Robust Pricing and Hedging of Options on Variance

Transcription

1 Robust Pricing and Hedging of Options on Variance Alexander Cox Jiajie Wang University of Bath Bachelier 21, Toronto

2 Financial Setting Option priced on an underlying asset S t Dynamics of S t unspecified, but suppose paths are continuous, and we see prices of call options at all strikes K and at maturity time T Assume for simplicity that all prices are discounted this won t affect our main results Under risk-neutral measure, S t should be a (local-)martingale, and we can recover the law of S T at time T from call prices C(K). (Breeden-Litzenberger)

3 Financial Setting Given these constraints, what can we say about market prices of other options? Two questions: What prices are consistent with a model? If there is no model, is there an arbitrage which works for every model in our class robust! Intuitively, understanding worst-case model should give insight into any corresponding arbitrage. Insight into hedge likely to be more important than pricing But... prices will indicate size of model-risk

4 Connection to Skorokhod Embeddings Under a risk-neutral measure, expect S t to be a local-martingale with known law at time T, say µ. Since S t is a continuous local martingale, we can write it as a time-change of a Brownian motion: S t = B At Now the law of B AT is known, and A T is a stopping time for B t Correspondence between possible price processes for S t and stopping times τ such that B τ µ. Problem of finding τ given µ is Skorokhod Embedding Problem Commonly look for worst-case or extremal solutions Surveys: Obłój, Hobson,...

5 Variance Options We may typically suppose a model for asset prices of the form: ds t S t = σ t dw t, where W t a Brownian motion. the volatility, σ t, is a predictable process Recent market innovations have led to asset volatility becoming an object of independent interest For example, a variance swap pays: T ( σ 2 t σ 2) dt where σ t is the strike. Dupire (1993) and Neuberger (1994) gave a simple replication strategy for such an option.

6 Variance Call A variance call is an option paying: ( ln S T K) + Let dx t = X t d W t for a suitable BM W t Can find a time change τ t such that S t = X τt, and so: dτ t = σ2 t S2 t S 2 t dt And hence ( (X τt,τ T ) = S T, T ) σu 2 du = (S T, ln S T ) More general options of the form: F( ln S T ).

7 Variance Call This suggests finding lower bound on price of variance call with given call prices is equivalent to: minimise: E(τ K) + subject to: L(X τ ) = µ where µ is a given law. Is there a Skorokhod Embedding which does this?

8 Root Construction β R R + a barrier if: B t (x, t) β = (x, s) β for all s t Given µ, exists β and a stopping time t τ = inf{t : (B t, t) β} which is an embedding. Minimises E(τ K) + over all (UI) embeddings Construction and optimality are subject of this talk Root (1969) Rost (1976)

9 Root Construction β R R + a barrier if: B t (x, t) β = (x, s) β for all s t Given µ, exists β and a stopping time t τ = inf{t : (B t, t) β} which is an embedding. Minimises E(τ K) + over all (UI) embeddings Construction and optimality are subject of this talk Root (1969) Rost (1976)

10 Variance Call Finding lower bound on price of variance call with given call prices is equivalent to: minimise: E(τ K) + subject to: L(X τ ) = µ where µ is a given law. This is (almost) the problem solved by Root s Barrier! Root proved this for X t a Brownian motion. Rost (1976) extended his solution to much more general processes, and proved optimality, which was conjectured by Kiefer. This connection to Variance options has been observed by a number of authors: Dupire ( 5), Carr & Lee ( 9), Hobson ( 9).

11 Variance Call Finding lower bound on price of variance call with given call prices is equivalent to: minimise: E(τ K) + subject to: L(X τ ) = µ where µ is a given law. This is (almost) the problem solved by Root s Barrier! Root proved this for X t a Brownian motion. Rost (1976) extended his solution to much more general processes, and proved optimality, which was conjectured by Kiefer. This connection to Variance options has been observed by a number of authors: Dupire ( 5), Carr & Lee ( 9), Hobson ( 9).

12 Variance Call Finding lower bound on price of variance call with given call prices is equivalent to: minimise: E(τ K) + subject to: L(X τ ) = µ where µ is a given law. This is (almost) the problem solved by Root s Barrier! Root proved this for X t a Brownian motion. Rost (1976) extended his solution to much more general processes, and proved optimality, which was conjectured by Kiefer. This connection to Variance options has been observed by a number of authors: Dupire ( 5), Carr & Lee ( 9), Hobson ( 9).

