Are stylized facts irrelevant in option-pricing?

Size: px
Start display at page:

Download "Are stylized facts irrelevant in option-pricing?"

Transcription

1 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 Institute of Applied Analysis and Stochastics E. Valkeila, Helsinki University of Technology Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 1 / 15

2 Outline 1. Market models, and self-financing strategies Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

3 Outline 1. Market models, and self-financing strategies 2. Pricing with replication, and arbitrage Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

4 Outline 1. Market models, and self-financing strategies 2. Pricing with replication, and arbitrage 3. Classical Black Scholes pricing model Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

5 Outline 1. Market models, and self-financing strategies 2. Pricing with replication, and arbitrage 3. Classical Black Scholes pricing model 4. Stylized facts Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

6 Outline 1. Market models, and self-financing strategies 2. Pricing with replication, and arbitrage 3. Classical Black Scholes pricing model 4. Stylized facts 5. Robust pricing models Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

7 Outline 1. Market models, and self-financing strategies 2. Pricing with replication, and arbitrage 3. Classical Black Scholes pricing model 4. Stylized facts 5. Robust pricing models 6. Forward integration Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

8 Outline 1. Market models, and self-financing strategies 2. Pricing with replication, and arbitrage 3. Classical Black Scholes pricing model 4. Stylized facts 5. Robust pricing models 6. Forward integration 7. Allowed strategies Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

9 Outline 1. Market models, and self-financing strategies 2. Pricing with replication, and arbitrage 3. Classical Black Scholes pricing model 4. Stylized facts 5. Robust pricing models 6. Forward integration 7. Allowed strategies 8. A no-arbitrage and robust-hedging result Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

10 Outline 1. Market models, and self-financing strategies 2. Pricing with replication, and arbitrage 3. Classical Black Scholes pricing model 4. Stylized facts 5. Robust pricing models 6. Forward integration 7. Allowed strategies 8. A no-arbitrage and robust-hedging result 9. Mixed models with stylized facts Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

11 Outline 1. Market models, and self-financing strategies 2. Pricing with replication, and arbitrage 3. Classical Black Scholes pricing model 4. Stylized facts 5. Robust pricing models 6. Forward integration 7. Allowed strategies 8. A no-arbitrage and robust-hedging result 9. Mixed models with stylized facts 10. A Message: Quadratic variation and volatility Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

12 Outline 1. Market models, and self-financing strategies 2. Pricing with replication, and arbitrage 3. Classical Black Scholes pricing model 4. Stylized facts 5. Robust pricing models 6. Forward integration 7. Allowed strategies 8. A no-arbitrage and robust-hedging result 9. Mixed models with stylized facts 10. A Message: Quadratic variation and volatility 11. Robustness beyond Black and Scholes Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

13 Outline 1. Market models, and self-financing strategies 2. Pricing with replication, and arbitrage 3. Classical Black Scholes pricing model 4. Stylized facts 5. Robust pricing models 6. Forward integration 7. Allowed strategies 8. A no-arbitrage and robust-hedging result 9. Mixed models with stylized facts 10. A Message: Quadratic variation and volatility 11. Robustness beyond Black and Scholes 12. References Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 2 / 15

14 1. Market models, and self-financing strategies Let C s0,+ be the space of continuous positive paths η : [0, T ] R with η(0) = s 0. A discounted market model is five-tuple (Ω, F, (S t ), (F t ), P) where the stock-price process S takes values in C s0,+. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 3 / 15

15 1. Market models, and self-financing strategies Let C s0,+ be the space of continuous positive paths η : [0, T ] R with η(0) = s 0. A discounted market model is five-tuple (Ω, F, (S t ), (F t ), P) where the stock-price process S takes values in C s0,+. Non-anticipating trading strategy Φ is self-financing if its wealth satisfies t V t (Φ, v 0 ; S) = v 0 + Φ r ds r, t [0, T ]. (1) 0 Here the economic notion self-financing is captured by the forward construction of the pathwise integral in (1). Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 3 / 15

16 2. Pricing with replication, and arbitrage An option is a mapping G : C s0,+ R +. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 4 / 15

17 2. Pricing with replication, and arbitrage An option is a mapping G : C s0,+ R +. The fair price of an option G is the capital v 0 of a hedging strategy Φ: G(S) = V T (Φ, v 0 ; S). Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 4 / 15

18 2. Pricing with replication, and arbitrage An option is a mapping G : C s0,+ R +. The fair price of an option G is the capital v 0 of a hedging strategy Φ: G(S) = V T (Φ, v 0 ; S). A strategy Φ is arbitrage (free lunch) if P [V T (Φ, 0; S) 0] = 1 and P [V T (Φ, 0; S) > 0] > 0. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 4 / 15

19 2. Pricing with replication, and arbitrage An option is a mapping G : C s0,+ R +. The fair price of an option G is the capital v 0 of a hedging strategy Φ: G(S) = V T (Φ, v 0 ; S). A strategy Φ is arbitrage (free lunch) if P [V T (Φ, 0; S) 0] = 1 and P [V T (Φ, 0; S) > 0] > 0. If the hedging capital v 0 is not unique them there is strong arbitrage. Also, note that replication and arbitrage are kind of opposite notions. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 4 / 15

20 3. Classical Black Scholes pricing model The Stock-price process is a geometric Brownian motion σ2 µt+σwt S t = s 0 e 2 t. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 5 / 15

21 3. Classical Black Scholes pricing model The Stock-price process is a geometric Brownian motion σ2 µt+σwt S t = s 0 e 2 t. With admissible strategies there is no arbitrage, and practically all options can be hedged. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 5 / 15

22 3. Classical Black Scholes pricing model The Stock-price process is a geometric Brownian motion σ2 µt+σwt S t = s 0 e 2 t. With admissible strategies there is no arbitrage, and practically all options can be hedged. Let R t be the log-return R t = log S t log S t 1 = σ W t + So, the log-returns are ) (µ σ2 t. 2 Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 5 / 15

