tA). It is not difﬁcult toverify that C is also a Gaussian process, i.e. all its ﬁnite dimensionaldistributions are Gaussian. It is sometimes called Brownian motionwith drift. The Lévy symbol of C is 1 ηC (u) = i(b, u) − (u, Au). 2In fact a Lévy process has continuous sample paths if and only if it is ofthe form (2.3). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 17 / 56
63. 63. Let A be a non-negative symmetric d × d matrix and let σ be a squareroot of A so that σ is a d × m matrix for which σσ T = A. Now let b ∈ Rdand let B be a Brownian motion in R m . We construct a processC = (C(t), t ≥ 0) in Rd by C(t) = bt + σB(t), (2.3)then C is a Lévy process with each C(t) ∼ N(tb, tA). It is not difﬁcult toverify that C is also a Gaussian process, i.e. all its ﬁnite dimensionaldistributions are Gaussian. It is sometimes called Brownian motionwith drift. The Lévy symbol of C is 1 ηC (u) = i(b, u) − (u, Au). 2In fact a Lévy process has continuous sample paths if and only if it is ofthe form (2.3). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 17 / 56
64. 64. Let A be a non-negative symmetric d × d matrix and let σ be a squareroot of A so that σ is a d × m matrix for which σσ T = A. Now let b ∈ Rdand let B be a Brownian motion in R m . We construct a processC = (C(t), t ≥ 0) in Rd by C(t) = bt + σB(t), (2.3)then C is a Lévy process with each C(t) ∼ N(tb, tA). It is not difﬁcult toverify that C is also a Gaussian process, i.e. all its ﬁnite dimensionaldistributions are Gaussian. It is sometimes called Brownian motionwith drift. The Lévy symbol of C is 1 ηC (u) = i(b, u) − (u, Au). 2In fact a Lévy process has continuous sample paths if and only if it is ofthe form (2.3). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 17 / 56
65. 65. Let A be a non-negative symmetric d × d matrix and let σ be a squareroot of A so that σ is a d × m matrix for which σσ T = A. Now let b ∈ Rdand let B be a Brownian motion in R m . We construct a processC = (C(t), t ≥ 0) in Rd by C(t) = bt + σB(t), (2.3)then C is a Lévy process with each C(t) ∼ N(tb, tA). It is not difﬁcult toverify that C is also a Gaussian process, i.e. all its ﬁnite dimensionaldistributions are Gaussian. It is sometimes called Brownian motionwith drift. The Lévy symbol of C is 1 ηC (u) = i(b, u) − (u, Au). 2In fact a Lévy process has continuous sample paths if and only if it is ofthe form (2.3). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 17 / 56
66. 66. Let A be a non-negative symmetric d × d matrix and let σ be a squareroot of A so that σ is a d × m matrix for which σσ T = A. Now let b ∈ Rdand let B be a Brownian motion in R m . We construct a processC = (C(t), t ≥ 0) in Rd by C(t) = bt + σB(t), (2.3)then C is a Lévy process with each C(t) ∼ N(tb, tA). It is not difﬁcult toverify that C is also a Gaussian process, i.e. all its ﬁnite dimensionaldistributions are Gaussian. It is sometimes called Brownian motionwith drift. The Lévy symbol of C is 1 ηC (u) = i(b, u) − (u, Au). 2In fact a Lévy process has continuous sample paths if and only if it is ofthe form (2.3). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 17 / 56
67. 67. Let A be a non-negative symmetric d × d matrix and let σ be a squareroot of A so that σ is a d × m matrix for which σσ T = A. Now let b ∈ Rdand let B be a Brownian motion in R m . We construct a processC = (C(t), t ≥ 0) in Rd by C(t) = bt + σB(t), (2.3)then C is a Lévy process with each C(t) ∼ N(tb, tA). It is not difﬁcult toverify that C is also a Gaussian process, i.e. all its ﬁnite dimensionaldistributions are Gaussian. It is sometimes called Brownian motionwith drift. The Lévy symbol of C is 1 ηC (u) = i(b, u) − (u, Au). 2In fact a Lévy process has continuous sample paths if and only if it is ofthe form (2.3). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 17 / 56
68. 68. Let A be a non-negative symmetric d × d matrix and let σ be a squareroot of A so that σ is a d × m matrix for which σσ T = A. Now let b ∈ Rdand let B be a Brownian motion in R m . We construct a processC = (C(t), t ≥ 0) in Rd by C(t) = bt + σB(t), (2.3)then C is a Lévy process with each C(t) ∼ N(tb, tA). It is not difﬁcult toverify that C is also a Gaussian process, i.e. all its ﬁnite dimensionaldistributions are Gaussian. It is sometimes called Brownian motionwith drift. The Lévy symbol of C is 1 ηC (u) = i(b, u) − (u, Au). 2In fact a Lévy process has continuous sample paths if and only if it is ofthe form (2.3). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 17 / 56