23 3. Classical Black Scholes pricing model The Stock-price process is a geometric Brownian motion σ2 µt+σwt S t = s 0 e 2 t. With admissible strategies there is no arbitrage, and practically all options can be hedged. Let R t be the log-return R t = log S t log S t 1 = σ W t + So, the log-returns are 1 independent, ) (µ σ2 t. 2 Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 5 / 15

24 3. Classical Black Scholes pricing model The Stock-price process is a geometric Brownian motion σ2 µt+σwt S t = s 0 e 2 t. With admissible strategies there is no arbitrage, and practically all options can be hedged. Let R t be the log-return R t = log S t log S t 1 = σ W t + So, the log-returns are 1 independent, 2 Gaussian. ) (µ σ2 t. 2 Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 5 / 15

25 4. Stylized facts Dictionary definition: Stylized facts are observations that have been made in so many contexts that they are widely understood to be empirical truths, to which theories must fit. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 6 / 15

26 4. Stylized facts Dictionary definition: Stylized facts are observations that have been made in so many contexts that they are widely understood to be empirical truths, to which theories must fit. Some less-disputed stylized facts of log-returns R t : Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 6 / 15

27 4. Stylized facts Dictionary definition: Stylized facts are observations that have been made in so many contexts that they are widely understood to be empirical truths, to which theories must fit. Some less-disputed stylized facts of log-returns R t : 1 Long-range dependence: Cor[R 1, R t ] t β for some β < 1. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 6 / 15

28 4. Stylized facts Dictionary definition: Stylized facts are observations that have been made in so many contexts that they are widely understood to be empirical truths, to which theories must fit. Some less-disputed stylized facts of log-returns R t : 1 Long-range dependence: Cor[R 1, R t ] t β for some β < 1. 2 Heavy tails: P[ R t > x] x α 1, and maybe also P[R t > x] x α 2. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 6 / 15

29 4. Stylized facts Dictionary definition: Stylized facts are observations that have been made in so many contexts that they are widely understood to be empirical truths, to which theories must fit. Some less-disputed stylized facts of log-returns R t : 1 Long-range dependence: Cor[R 1, R t ] t β for some β < 1. 2 Heavy tails: P[ R t > x] x α 1, and maybe also P[R t > x] x α 2. 3 Gain/Loss asymmetry: P[ R t > x] >> P[R t > x] (does not apply FX-rates, obviously). Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 6 / 15

30 4. Stylized facts Dictionary definition: Stylized facts are observations that have been made in so many contexts that they are widely understood to be empirical truths, to which theories must fit. Some less-disputed stylized facts of log-returns R t : 1 Long-range dependence: Cor[R 1, R t ] t β for some β < 1. 2 Heavy tails: P[ R t > x] x α 1, and maybe also P[R t > x] x α 2. 3 Gain/Loss asymmetry: P[ R t > x] >> P[R t > x] (does not apply FX-rates, obviously). 4 Jumps. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 6 / 15

31 4. Stylized facts Dictionary definition: Stylized facts are observations that have been made in so many contexts that they are widely understood to be empirical truths, to which theories must fit. Some less-disputed stylized facts of log-returns R t : 1 Long-range dependence: Cor[R 1, R t ] t β for some β < 1. 2 Heavy tails: P[ R t > x] x α 1, and maybe also P[R t > x] x α 2. 3 Gain/Loss asymmetry: P[ R t > x] >> P[R t > x] (does not apply FX-rates, obviously). 4 Jumps. 5 Volatility clustering. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 6 / 15

32 4. Stylized facts Dictionary definition: Stylized facts are observations that have been made in so many contexts that they are widely understood to be empirical truths, to which theories must fit. Some less-disputed stylized facts of log-returns R t : 1 Long-range dependence: Cor[R 1, R t ] t β for some β < 1. 2 Heavy tails: P[ R t > x] x α 1, and maybe also P[R t > x] x α 2. 3 Gain/Loss asymmetry: P[ R t > x] >> P[R t > x] (does not apply FX-rates, obviously). 4 Jumps. 5 Volatility clustering. All of these stylized facts are in conflict with the Black Scholes model, and they are ill suited for semimartingale models. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 6 / 15

33 5. Robust pricing models We introduce a class of pricing models that is invariant to the Black Scholes model as long as option-pricing is considered. The class includes models with different stylized facts. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 7 / 15

34 5. Robust pricing models We introduce a class of pricing models that is invariant to the Black Scholes model as long as option-pricing is considered. The class includes models with different stylized facts. (Ω, F, (S t ), (F t ), P) is in the model class M σ if Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 7 / 15

35 5. Robust pricing models We introduce a class of pricing models that is invariant to the Black Scholes model as long as option-pricing is considered. The class includes models with different stylized facts. (Ω, F, (S t ), (F t ), P) is in the model class M σ if 1 S takes values in C s0,+, Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 7 / 15

36 5. Robust pricing models We introduce a class of pricing models that is invariant to the Black Scholes model as long as option-pricing is considered. The class includes models with different stylized facts. (Ω, F, (S t ), (F t ), P) is in the model class M σ if 1 S takes values in C s0,+, 2 the pathwise quadratic variation S of S is of the form d S t = σ 2 S 2 t dt, Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 7 / 15

37 5. Robust pricing models We introduce a class of pricing models that is invariant to the Black Scholes model as long as option-pricing is considered. The class includes models with different stylized facts. (Ω, F, (S t ), (F t ), P) is in the model class M σ if 1 S takes values in C s0,+, 2 the pathwise quadratic variation S of S is of the form d S t = σ 2 S 2 t dt, 3 for all ε > 0 and η C s0,+ we have the small ball property P [ S η < ε] > 0. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 7 / 15

38 6. Forward integration M σ contains non-semimartingale models. So, we cannot use Itô integrals. However, the forward integral is economically meaningful: Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 8 / 15

39 6. Forward integration M σ contains non-semimartingale models. So, we cannot use Itô integrals. However, the forward integral is economically meaningful: t 0 Φ r ds r is the P-a.s. forward-sum limit lim n t k πn t k t Φ tk 1 ( Stk S tk 1 ). Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 8 / 15

40 6. Forward integration M σ contains non-semimartingale models. So, we cannot use Itô integrals. However, the forward integral is economically meaningful: t 0 Φ r ds r is the P-a.s. forward-sum limit lim n t k πn t k t Φ tk 1 ( Stk S tk 1 ). Let u C 1,2,1 ([0, T ], R +, R m ) and Y 1,..., Y m be bounded variation processes. If S has pathwise quadratic variation then we have the Itô formula for u(t, S t, Yt 1,..., Yt m ): du = u u dt + t x ds u m x 2 d S + i=1 u y i dy i. This implies that the forward integral on the right hand side exists and has a continuous modification. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 8 / 15

41 7. Allowed strategies Even in the classical Black Scholes model one restricts to admissible strategies to exclude arbitrage. We shall restrict the admissible strategies a little more. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 9 / 15

42 7. Allowed strategies Even in the classical Black Scholes model one restricts to admissible strategies to exclude arbitrage. We shall restrict the admissible strategies a little more. A strategy Φ is allowed if it is admissible and of the form Φ t = ϕ (t, S t, g 1 (t, S),..., g m (t, S)), where ϕ C 1 ([0, T ] R + R m ) and g k s are hindsight factors: Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 9 / 15

43 7. Allowed strategies Even in the classical Black Scholes model one restricts to admissible strategies to exclude arbitrage. We shall restrict the admissible strategies a little more. A strategy Φ is allowed if it is admissible and of the form Φ t = ϕ (t, S t, g 1 (t, S),..., g m (t, S)), where ϕ C 1 ([0, T ] R + R m ) and g k s are hindsight factors: 1 g(t, η) = g(t, η) whenever η(r) = η(r) on r [0, t], 2 g(, η) is of bounded variation and continuous, 3 t 0 f (u)dg(u, η) t 0 f (u)dg(u, η) K f 1[0,t] η η Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 9 / 15

44 8. A no-arbitrage and robust-hedging result Theorem NA There is no arbitrage with allowed strategies. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 10 / 15

45 8. A no-arbitrage and robust-hedging result Theorem NA There is no arbitrage with allowed strategies. Theorem RH Suppose a continuous option G : C s0,+ R. If G( S) can be hedged in one model S M σ with an allowed strategy then G(S) can be hedged in any model S M σ. Moreover, the hedges are as strategies of the stock-path independent of the model. Moreover still, if ϕ is a functional hedge in one model then it is a functional hedge in all models. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 10 / 15

46 8. A no-arbitrage and robust-hedging result Theorem NA There is no arbitrage with allowed strategies. Theorem RH Suppose a continuous option G : C s0,+ R. If G( S) can be hedged in one model S M σ with an allowed strategy then G(S) can be hedged in any model S M σ. Moreover, the hedges are as strategies of the stock-path independent of the model. Moreover still, if ϕ is a functional hedge in one model then it is a functional hedge in all models. Corollary PDE In the Black Scholes model hedges for European, Asian, and lookback-options can be constructed by using the Black Scholes partial differential equation. These hedges hold for any model that is continuous, satisfies the small ball property, and has the same quadratic variation as the Black Scholes model. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 10 / 15

47 9. Mixed models with stylized facts (1/2) Consider a mixed model { S t = s 0 exp µt + σw t σ } 2 t + δbh t I α 1 t + I α 2 t, where B H is a fractional Brownian motion with Hurst index H > 0.5. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 11 / 15

48 9. Mixed models with stylized facts (1/2) Consider a mixed model { S t = s 0 exp µt + σw t σ } 2 t + δbh t I α 1 t + I α 2 t, where B H is a fractional Brownian motion with Hurst index H > 0.5. I α i s are integrated compound Poisson processes with positive heavy-tailed jumps: t I α i t = Uk i ds, 0 k:τk i s τ i k s are Poisson arrivals and P[Ui k > x] x α i. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 11 / 15

49 9. Mixed models with stylized facts (1/2) Consider a mixed model { S t = s 0 exp µt + σw t σ } 2 t + δbh t I α 1 t + I α 2 t, where B H is a fractional Brownian motion with Hurst index H > 0.5. I α i s are integrated compound Poisson processes with positive heavy-tailed jumps: t I α i t = Uk i ds, 0 k:τk i s τ i k s are Poisson arrivals and P[Ui k > x] x α i. W, B H, I α 1, and I α 2 are independent. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 11 / 15

50 9. Mixed models with stylized facts (2/2) Consider now stylized facts in the mixed model. 1 Long-range dependence: If I α i s are in L 2 then Cor[R 1, R t ] δ 2 H(2H 1)t 2H 2. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 12 / 15

51 9. Mixed models with stylized facts (2/2) Consider now stylized facts in the mixed model. 1 Long-range dependence: If I α i s are in L 2 then Cor[R 1, R t ] δ 2 H(2H 1)t 2H 2. 2 Heavy tails: P[ R t > x] x α 1 and P[R t > x] x α 2. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 12 / 15

52 9. Mixed models with stylized facts (2/2) Consider now stylized facts in the mixed model. 1 Long-range dependence: If I α i s are in L 2 then Cor[R 1, R t ] δ 2 H(2H 1)t 2H 2. 2 Heavy tails: P[ R t > x] x α 1 and P[R t > x] x α 2. 3 Gain/Loss asymmetry: Obvious if α 1 < α 2. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 12 / 15

53 9. Mixed models with stylized facts (2/2) Consider now stylized facts in the mixed model. 1 Long-range dependence: If I α i s are in L 2 then Cor[R 1, R t ] δ 2 H(2H 1)t 2H 2. 2 Heavy tails: P[ R t > x] x α 1 and P[R t > x] x α 2. 3 Gain/Loss asymmetry: Obvious if α 1 < α 2. 4 Jumps: No, but can you tell the difference between jumps and heavy tails from a discrete data? Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 12 / 15

54 9. Mixed models with stylized facts (2/2) Consider now stylized facts in the mixed model. 1 Long-range dependence: If I α i s are in L 2 then Cor[R 1, R t ] δ 2 H(2H 1)t 2H 2. 2 Heavy tails: P[ R t > x] x α 1 and P[R t > x] x α 2. 3 Gain/Loss asymmetry: Obvious if α 1 < α 2. 4 Jumps: No, but can you tell the difference between jumps and heavy tails from a discrete data? 5 Volatility clustering: What is volatility? If volatility is standard deviation, we can have any kind of volatility structure: E.g. change the Poisson arrivals to clustered arrivals. If volatility (squared) is the quadratic variation then it is fixed to constant σ 2. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 12 / 15