69. 69. Example 2 - The Poisson ProcessThe Poisson process of intensity λ > 0 is a Lévy process N takingvalues in N ∪ {0} wherein each N(t) ∼ π(λt) so we have (λt)n −λt P(N(t) = n) = e , n!for each n = 0, 1, 2, . . ..The Poisson process is widely used in applications and there is awealth of literature concerning it and its generalisations. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 18 / 56
70. 70. Example 2 - The Poisson ProcessThe Poisson process of intensity λ > 0 is a Lévy process N takingvalues in N ∪ {0} wherein each N(t) ∼ π(λt) so we have (λt)n −λt P(N(t) = n) = e , n!for each n = 0, 1, 2, . . ..The Poisson process is widely used in applications and there is awealth of literature concerning it and its generalisations. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 18 / 56
71. 71. Example 2 - The Poisson ProcessThe Poisson process of intensity λ > 0 is a Lévy process N takingvalues in N ∪ {0} wherein each N(t) ∼ π(λt) so we have (λt)n −λt P(N(t) = n) = e , n!for each n = 0, 1, 2, . . ..The Poisson process is widely used in applications and there is awealth of literature concerning it and its generalisations. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 18 / 56
72. 72. We deﬁne non-negative random variables (Tn , N ∪ {0}) (usually calledwaiting times) by T0 = 0 and for n ∈ N, Tn = inf{t ≥ 0; N(t) = n},then it is well known that the Tn ’s are gamma distributed. Moreover,the inter-arrival times Tn − Tn−1 for n ∈ N are i.i.d. and each has 1exponential distribution with mean λ . The sample paths of N areclearly piecewise constant with “jump” discontinuities of size 1 at eachof the random times (Tn , n ∈ N). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 19 / 56
73. 73. We deﬁne non-negative random variables (Tn , N ∪ {0}) (usually calledwaiting times) by T0 = 0 and for n ∈ N, Tn = inf{t ≥ 0; N(t) = n},then it is well known that the Tn ’s are gamma distributed. Moreover,the inter-arrival times Tn − Tn−1 for n ∈ N are i.i.d. and each has 1exponential distribution with mean λ . The sample paths of N areclearly piecewise constant with “jump” discontinuities of size 1 at eachof the random times (Tn , n ∈ N). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 19 / 56
74. 74. We deﬁne non-negative random variables (Tn , N ∪ {0}) (usually calledwaiting times) by T0 = 0 and for n ∈ N, Tn = inf{t ≥ 0; N(t) = n},then it is well known that the Tn ’s are gamma distributed. Moreover,the inter-arrival times Tn − Tn−1 for n ∈ N are i.i.d. and each has 1exponential distribution with mean λ . The sample paths of N areclearly piecewise constant with “jump” discontinuities of size 1 at eachof the random times (Tn , n ∈ N). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 19 / 56
75. 75. We deﬁne non-negative random variables (Tn , N ∪ {0}) (usually calledwaiting times) by T0 = 0 and for n ∈ N, Tn = inf{t ≥ 0; N(t) = n},then it is well known that the Tn ’s are gamma distributed. Moreover,the inter-arrival times Tn − Tn−1 for n ∈ N are i.i.d. and each has 1exponential distribution with mean λ . The sample paths of N areclearly piecewise constant with “jump” discontinuities of size 1 at eachof the random times (Tn , n ∈ N). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 19 / 56
76. 76. 10 8 6 Number 4 2 0 0 5 10 15 20 TimeSimulation of a Poisson process (λ = 0.5) Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 20 / 56
77. 77. For later work it is useful to introduce the compensated Poisson ˜ ˜ ˜process N = (N(t), t ≥ 0) where each N(t) = N(t) − λt. Note that ˜E(N(t)) = 0 and E(N(t)˜ 2 ) = λt for each t ≥ 0 . Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 21 / 56
78. 78. For later work it is useful to introduce the compensated Poisson ˜ ˜ ˜process N = (N(t), t ≥ 0) where each N(t) = N(t) − λt. Note that ˜E(N(t)) = 0 and E(N(t)˜ 2 ) = λt for each t ≥ 0 . Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 21 / 56
79. 79. Example 3 - The Compound Poisson ProcessLet (Z (n), n ∈ N) be a sequence of i.i.d. random variables takingvalues in Rd with common law µZ and let N be a Poisson process ofintensity λ which is independent of all the Z (n)’s. The compoundPoisson process Y is deﬁned as follows:- 0 if N(t) = 0 Y (t) := Z (1) + · · · + Z (N(t)) if N(t) > 0,for each t ≥ 0,so each Y (t) ∼ π(λt, µZ ). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 22 / 56