55 10. A Message: Quadratic variation and volatility The hedges depend only on the quadratic variation. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 13 / 15

56 10. A Message: Quadratic variation and volatility The hedges depend only on the quadratic variation. The quadratic variation is a path property. It tells nothing about the probabilistic structure of the stock-price (Black and Scholes tell us the mean return is irrelevant. We boldly suggest that probability is irrelevant, as far as option-pricing is concerned). Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 13 / 15

57 10. A Message: Quadratic variation and volatility The hedges depend only on the quadratic variation. The quadratic variation is a path property. It tells nothing about the probabilistic structure of the stock-price (Black and Scholes tell us the mean return is irrelevant. We boldly suggest that probability is irrelevant, as far as option-pricing is concerned). Don t be surprised if the implied and historical volatility do not agree: The latter is an estimate of the variance and the former is an estimate of the quadratic variation. In the Black Scholes model these notions coincide. But that is just luck! Indeed, consider a mixed fractional Black Scholes model R t = σ W t + δ B H t. Then quadratic variation or R t is σ 2, but the variance of R t is σ 2 + δ 2. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 13 / 15

58 10. A Message: Quadratic variation and volatility The hedges depend only on the quadratic variation. The quadratic variation is a path property. It tells nothing about the probabilistic structure of the stock-price (Black and Scholes tell us the mean return is irrelevant. We boldly suggest that probability is irrelevant, as far as option-pricing is concerned). Don t be surprised if the implied and historical volatility do not agree: The latter is an estimate of the variance and the former is an estimate of the quadratic variation. In the Black Scholes model these notions coincide. But that is just luck! Indeed, consider a mixed fractional Black Scholes model R t = σ W t + δ B H t. Then quadratic variation or R t is σ 2, but the variance of R t is σ 2 + δ 2. Don t use the historical volatility! Instead, use either implied volatility or estimate the quadratic variation (which may be difficult). Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 13 / 15

59 11. Robustness beyond Black and Scholes Instead of taking the Black Scholes model as reference we can consider models S t = s 0 exp X t, where X is continuous semimartingale with X 0 = 0. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 14 / 15

60 11. Robustness beyond Black and Scholes Instead of taking the Black Scholes model as reference we can consider models S t = s 0 exp X t, where X is continuous semimartingale with X 0 = 0. We can extend our robustness results to models S t = s 0 exp X t where X is continuous X 0 = 0, X and X have the same pathwise quadratic variation, and the support of P X 1 is the same as the support of P X 1. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 14 / 15

61 11. Robustness beyond Black and Scholes Instead of taking the Black Scholes model as reference we can consider models S t = s 0 exp X t, where X is continuous semimartingale with X 0 = 0. We can extend our robustness results to models S t = s 0 exp X t where X is continuous X 0 = 0, X and X have the same pathwise quadratic variation, and the support of P X 1 is the same as the support of P X 1. So, when option pricing is considered it does not matter whether S or S is the model. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 14 / 15

62 12. References Cont (2001): Empirical properties of asset returns: stylized facts and statistical issues. Föllmer (1981): Calcul d Itô sans probabilités. Schoenmakers, Kloeden (1999): Robust Option Replication for a Black Scholes Model Extended with Nondeterministic Trends. Russo, Vallois (1993): Forward, backward and symmetric stochastic integration. Sottinen, Valkeila (2003): On arbitrage and replication in the Fractional Black Scholes pricing model. This talk: Bender, Sottinen, Valkeila (2006): No-arbitrage pricing beyond semimartingales. Tommi Sottinen, University of Helsinki () Are stylized facts irrelevant in option-pricing? 15 / 15

Replication and Absence of Arbitrage in Non-Semimartingale Models

Replication 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 information

Non-semimartingales in finance

Non-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 information

How to hedge Asian options in fractional Black-Scholes model

How 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 information

How to hedge Asian options in fractional Black-Scholes model

How 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 information

M5MF6. Advanced Methods in Derivatives Pricing

M5MF6. 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 information

Fractional Brownian Motion as a Model in Finance

Fractional 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 information

Fractional Brownian Motion as a Model in Finance

Fractional 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 information

NEWCASTLE UNIVERSITY SCHOOL OF MATHEMATICS, STATISTICS & PHYSICS SEMESTER 1 SPECIMEN 2 MAS3904. Stochastic Financial Modelling. Time allowed: 2 hours

NEWCASTLE UNIVERSITY SCHOOL OF MATHEMATICS, STATISTICS & PHYSICS SEMESTER 1 SPECIMEN 2 MAS3904. Stochastic Financial Modelling. Time allowed: 2 hours NEWCASTLE UNIVERSITY SCHOOL OF MATHEMATICS, STATISTICS & PHYSICS SEMESTER 1 SPECIMEN 2 Stochastic Financial Modelling Time allowed: 2 hours Candidates should attempt all questions. Marks for each question

More information

Stochastic Processes and Stochastic Calculus - 9 Complete and Incomplete Market Models

Stochastic Processes and Stochastic Calculus - 9 Complete and Incomplete Market Models Stochastic Processes and Stochastic Calculus - 9 Complete and Incomplete Market Models Eni Musta Università degli studi di Pisa San Miniato - 16 September 2016 Overview 1 Self-financing portfolio 2 Complete

More information

EMPIRICAL EVIDENCE ON ARBITRAGE BY CHANGING THE STOCK EXCHANGE

EMPIRICAL EVIDENCE ON ARBITRAGE BY CHANGING THE STOCK EXCHANGE Advances and Applications in Statistics Volume, Number, This paper is available online at http://www.pphmj.com 9 Pushpa Publishing House EMPIRICAL EVIDENCE ON ARBITRAGE BY CHANGING THE STOCK EXCHANGE JOSÉ

More information

1.1 Basic Financial Derivatives: Forward Contracts and Options

1.1 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 information

Economathematics. Problem Sheet 1. Zbigniew Palmowski. Ws 2 dw s = 1 t

Economathematics. Problem Sheet 1. Zbigniew Palmowski. Ws 2 dw s = 1 t Economathematics Problem Sheet 1 Zbigniew Palmowski 1. Calculate Ee X where X is a gaussian random variable with mean µ and volatility σ >.. Verify that where W is a Wiener process. Ws dw s = 1 3 W t 3

More information

Hedging of Contingent Claims under Incomplete Information

Hedging 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 information

The Black-Scholes Model

The Black-Scholes Model The Black-Scholes Model Liuren Wu Options Markets Liuren Wu ( c ) The Black-Merton-Scholes Model colorhmoptions Markets 1 / 18 The Black-Merton-Scholes-Merton (BMS) model Black and Scholes (1973) and Merton

More information

The Black-Scholes Model

The Black-Scholes Model The Black-Scholes Model Liuren Wu Options Markets (Hull chapter: 12, 13, 14) Liuren Wu ( c ) The Black-Scholes Model colorhmoptions Markets 1 / 17 The Black-Scholes-Merton (BSM) model Black and Scholes

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 19 11/20/2013. Applications of Ito calculus to finance

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 19 11/20/2013. Applications of Ito calculus to finance MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.7J Fall 213 Lecture 19 11/2/213 Applications of Ito calculus to finance Content. 1. Trading strategies 2. Black-Scholes option pricing formula 1 Security

More information

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. 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 information

Dr. Maddah ENMG 625 Financial Eng g II 10/16/06

Dr. 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 information

Hedging Credit Derivatives in Intensity Based Models

Hedging Credit Derivatives in Intensity Based Models Hedging Credit Derivatives in Intensity Based Models PETER CARR Head of Quantitative Financial Research, Bloomberg LP, New York Director of the Masters Program in Math Finance, Courant Institute, NYU Stanford

More information

Pricing Dynamic Solvency Insurance and Investment Fund Protection

Pricing Dynamic Solvency Insurance and Investment Fund Protection Pricing Dynamic Solvency Insurance and Investment Fund Protection Hans U. Gerber and Gérard Pafumi Switzerland Abstract In the first part of the paper the surplus of a company is modelled by a Wiener process.

More information

Lecture Note 8 of Bus 41202, Spring 2017: Stochastic Diffusion Equation & Option Pricing

Lecture Note 8 of Bus 41202, Spring 2017: Stochastic Diffusion Equation & Option Pricing Lecture Note 8 of Bus 41202, Spring 2017: Stochastic Diffusion Equation & Option Pricing We shall go over this note quickly due to time constraints. Key concept: Ito s lemma Stock Options: A contract giving

More information

AMH4 - ADVANCED OPTION PRICING. Contents

AMH4 - 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 information

Lecture 17. The model is parametrized by the time period, δt, and three fixed constant parameters, v, σ and the riskless rate r.

Lecture 17. The model is parametrized by the time period, δt, and three fixed constant parameters, v, σ and the riskless rate r. Lecture 7 Overture to continuous models Before rigorously deriving the acclaimed Black-Scholes pricing formula for the value of a European option, we developed a substantial body of material, in continuous

More information

BROWNIAN MOTION Antonella Basso, Martina Nardon

BROWNIAN 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 information

Hedging with Life and General Insurance Products

Hedging with Life and General Insurance Products Hedging with Life and General Insurance Products June 2016 2 Hedging with Life and General Insurance Products Jungmin Choi Department of Mathematics East Carolina University Abstract In this study, a hybrid

More information

Rough volatility models: When population processes become a new tool for trading and risk management

Rough volatility models: When population processes become a new tool for trading and risk management Rough volatility models: When population processes become a new tool for trading and risk management Omar El Euch and Mathieu Rosenbaum École Polytechnique 4 October 2017 Omar El Euch and Mathieu Rosenbaum

More information

Stochastic Calculus, Application of Real Analysis in Finance

Stochastic 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 information

STOCHASTIC CALCULUS AND BLACK-SCHOLES MODEL

STOCHASTIC 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 information

CONTINUOUS TIME PRICING AND TRADING: A REVIEW, WITH SOME EXTRA PIECES

CONTINUOUS TIME PRICING AND TRADING: A REVIEW, WITH SOME EXTRA PIECES CONTINUOUS TIME PRICING AND TRADING: A REVIEW, WITH SOME EXTRA PIECES THE SOURCE OF A PRICE IS ALWAYS A TRADING STRATEGY SPECIAL CASES WHERE TRADING STRATEGY IS INDEPENDENT OF PROBABILITY MEASURE COMPLETENESS,

More information

FINANCIAL OPTION ANALYSIS HANDOUTS

FINANCIAL OPTION ANALYSIS HANDOUTS FINANCIAL OPTION ANALYSIS HANDOUTS 1 2 FAIR PRICING There is a market for an object called S. The prevailing price today is S 0 = 100. At this price the object S can be bought or sold by anyone for any

More information

Change of Measure (Cameron-Martin-Girsanov Theorem)

Change of Measure (Cameron-Martin-Girsanov Theorem) Change of Measure Cameron-Martin-Girsanov Theorem Radon-Nikodym derivative: Taking again our intuition from the discrete world, we know that, in the context of option pricing, we need to price the claim

More information

Stochastic modelling of electricity markets Pricing Forwards and Swaps

Stochastic modelling of electricity markets Pricing Forwards and Swaps Stochastic modelling of electricity markets Pricing Forwards and Swaps Jhonny Gonzalez School of Mathematics The University of Manchester Magical books project August 23, 2012 Clip for this slide Pricing

More information

MATH3075/3975 FINANCIAL MATHEMATICS TUTORIAL PROBLEMS

MATH3075/3975 FINANCIAL MATHEMATICS TUTORIAL PROBLEMS MATH307/37 FINANCIAL MATHEMATICS TUTORIAL PROBLEMS School of Mathematics and Statistics Semester, 04 Tutorial problems should be used to test your mathematical skills and understanding of the lecture material.