80. 80. Example 3 - The Compound Poisson ProcessLet (Z (n), n ∈ N) be a sequence of i.i.d. random variables takingvalues in Rd with common law µZ and let N be a Poisson process ofintensity λ which is independent of all the Z (n)’s. The compoundPoisson process Y is deﬁned as follows:- 0 if N(t) = 0 Y (t) := Z (1) + · · · + Z (N(t)) if N(t) > 0,for each t ≥ 0,so each Y (t) ∼ π(λt, µZ ). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 22 / 56
81. 81. Example 3 - The Compound Poisson ProcessLet (Z (n), n ∈ N) be a sequence of i.i.d. random variables takingvalues in Rd with common law µZ and let N be a Poisson process ofintensity λ which is independent of all the Z (n)’s. The compoundPoisson process Y is deﬁned as follows:- 0 if N(t) = 0 Y (t) := Z (1) + · · · + Z (N(t)) if N(t) > 0,for each t ≥ 0,so each Y (t) ∼ π(λt, µZ ). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 22 / 56
82. 82. From the work of Lecture 1, Y has Lévy symbol ηY (u) = (ei(u,y ) − 1)λµZ (dy ) .Again the sample paths of Y are piecewise constant with “jumpdiscontinuities” at the random times (T (n), n ∈ N), however this timethe size of the jumps is itself random, and the jump at T (n) can be anyvalue in the range of the random variable Z (n). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 23 / 56
83. 83. From the work of Lecture 1, Y has Lévy symbol ηY (u) = (ei(u,y ) − 1)λµZ (dy ) .Again the sample paths of Y are piecewise constant with “jumpdiscontinuities” at the random times (T (n), n ∈ N), however this timethe size of the jumps is itself random, and the jump at T (n) can be anyvalue in the range of the random variable Z (n). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 23 / 56
84. 84. From the work of Lecture 1, Y has Lévy symbol ηY (u) = (ei(u,y ) − 1)λµZ (dy ) .Again the sample paths of Y are piecewise constant with “jumpdiscontinuities” at the random times (T (n), n ∈ N), however this timethe size of the jumps is itself random, and the jump at T (n) can be anyvalue in the range of the random variable Z (n). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 23 / 56
85. 85. 0 -1 Path -2 -3 0 10 20 30 TimeSimulation of a compound Poisson process with N(0, 1)summands(λ = 1). Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 24 / 56
86. 86. Example 4 - Interlacing ProcessesLet C be a Gaussian Lévy process as in Example 1 and Y be acompound Poisson process as in Example 3, which is independent ofC.Deﬁne a new process X by X (t) = C(t) + Y (t),for all t ≥ 0, then it is not difﬁcult to verify that X is a Lévy process withLévy symbol 1 ηX (u) = i(b, u) − (u, Au) + (ei(u,y ) − 1)λµZ (dy ) . 2Using the notation of Examples 2 and 3, we see that the paths of Xhave jumps of random size occurring at random times. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 25 / 56
87. 87. Example 4 - Interlacing ProcessesLet C be a Gaussian Lévy process as in Example 1 and Y be acompound Poisson process as in Example 3, which is independent ofC.Deﬁne a new process X by X (t) = C(t) + Y (t),for all t ≥ 0, then it is not difﬁcult to verify that X is a Lévy process withLévy symbol 1 ηX (u) = i(b, u) − (u, Au) + (ei(u,y ) − 1)λµZ (dy ) . 2Using the notation of Examples 2 and 3, we see that the paths of Xhave jumps of random size occurring at random times. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 25 / 56
88. 88. Example 4 - Interlacing ProcessesLet C be a Gaussian Lévy process as in Example 1 and Y be acompound Poisson process as in Example 3, which is independent ofC.Deﬁne a new process X by X (t) = C(t) + Y (t),for all t ≥ 0, then it is not difﬁcult to verify that X is a Lévy process withLévy symbol 1 ηX (u) = i(b, u) − (u, Au) + (ei(u,y ) − 1)λµZ (dy ) . 2Using the notation of Examples 2 and 3, we see that the paths of Xhave jumps of random size occurring at random times. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 25 / 56