More information

Beyond the Black-Scholes-Merton model

Beyond the Black-Scholes-Merton model Econophysics Lecture Leiden, November 5, 2009 Overview 1 Limitations of the Black-Scholes model 2 3 4 Limitations of the Black-Scholes model Black-Scholes model Good news: it is a nice, well-behaved model

More information

The Black-Scholes Model

The Black-Scholes Model IEOR E4706: Foundations of Financial Engineering c 2016 by Martin Haugh The Black-Scholes Model In these notes we will use Itô s Lemma and a replicating argument to derive the famous Black-Scholes formula

More information

We discussed last time how the Girsanov theorem allows us to reweight probability measures to change the drift in an SDE.

We discussed last time how the Girsanov theorem allows us to reweight probability measures to change the drift in an SDE. Risk Neutral Pricing Thursday, May 12, 2011 2:03 PM We discussed last time how the Girsanov theorem allows us to reweight probability measures to change the drift in an SDE. This is used to construct a

More information

In chapter 5, we approximated the Black-Scholes model

In chapter 5, we approximated the Black-Scholes model Chapter 7 The Black-Scholes Equation In chapter 5, we approximated the Black-Scholes model ds t /S t = µ dt + σ dx t 7.1) with a suitable Binomial model and were able to derive a pricing formula for option

More information

From Discrete Time to Continuous Time Modeling

From 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 information

Risk Minimization Control for Beating the Market Strategies

Risk Minimization Control for Beating the Market Strategies Risk Minimization Control for Beating the Market Strategies Jan Večeř, Columbia University, Department of Statistics, Mingxin Xu, Carnegie Mellon University, Department of Mathematical Sciences, Olympia

More information

Optimal Hedging of Variance Derivatives. John Crosby. Centre for Economic and Financial Studies, Department of Economics, Glasgow University

Optimal Hedging of Variance Derivatives. John Crosby. Centre for Economic and Financial Studies, Department of Economics, Glasgow University Optimal Hedging of Variance Derivatives John Crosby Centre for Economic and Financial Studies, Department of Economics, Glasgow University Presentation at Baruch College, in New York, 16th November 2010

More information

Stochastic Differential equations as applied to pricing of options

Stochastic Differential equations as applied to pricing of options Stochastic Differential equations as applied to pricing of options By Yasin LUT Supevisor:Prof. Tuomo Kauranne December 2010 Introduction Pricing an European call option Conclusion INTRODUCTION A stochastic

More information

The stochastic calculus

The 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 information

Advanced Stochastic Processes.

Advanced Stochastic Processes. Advanced Stochastic Processes. David Gamarnik LECTURE 16 Applications of Ito calculus to finance Lecture outline Trading strategies Black Scholes option pricing formula 16.1. Security price processes,

More information

An overview of some financial models using BSDE with enlarged filtrations

An 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 information

The Black-Scholes Equation using Heat Equation

The 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 information

KØBENHAVNS UNIVERSITET (Blok 2, 2011/2012) Naturvidenskabelig kandidateksamen Continuous time finance (FinKont) TIME ALLOWED : 3 hours

KØBENHAVNS UNIVERSITET (Blok 2, 2011/2012) Naturvidenskabelig kandidateksamen Continuous time finance (FinKont) TIME ALLOWED : 3 hours This question paper consists of 3 printed pages FinKont KØBENHAVNS UNIVERSITET (Blok 2, 211/212) Naturvidenskabelig kandidateksamen Continuous time finance (FinKont) TIME ALLOWED : 3 hours This exam paper

More information

Illiquidity, Credit risk and Merton s model

Illiquidity, Credit risk and Merton s model Illiquidity, Credit risk and Merton s model (joint work with J. Dong and L. Korobenko) A. Deniz Sezer University of Calgary April 28, 2016 Merton s model of corporate debt A corporate bond is a contingent

More information

Stochastic Dynamical Systems and SDE s. An Informal Introduction

Stochastic 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 information

King s College London

King 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 information

Lecture 11: Ito Calculus. Tuesday, October 23, 12

Lecture 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 information

A Moment Matching Approach To The Valuation Of A Volume Weighted Average Price Option

A Moment Matching Approach To The Valuation Of A Volume Weighted Average Price Option A Moment Matching Approach To The Valuation Of A Volume Weighted Average Price Option Antony Stace Department of Mathematics and MASCOS University of Queensland 15th October 2004 AUSTRALIAN RESEARCH COUNCIL

More information

- 1 - **** d(lns) = (µ (1/2)σ 2 )dt + σdw t

- 1 - **** d(lns) = (µ (1/2)σ 2 )dt + σdw t - 1 - **** These answers indicate the solutions to the 2014 exam questions. Obviously you should plot graphs where I have simply described the key features. It is important when plotting graphs to label

More information

Pricing Volatility Derivatives with General Risk Functions. Alejandro Balbás University Carlos III of Madrid

Pricing Volatility Derivatives with General Risk Functions. Alejandro Balbás University Carlos III of Madrid Pricing Volatility Derivatives with General Risk Functions Alejandro Balbás University Carlos III of Madrid alejandro.balbas@uc3m.es Content Introduction. Describing volatility derivatives. Pricing and

More information

SYSM 6304: Risk and Decision Analysis Lecture 6: Pricing and Hedging Financial Derivatives

SYSM 6304: Risk and Decision Analysis Lecture 6: Pricing and Hedging Financial Derivatives SYSM 6304: Risk and Decision Analysis Lecture 6: Pricing and Hedging Financial Derivatives M. Vidyasagar Cecil & Ida Green Chair The University of Texas at Dallas Email: M.Vidyasagar@utdallas.edu October

More information

Option pricing in the stochastic volatility model of Barndorff-Nielsen and Shephard

Option pricing in the stochastic volatility model of Barndorff-Nielsen and Shephard Option pricing in the stochastic volatility model of Barndorff-Nielsen and Shephard Indifference pricing and the minimal entropy martingale measure Fred Espen Benth Centre of Mathematics for Applications