89. 89. Example 4 - Interlacing ProcessesLet C be a Gaussian Lévy process as in Example 1 and Y be acompound Poisson process as in Example 3, which is independent ofC.Deﬁne a new process X by X (t) = C(t) + Y (t),for all t ≥ 0, then it is not difﬁcult to verify that X is a Lévy process withLévy symbol 1 ηX (u) = i(b, u) − (u, Au) + (ei(u,y ) − 1)λµZ (dy ) . 2Using the notation of Examples 2 and 3, we see that the paths of Xhave jumps of random size occurring at random times. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 25 / 56
90. 90. Example 4 - Interlacing ProcessesLet C be a Gaussian Lévy process as in Example 1 and Y be acompound Poisson process as in Example 3, which is independent ofC.Deﬁne a new process X by X (t) = C(t) + Y (t),for all t ≥ 0, then it is not difﬁcult to verify that X is a Lévy process withLévy symbol 1 ηX (u) = i(b, u) − (u, Au) + (ei(u,y ) − 1)λµZ (dy ) . 2Using the notation of Examples 2 and 3, we see that the paths of Xhave jumps of random size occurring at random times. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 25 / 56
91. 91. X (t) = C(t) for 0 ≤ t < T1 , = C(T1 ) + Z1 when t = T1 , = X (T1 ) + C(t) − C(T1 ) for T1 < t < T2 , = X (T2 −) + Z2 when t = T2 ,and so on recursively. We call this procedure an interlacing as acontinuous path process is “interlaced” with random jumps. It seemsreasonable that the most general Lévy process might arise as the limitof a sequence of such interlacings, and this can be establishedrigorously. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 26 / 56
92. 92. X (t) = C(t) for 0 ≤ t < T1 , = C(T1 ) + Z1 when t = T1 , = X (T1 ) + C(t) − C(T1 ) for T1 < t < T2 , = X (T2 −) + Z2 when t = T2 ,and so on recursively. We call this procedure an interlacing as acontinuous path process is “interlaced” with random jumps. It seemsreasonable that the most general Lévy process might arise as the limitof a sequence of such interlacings, and this can be establishedrigorously. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 26 / 56
93. 93. X (t) = C(t) for 0 ≤ t < T1 , = C(T1 ) + Z1 when t = T1 , = X (T1 ) + C(t) − C(T1 ) for T1 < t < T2 , = X (T2 −) + Z2 when t = T2 ,and so on recursively. We call this procedure an interlacing as acontinuous path process is “interlaced” with random jumps. It seemsreasonable that the most general Lévy process might arise as the limitof a sequence of such interlacings, and this can be establishedrigorously. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 26 / 56
94. 94. X (t) = C(t) for 0 ≤ t < T1 , = C(T1 ) + Z1 when t = T1 , = X (T1 ) + C(t) − C(T1 ) for T1 < t < T2 , = X (T2 −) + Z2 when t = T2 ,and so on recursively. We call this procedure an interlacing as acontinuous path process is “interlaced” with random jumps. It seemsreasonable that the most general Lévy process might arise as the limitof a sequence of such interlacings, and this can be establishedrigorously. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 26 / 56
95. 95. X (t) = C(t) for 0 ≤ t < T1 , = C(T1 ) + Z1 when t = T1 , = X (T1 ) + C(t) − C(T1 ) for T1 < t < T2 , = X (T2 −) + Z2 when t = T2 ,and so on recursively. We call this procedure an interlacing as acontinuous path process is “interlaced” with random jumps. It seemsreasonable that the most general Lévy process might arise as the limitof a sequence of such interlacings, and this can be establishedrigorously. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 26 / 56
96. 96. X (t) = C(t) for 0 ≤ t < T1 , = C(T1 ) + Z1 when t = T1 , = X (T1 ) + C(t) − C(T1 ) for T1 < t < T2 , = X (T2 −) + Z2 when t = T2 ,and so on recursively. We call this procedure an interlacing as acontinuous path process is “interlaced” with random jumps. It seemsreasonable that the most general Lévy process might arise as the limitof a sequence of such interlacings, and this can be establishedrigorously. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 26 / 56
97. 97. Example 5 - Stable Lévy ProcessesA stable Lévy process is a Lévy process X in which the Lévy symbol isthat of a given stable law. So, in particular, each X (t) is a stablerandom variable. For example, we have the rotationally invariant casewhose Lévy symbol is given by η(u) = −σ α |u|α ,where α is the index of stability (0 < α ≤ 2). One of the reasons whythese are important in applications is that they display self-similarity. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 27 / 56
98. 98. Example 5 - Stable Lévy ProcessesA stable Lévy process is a Lévy process X in which the Lévy symbol isthat of a given stable law. So, in particular, each X (t) is a stablerandom variable. For example, we have the rotationally invariant casewhose Lévy symbol is given by η(u) = −σ α |u|α ,where α is the index of stability (0 < α ≤ 2). One of the reasons whythese are important in applications is that they display self-similarity. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 27 / 56
99. 99. Example 5 - Stable Lévy ProcessesA stable Lévy process is a Lévy process X in which the Lévy symbol isthat of a given stable law. So, in particular, each X (t) is a stablerandom variable. For example, we have the rotationally invariant casewhose Lévy symbol is given by η(u) = −σ α |u|α ,where α is the index of stability (0 < α ≤ 2). One of the reasons whythese are important in applications is that they display self-similarity. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 27 / 56
100. 100. Example 5 - Stable Lévy ProcessesA stable Lévy process is a Lévy process X in which the Lévy symbol isthat of a given stable law. So, in particular, each X (t) is a stablerandom variable. For example, we have the rotationally invariant casewhose Lévy symbol is given by η(u) = −σ α |u|α ,where α is the index of stability (0 < α ≤ 2). One of the reasons whythese are important in applications is that they display self-similarity. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 27 / 56
101. 101. In general, a stochastic process Y = (Y (t), t ≥ 0) is self-similar withHurst index H > 0 if the two processes (Y (at), t ≥ 0) and(aH Y (t), t ≥ 0) have the same ﬁnite-dimensional distributions for alla ≥ 0. By examining characteristic functions, it is easily veriﬁed that arotationally invariant stable Lévy process is self-similar with Hurstindex H = α , so that e.g. Brownian motion is self-similar with H = 1 . A 1 2Lévy process X is self-similar if and only if each X (t) is strictly stable. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 28 / 56
102. 102. In general, a stochastic process Y = (Y (t), t ≥ 0) is self-similar withHurst index H > 0 if the two processes (Y (at), t ≥ 0) and(aH Y (t), t ≥ 0) have the same ﬁnite-dimensional distributions for alla ≥ 0. By examining characteristic functions, it is easily veriﬁed that arotationally invariant stable Lévy process is self-similar with Hurst 1 1index H = α , so that e.g. Brownian motion is self-similar with H = 2 . ALévy process X is self-similar if and only if each X (t) is strictly stable. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 28 / 56
103. 103. In general, a stochastic process Y = (Y (t), t ≥ 0) is self-similar withHurst index H > 0 if the two processes (Y (at), t ≥ 0) and(aH Y (t), t ≥ 0) have the same ﬁnite-dimensional distributions for alla ≥ 0. By examining characteristic functions, it is easily veriﬁed that arotationally invariant stable Lévy process is self-similar with Hurst 1 1index H = α , so that e.g. Brownian motion is self-similar with H = 2 . ALévy process X is self-similar if and only if each X (t) is strictly stable. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 28 / 56
104. 104. In general, a stochastic process Y = (Y (t), t ≥ 0) is self-similar withHurst index H > 0 if the two processes (Y (at), t ≥ 0) and(aH Y (t), t ≥ 0) have the same ﬁnite-dimensional distributions for alla ≥ 0. By examining characteristic functions, it is easily veriﬁed that arotationally invariant stable Lévy process is self-similar with Hurst 1 1index H = α , so that e.g. Brownian motion is self-similar with H = 2 . ALévy process X is self-similar if and only if each X (t) is strictly stable. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 28 / 56
105. 105. 80 40 0 −40 −80 −120 −160 −200 −240 0 1 2 3 4 5Simulation of the Cauchy process. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 29 / 56
106. 106. Densities of Lévy ProcessesQuestion: When does a Lévy process have a density ft for all t > 0 sothat for all Borel sets B: P(Xt ∈ B) = pt (B) = ft (x)dx? BIn general, a random variable has a continuous density if itscharacteristic function is integrable and in this case, the density is theFourier transform of the characteristic function.So for Lévy processes, if for all t > 0, |etη(u) |du = et (η(u)) du < ∞ Rd Rdwe then have ft (x) = (2π)−d etη(u)−i(u,x) du. Rd Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 30 / 56