More information

Basic Concepts in Mathematical Finance

Basic Concepts in Mathematical Finance Chapter 1 Basic Concepts in Mathematical Finance In this chapter, we give an overview of basic concepts in mathematical finance theory, and then explain those concepts in very simple cases, namely in the

More information

Application of Stochastic Calculus to Price a Quanto Spread

Application of Stochastic Calculus to Price a Quanto Spread Application of Stochastic Calculus to Price a Quanto Spread Christopher Ting http://www.mysmu.edu/faculty/christophert/ Algorithmic Quantitative Finance July 15, 2017 Christopher Ting July 15, 2017 1/33

More information

Mathematics of Finance Final Preparation December 19. To be thoroughly prepared for the final exam, you should

Mathematics of Finance Final Preparation December 19. To be thoroughly prepared for the final exam, you should Mathematics of Finance Final Preparation December 19 To be thoroughly prepared for the final exam, you should 1. know how to do the homework problems. 2. be able to provide (correct and complete!) definitions

More information

Pricing and hedging with rough-heston models

Pricing and hedging with rough-heston models Pricing and hedging with rough-heston models Omar El Euch, Mathieu Rosenbaum Ecole Polytechnique 1 January 216 El Euch, Rosenbaum Pricing and hedging with rough-heston models 1 Table of contents Introduction

More information

Hedging under Arbitrage

Hedging 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 information

Dependence Structure and Extreme Comovements in International Equity and Bond Markets

Dependence Structure and Extreme Comovements in International Equity and Bond Markets Dependence Structure and Extreme Comovements in International Equity and Bond Markets René Garcia Edhec Business School, Université de Montréal, CIRANO and CIREQ Georges Tsafack Suffolk University Measuring

More information

Lecture 8: The Black-Scholes theory

Lecture 8: The Black-Scholes theory Lecture 8: The Black-Scholes theory Dr. Roman V Belavkin MSO4112 Contents 1 Geometric Brownian motion 1 2 The Black-Scholes pricing 2 3 The Black-Scholes equation 3 References 5 1 Geometric Brownian motion

More information

The Use of Importance Sampling to Speed Up Stochastic Volatility Simulations

The Use of Importance Sampling to Speed Up Stochastic Volatility Simulations The Use of Importance Sampling to Speed Up Stochastic Volatility Simulations Stan Stilger June 6, 1 Fouque and Tullie use importance sampling for variance reduction in stochastic volatility simulations.

More information

Risk-Neutral Valuation

Risk-Neutral Valuation N.H. Bingham and Rüdiger Kiesel Risk-Neutral Valuation Pricing and Hedging of Financial Derivatives W) Springer Contents 1. Derivative Background 1 1.1 Financial Markets and Instruments 2 1.1.1 Derivative

More information

Lecture 1: Lévy processes

Lecture 1: Lévy processes Lecture 1: Lévy processes A. E. Kyprianou Department of Mathematical Sciences, University of Bath 1/ 22 Lévy processes 2/ 22 Lévy processes A process X = {X t : t 0} defined on a probability space (Ω,

More information

Financial Derivatives Section 5

Financial Derivatives Section 5 Financial Derivatives Section 5 The Black and Scholes Model Michail Anthropelos anthropel@unipi.gr http://web.xrh.unipi.gr/faculty/anthropelos/ University of Piraeus Spring 2018 M. Anthropelos (Un. of

More information

Statistical methods for financial models driven by Lévy processes

Statistical methods for financial models driven by Lévy processes Statistical methods for financial models driven by Lévy processes José Enrique Figueroa-López Department of Statistics, Purdue University PASI Centro de Investigación en Matemátics (CIMAT) Guanajuato,

More information

CRRAO Advanced Institute of Mathematics, Statistics and Computer Science (AIMSCS) Research Report. B. L. S. Prakasa Rao

CRRAO Advanced Institute of Mathematics, Statistics and Computer Science (AIMSCS) Research Report. B. L. S. Prakasa Rao CRRAO Advanced Institute of Mathematics, Statistics and Computer Science (AIMSCS) Research Report Author (s): B. L. S. Prakasa Rao Title of the Report: Option pricing for processes driven by mixed fractional

More information

Local vs Non-local Forward Equations for Option Pricing

Local 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 information

Dynamic Portfolio Choice II

Dynamic Portfolio Choice II Dynamic Portfolio Choice II Dynamic Programming Leonid Kogan MIT, Sloan 15.450, Fall 2010 c Leonid Kogan ( MIT, Sloan ) Dynamic Portfolio Choice II 15.450, Fall 2010 1 / 35 Outline 1 Introduction to Dynamic

More information

last problem outlines how the Black Scholes PDE (and its derivation) may be modified to account for the payment of stock dividends.

last problem outlines how the Black Scholes PDE (and its derivation) may be modified to account for the payment of stock dividends. 224 10 Arbitrage and SDEs last problem outlines how the Black Scholes PDE (and its derivation) may be modified to account for the payment of stock dividends. 10.1 (Calculation of Delta First and Finest

More information

Forwards and Futures. Chapter Basics of forwards and futures Forwards

Forwards and Futures. Chapter Basics of forwards and futures Forwards Chapter 7 Forwards and Futures Copyright c 2008 2011 Hyeong In Choi, All rights reserved. 7.1 Basics of forwards and futures The financial assets typically stocks we have been dealing with so far are the

More information

Equivalence between Semimartingales and Itô Processes

Equivalence 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 information

Limit 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 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 information

Pricing in markets modeled by general processes with independent increments

Pricing in markets modeled by general processes with independent increments Pricing in markets modeled by general processes with independent increments Tom Hurd Financial Mathematics at McMaster www.phimac.org Thanks to Tahir Choulli and Shui Feng Financial Mathematics Seminar

More information

( ) since this is the benefit of buying the asset at the strike price rather

( ) since this is the benefit of buying the asset at the strike price rather Review of some financial models for MAT 483 Parity and Other Option Relationships The basic parity relationship for European options with the same strike price and the same time to expiration is: C( KT

More information

LECTURE 4: BID AND ASK HEDGING

LECTURE 4: BID AND ASK HEDGING LECTURE 4: BID AND ASK HEDGING 1. Introduction One of the consequences of incompleteness is that the price of derivatives is no longer unique. Various strategies for dealing with this exist, but a useful