107. 107. Densities of Lévy ProcessesQuestion: When does a Lévy process have a density ft for all t > 0 sothat for all Borel sets B: P(Xt ∈ B) = pt (B) = ft (x)dx? BIn general, a random variable has a continuous density if itscharacteristic function is integrable and in this case, the density is theFourier transform of the characteristic function.So for Lévy processes, if for all t > 0, |etη(u) |du = et (η(u)) du < ∞ Rd Rdwe then have ft (x) = (2π)−d etη(u)−i(u,x) du. Rd Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 30 / 56
108. 108. Densities of Lévy ProcessesQuestion: When does a Lévy process have a density ft for all t > 0 sothat for all Borel sets B: P(Xt ∈ B) = pt (B) = ft (x)dx? BIn general, a random variable has a continuous density if itscharacteristic function is integrable and in this case, the density is theFourier transform of the characteristic function.So for Lévy processes, if for all t > 0, |etη(u) |du = et (η(u)) du < ∞ Rd Rdwe then have ft (x) = (2π)−d etη(u)−i(u,x) du. Rd Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 30 / 56
109. 109. Densities of Lévy ProcessesQuestion: When does a Lévy process have a density ft for all t > 0 sothat for all Borel sets B: P(Xt ∈ B) = pt (B) = ft (x)dx? BIn general, a random variable has a continuous density if itscharacteristic function is integrable and in this case, the density is theFourier transform of the characteristic function.So for Lévy processes, if for all t > 0, |etη(u) |du = et (η(u)) du < ∞ Rd Rdwe then have ft (x) = (2π)−d etη(u)−i(u,x) du. Rd Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 30 / 56
110. 110. Every Lévy process with a non-degenerate Gaussian component hasa density.In this case 1 (η(u)) = − (u, Au) + (cos(u, y ) − 1)ν(dy ), 2 Rd −{0}and so t et (η(u)) du ≤ e− 2 (u,Au) < ∞, Rd Rdusing (u, Au) ≥ λ|u|2 where λ > 0 is smallest eigenvalue of A. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 31 / 56
111. 111. Every Lévy process with a non-degenerate Gaussian component hasa density.In this case 1 (η(u)) = − (u, Au) + (cos(u, y ) − 1)ν(dy ), 2 Rd −{0}and so t et (η(u)) du ≤ e− 2 (u,Au) < ∞, Rd Rdusing (u, Au) ≥ λ|u|2 where λ > 0 is smallest eigenvalue of A. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 31 / 56
112. 112. Every Lévy process with a non-degenerate Gaussian component hasa density.In this case 1 (η(u)) = − (u, Au) + (cos(u, y ) − 1)ν(dy ), 2 Rd −{0}and so t et (η(u)) du ≤ e− 2 (u,Au) < ∞, Rd Rdusing (u, Au) ≥ λ|u|2 where λ > 0 is smallest eigenvalue of A. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 31 / 56
113. 113. For examples where densities exist for A = 0 with d = 1: if X isα-stable, it has a density since for all 1 ≤ α ≤ 2: α e−t|u| du ≤ e−t|u| du < ∞, |u|≥1 |u|≥1and for 0 ≤ α < 1: ∞ α 2 1 e−t|u| du = e−ty y α −1 dy < ∞. R α 0 Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 32 / 56
114. 114. For examples where densities exist for A = 0 with d = 1: if X isα-stable, it has a density since for all 1 ≤ α ≤ 2: α e−t|u| du ≤ e−t|u| du < ∞, |u|≥1 |u|≥1and for 0 ≤ α < 1: ∞ α 2 1 e−t|u| du = e−ty y α −1 dy < ∞. R α 0 Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 32 / 56
115. 115. For examples where densities exist for A = 0 with d = 1: if X isα-stable, it has a density since for all 1 ≤ α ≤ 2: α e−t|u| du ≤ e−t|u| du < ∞, |u|≥1 |u|≥1and for 0 ≤ α < 1: ∞ α 2 1 e−t|u| du = e−ty y α −1 dy < ∞. R α 0 Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 32 / 56
116. 116. In general, a sufﬁcient condition for a density is ν(Rd ) = ∞ ν ∗m is absolutely continuous with respect to Lebesgue measure ˜ for some m ∈ N where ν (A) = ˜ (|x|2 ∧ 1)ν(dx). A Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 33 / 56
117. 117. In general, a sufﬁcient condition for a density is ν(Rd ) = ∞ ν ∗m is absolutely continuous with respect to Lebesgue measure ˜ for some m ∈ N where ν (A) = ˜ (|x|2 ∧ 1)ν(dx). A Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 33 / 56
118. 118. In general, a sufﬁcient condition for a density is ν(Rd ) = ∞ ν ∗m is absolutely continuous with respect to Lebesgue measure ˜ for some m ∈ N where ν (A) = ˜ (|x|2 ∧ 1)ν(dx). A Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 33 / 56
119. 119. A Lévy process has a Lévy density gν if its Lévy measure ν isabsolutely continuous with respect to Lebesgue measure, then gν is dνdeﬁned to be the Radon-Nikodym derivative . dxA process may have a Lévy density but not have a density.Example. Let X be a compound Poisson process with eachX (t) = Y1 + Y2 + · · · + YN(t) wherein each Yj has a density fY , thengν = λfY is the Lévy density.But P(Y (t) = 0) ≥ P(N(t) = 0) = e−λt > 0,so pt has an atom at {0}. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 34 / 56
120. 120. A Lévy process has a Lévy density gν if its Lévy measure ν isabsolutely continuous with respect to Lebesgue measure, then gν is dνdeﬁned to be the Radon-Nikodym derivative . dxA process may have a Lévy density but not have a density.Example. Let X be a compound Poisson process with eachX (t) = Y1 + Y2 + · · · + YN(t) wherein each Yj has a density fY , thengν = λfY is the Lévy density.But P(Y (t) = 0) ≥ P(N(t) = 0) = e−λt > 0,so pt has an atom at {0}. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 34 / 56
121. 121. A Lévy process has a Lévy density gν if its Lévy measure ν isabsolutely continuous with respect to Lebesgue measure, then gν is dνdeﬁned to be the Radon-Nikodym derivative . dxA process may have a Lévy density but not have a density.Example. Let X be a compound Poisson process with eachX (t) = Y1 + Y2 + · · · + YN(t) wherein each Yj has a density fY , thengν = λfY is the Lévy density.But P(Y (t) = 0) ≥ P(N(t) = 0) = e−λt > 0,so pt has an atom at {0}. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 34 / 56
122. 122. A Lévy process has a Lévy density gν if its Lévy measure ν isabsolutely continuous with respect to Lebesgue measure, then gν is dνdeﬁned to be the Radon-Nikodym derivative . dxA process may have a Lévy density but not have a density.Example. Let X be a compound Poisson process with eachX (t) = Y1 + Y2 + · · · + YN(t) wherein each Yj has a density fY , thengν = λfY is the Lévy density.But P(Y (t) = 0) ≥ P(N(t) = 0) = e−λt > 0,so pt has an atom at {0}. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 34 / 56
123. 123. A Lévy process has a Lévy density gν if its Lévy measure ν isabsolutely continuous with respect to Lebesgue measure, then gν is dνdeﬁned to be the Radon-Nikodym derivative . dxA process may have a Lévy density but not have a density.Example. Let X be a compound Poisson process with eachX (t) = Y1 + Y2 + · · · + YN(t) wherein each Yj has a density fY , thengν = λfY is the Lévy density.But P(Y (t) = 0) ≥ P(N(t) = 0) = e−λt > 0,so pt has an atom at {0}. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 34 / 56
124. 124. We have pt (A) = e−λt δ0 (A) + ac A ft (x)dx, where for x = 0 ∞ −λt (λt)n ∗n ftac (x) =e f (x). n! Y n=1ftac (x) is the conditional density of X (t) given that it jumps at least oncebetween 0 and t.In this case, (2.2) takes the precise form (for x = 0) ftac (x) gν (x) = lim . t↓0 t Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 35 / 56
125. 125. We have pt (A) = e−λt δ0 (A) + ac A ft (x)dx, where for x = 0 ∞ −λt (λt)n ∗n ftac (x) =e f (x). n! Y n=1ftac (x) is the conditional density of X (t) given that it jumps at least oncebetween 0 and t.In this case, (2.2) takes the precise form (for x = 0) ftac (x) gν (x) = lim . t↓0 t Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 35 / 56
126. 126. We have pt (A) = e−λt δ0 (A) + ac A ft (x)dx, where for x = 0 ∞ −λt (λt)n ∗n ftac (x) =e f (x). n! Y n=1ftac (x) is the conditional density of X (t) given that it jumps at least oncebetween 0 and t.In this case, (2.2) takes the precise form (for x = 0) ftac (x) gν (x) = lim . t↓0 t Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 35 / 56
127. 127. We have pt (A) = e−λt δ0 (A) + ac A ft (x)dx, where for x = 0 ∞ −λt (λt)n ∗n ftac (x) =e f (x). n! Y n=1ftac (x) is the conditional density of X (t) given that it jumps at least oncebetween 0 and t.In this case, (2.2) takes the precise form (for x = 0) ftac (x) gν (x) = lim . t↓0 t Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 35 / 56
128. 128. SubordinatorsA subordinator is a one-dimensional Lévy process which is increasinga.s. Such processes can be thought of as a random model of timeevolution, since if T = (T (t), t ≥ 0) is a subordinator we haveT (t) ≥ 0 for each t > 0 a.s. and T (t1 ) ≤ T (t2 ) whenever t1 ≤ t2 a.s.Now since for X (t) ∼ N(0, At) we haveP(X (t) ≥ 0) = P(X (t) ≤ 0) = 1 , it is clear that such a process cannot 2be a subordinator. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 36 / 56