More information

3.1 Itô s Lemma for Continuous Stochastic Variables

3.1 Itô s Lemma for Continuous Stochastic Variables Lecture 3 Log Normal Distribution 3.1 Itô s Lemma for Continuous Stochastic Variables Mathematical Finance is about pricing (or valuing) financial contracts, and in particular those contracts which depend

More information

The 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

The 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 information

Financial Engineering. Craig Pirrong Spring, 2006

Financial Engineering. Craig Pirrong Spring, 2006 Financial Engineering Craig Pirrong Spring, 2006 March 8, 2006 1 Levy Processes Geometric Brownian Motion is very tractible, and captures some salient features of speculative price dynamics, but it is

More information

AN ANALYTICALLY TRACTABLE UNCERTAIN VOLATILITY MODEL

AN ANALYTICALLY TRACTABLE UNCERTAIN VOLATILITY MODEL AN ANALYTICALLY TRACTABLE UNCERTAIN VOLATILITY MODEL FABIO MERCURIO BANCA IMI, MILAN http://www.fabiomercurio.it 1 Stylized facts Traders use the Black-Scholes formula to price plain-vanilla options. An

More information

Risk Neutral Measures

Risk Neutral Measures CHPTER 4 Risk Neutral Measures Our aim in this section is to show how risk neutral measures can be used to price derivative securities. The key advantage is that under a risk neutral measure the discounted

More information

Volatility. Roberto Renò. 2 March 2010 / Scuola Normale Superiore. Dipartimento di Economia Politica Università di Siena

Volatility. 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 information

Bluff Your Way Through Black-Scholes

Bluff Your Way Through Black-Scholes Bluff our Way Through Black-Scholes Saurav Sen December 000 Contents What is Black-Scholes?.............................. 1 The Classical Black-Scholes Model....................... 1 Some Useful Background

More information

Black-Scholes Option Pricing

Black-Scholes Option Pricing Black-Scholes Option Pricing The pricing kernel furnishes an alternate derivation of the Black-Scholes formula for the price of a call option. Arbitrage is again the foundation for the theory. 1 Risk-Free

More information

Math 239 Homework 1 solutions

Math 239 Homework 1 solutions Math 239 Homework 1 solutions Question 1. Delta hedging simulation. (a) Means, standard deviations and histograms are found using HW1Q1a.m with 100,000 paths. In the case of weekly rebalancing: mean =

More information

King s College London

King 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 information

1 The continuous time limit

1 The continuous time limit Derivative Securities, Courant Institute, Fall 2008 http://www.math.nyu.edu/faculty/goodman/teaching/derivsec08/index.html Jonathan Goodman and Keith Lewis Supplementary notes and comments, Section 3 1

More information

(1) Consider a European call option and a European put option on a nondividend-paying stock. You are given:

(1) Consider a European call option and a European put option on a nondividend-paying stock. You are given: (1) Consider a European call option and a European put option on a nondividend-paying stock. You are given: (i) The current price of the stock is $60. (ii) The call option currently sells for $0.15 more

More information

Chapter 15: Jump Processes and Incomplete Markets. 1 Jumps as One Explanation of Incomplete Markets

Chapter 15: Jump Processes and Incomplete Markets. 1 Jumps as One Explanation of Incomplete Markets Chapter 5: Jump Processes and Incomplete Markets Jumps as One Explanation of Incomplete Markets It is easy to argue that Brownian motion paths cannot model actual stock price movements properly in reality,

More information

Aspects of Financial Mathematics:

Aspects of Financial Mathematics: Aspects of Financial Mathematics: Options, Derivatives, Arbitrage, and the Black-Scholes Pricing Formula J. Robert Buchanan Millersville University of Pennsylvania email: Bob.Buchanan@millersville.edu

More information

Introduction to Stochastic Calculus With Applications

Introduction 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 information

Optimal trading strategies under arbitrage

Optimal trading strategies under arbitrage Optimal trading strategies under arbitrage Johannes Ruf Columbia University, Department of Statistics The Third Western Conference in Mathematical Finance November 14, 2009 How should an investor trade

More information

Stochastic Volatility (Working Draft I)

Stochastic Volatility (Working Draft I) Stochastic Volatility (Working Draft I) Paul J. Atzberger General comments or corrections should be sent to: paulatz@cims.nyu.edu 1 Introduction When using the Black-Scholes-Merton model to price derivative

More information

INSTITUTE OF ACTUARIES OF INDIA

INSTITUTE OF ACTUARIES OF INDIA INSTITUTE OF ACTUARIES OF INDIA EXAMINATIONS 23 rd March 2017 Subject CT8 Financial Economics Time allowed: Three Hours (10.30 13.30 Hours) Total Marks: 100 INSTRUCTIONS TO THE CANDIDATES 1. Please read

More information

Weak Reflection Principle and Static Hedging of Barrier Options

Weak Reflection Principle and Static Hedging of Barrier Options Weak Reflection Principle and Static Hedging of Barrier Options Sergey Nadtochiy Department of Mathematics University of Michigan Apr 2013 Fields Quantitative Finance Seminar Fields Institute, Toronto

More information

Constructing Markov models for barrier options

Constructing 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 information

Logarithmic derivatives of densities for jump processes

Logarithmic 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 information

θ(t ) = T f(0, T ) + σ2 T

θ(t ) = T f(0, T ) + σ2 T 1 Derivatives Pricing and Financial Modelling Andrew Cairns: room M3.08 E-mail: A.Cairns@ma.hw.ac.uk Tutorial 10 1. (Ho-Lee) Let X(T ) = T 0 W t dt. (a) What is the distribution of X(T )? (b) Find E[exp(

More information

Recent Advances in Fractional Stochastic Volatility Models

Recent Advances in Fractional Stochastic Volatility Models Recent Advances in Fractional Stochastic Volatility Models Alexandra Chronopoulou Industrial & Enterprise Systems Engineering University of Illinois at Urbana-Champaign IPAM National Meeting of Women in

More information