129. 129. SubordinatorsA subordinator is a one-dimensional Lévy process which is increasinga.s. Such processes can be thought of as a random model of timeevolution, since if T = (T (t), t ≥ 0) is a subordinator we haveT (t) ≥ 0 for each t > 0 a.s. and T (t1 ) ≤ T (t2 ) whenever t1 ≤ t2 a.s.Now since for X (t) ∼ N(0, At) we have 1P(X (t) ≥ 0) = P(X (t) ≤ 0) = 2 , it is clear that such a process cannotbe a subordinator. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 36 / 56
130. 130. SubordinatorsA subordinator is a one-dimensional Lévy process which is increasinga.s. Such processes can be thought of as a random model of timeevolution, since if T = (T (t), t ≥ 0) is a subordinator we haveT (t) ≥ 0 for each t > 0 a.s. and T (t1 ) ≤ T (t2 ) whenever t1 ≤ t2 a.s.Now since for X (t) ∼ N(0, At) we have 1P(X (t) ≥ 0) = P(X (t) ≤ 0) = 2 , it is clear that such a process cannotbe a subordinator. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 36 / 56
131. 131. TheoremIf T is a subordinator then its Lévy symbol takes the form η(u) = ibu + (eiuy − 1)λ(dy ), (2.4) (0,∞)where b ≥ 0, and the Lévy measure λ satisﬁes the additionalrequirements λ(−∞, 0) = 0 and (y ∧ 1)λ(dy ) < ∞. (0,∞)Conversely, any mapping from Rd → C of the form (2.4) is the Lévysymbol of a subordinator.We call the pair (b, λ), the characteristics of the subordinator T . Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 37 / 56
132. 132. TheoremIf T is a subordinator then its Lévy symbol takes the form η(u) = ibu + (eiuy − 1)λ(dy ), (2.4) (0,∞)where b ≥ 0, and the Lévy measure λ satisﬁes the additionalrequirements λ(−∞, 0) = 0 and (y ∧ 1)λ(dy ) < ∞. (0,∞)Conversely, any mapping from Rd → C of the form (2.4) is the Lévysymbol of a subordinator.We call the pair (b, λ), the characteristics of the subordinator T . Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 37 / 56
133. 133. TheoremIf T is a subordinator then its Lévy symbol takes the form η(u) = ibu + (eiuy − 1)λ(dy ), (2.4) (0,∞)where b ≥ 0, and the Lévy measure λ satisﬁes the additionalrequirements λ(−∞, 0) = 0 and (y ∧ 1)λ(dy ) < ∞. (0,∞)Conversely, any mapping from Rd → C of the form (2.4) is the Lévysymbol of a subordinator.We call the pair (b, λ), the characteristics of the subordinator T . Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 37 / 56
134. 134. TheoremIf T is a subordinator then its Lévy symbol takes the form η(u) = ibu + (eiuy − 1)λ(dy ), (2.4) (0,∞)where b ≥ 0, and the Lévy measure λ satisﬁes the additionalrequirements λ(−∞, 0) = 0 and (y ∧ 1)λ(dy ) < ∞. (0,∞)Conversely, any mapping from Rd → C of the form (2.4) is the Lévysymbol of a subordinator.We call the pair (b, λ), the characteristics of the subordinator T . Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 37 / 56
135. 135. For each t ≥ 0, the map u → E(eiuT (t) ) can be analytically continued tothe region {iu, u > 0} and we then obtain the following expression forthe Laplace transform of the distribution E(e−uT (t) ) = e−tψ(u) , where ψ(u) = −η(iu) = bu + (1 − e−uy )λ(dy ) (2.5) (0,∞)for each t, u ≥ 0.This is much more useful for both theoretical and practical applicationthan the characteristic function.The function ψ is usually called the Laplace exponent of thesubordinator. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 38 / 56
136. 136. For each t ≥ 0, the map u → E(eiuT (t) ) can be analytically continued tothe region {iu, u > 0} and we then obtain the following expression forthe Laplace transform of the distribution E(e−uT (t) ) = e−tψ(u) , where ψ(u) = −η(iu) = bu + (1 − e−uy )λ(dy ) (2.5) (0,∞)for each t, u ≥ 0.This is much more useful for both theoretical and practical applicationthan the characteristic function.The function ψ is usually called the Laplace exponent of thesubordinator. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 38 / 56
137. 137. For each t ≥ 0, the map u → E(eiuT (t) ) can be analytically continued tothe region {iu, u > 0} and we then obtain the following expression forthe Laplace transform of the distribution E(e−uT (t) ) = e−tψ(u) , where ψ(u) = −η(iu) = bu + (1 − e−uy )λ(dy ) (2.5) (0,∞)for each t, u ≥ 0.This is much more useful for both theoretical and practical applicationthan the characteristic function.The function ψ is usually called the Laplace exponent of thesubordinator. Dave Applebaum (Shefﬁeld UK) Lecture 2 December 2011 38 / 56