13 Questions Question This known connection leads to two important questions: 1. How do we find the Root stopping time? 2. Is there a corresponding hedging strategy? Dupire has given a connected free boundary problem Dupire, Carr & Lee have given strategies which sub/super-replicate the payoff, but are not necessarily optimal Hobson has given a formal, but not easily solved, condition a hedging strategy must satisfy.

14 Questions Question This known connection leads to two important questions: 1. How do we find the Root stopping time? 2. Is there a corresponding hedging strategy? Dupire has given a connected free boundary problem Dupire, Carr & Lee have given strategies which sub/super-replicate the payoff, but are not necessarily optimal Hobson has given a formal, but not easily solved, condition a hedging strategy must satisfy.

15 Questions Question This known connection leads to two important questions: 1. How do we find the Root stopping time? 2. Is there a corresponding hedging strategy? Dupire has given a connected free boundary problem Dupire, Carr & Lee have given strategies which sub/super-replicate the payoff, but are not necessarily optimal Hobson has given a formal, but not easily solved, condition a hedging strategy must satisfy.

16 Questions Question This known connection leads to two important questions: 1. How do we find the Root stopping time? 2. Is there a corresponding hedging strategy? Dupire has given a connected free boundary problem Dupire, Carr & Lee have given strategies which sub/super-replicate the payoff, but are not necessarily optimal Hobson has given a formal, but not easily solved, condition a hedging strategy must satisfy.

17 Root s Problem We want to connect Root s solution and the solution of a free-boundary problem. We will consider the case where X t = σ(x t )db t and σ is nice (smooth, Lipschitz, strictly positive on (, )). To make explicit the first, we define: Root s Problem (RP) Find an open set D R R + such that (R R + )/D is a barrier generating a UI stopping time τ D and X τd µ. Here we denote the exit time from D as τ D.

18 Free Boundary Problem Free Boundary Problem (FBP) To find a continuous function u : R [, ) R and a connected open set D : {(x, t), < t < R(x)} where R : R R + = [, ] is a lower semi-continuous function, and u C (R [, )) and u C 2,1 (D); u = 1 t 2 σ(x)2 2 u x 2, on D; u(x, ) = x S ; u(x, t) = U µ (x) = x y µ(dy), if t R(x), u(x, t) is concave with respect to x R. 2 u disappears on D. x 2

19 (RP) is equivalent to (FBP) An easy connection is then the following: Theorem Under some conditions on D, if D is a solution to (RP), we can find a solution to (FBP). In addition, this solution is unique. Sketch Proof of (RP) = (FBP) Simply take u(x, t) = E X t τd x. Resulting properties are mostly straightforward/follow from regularity of D C, and fact that, for (x, t) D C : E X t τd x = E X τd x.

20 (RP) is equivalent to (FBP) An easy connection is then the following: Theorem Under some conditions on D, if D is a solution to (RP), we can find a solution to (FBP). In addition, this solution is unique. Sketch Proof of (RP) = (FBP) Simply take u(x, t) = E X t τd x. Resulting properties are mostly straightforward/follow from regularity of D C, and fact that, for (x, t) D C : E X t τd x = E X τd x.

21 Optimality of Root s Barrier Rost s Result Given a function F which is convex, increasing, Root s barrier solves: minimise EF(τ) subject to: X τ µ τ a (UI) stopping time Want: A simple proof of this that identifies a financially meaningful hedging strategy.

22 Optimality of Root s Barrier Rost s Result Given a function F which is convex, increasing, Root s barrier solves: minimise EF(τ) subject to: X τ µ τ a (UI) stopping time Want: A simple proof of this that identifies a financially meaningful hedging strategy.

23 Optimality Write f(t) = F (t), define and M(x, t) = E (x,t) f(τ D ), x y M(z, ) Z(x) = 2 σ 2 dz dy, (z) so that in particular, Z (x) = 2σ 2 (x)m(x, ). And finally, let: G(x, t) = t M(x, s) ds Z(x).

24 Optimality Write f(t) = F (t), define and M(x, t) = E (x,t) f(τ D ), x y M(z, ) Z(x) = 2 σ 2 dz dy, (z) so that in particular, Z (x) = 2σ 2 (x)m(x, ). And finally, let: G(x, t) = t M(x, s) ds Z(x).

25 Optimality Write f(t) = F (t), define and M(x, t) = E (x,t) f(τ D ), x y M(z, ) Z(x) = 2 σ 2 dz dy, (z) so that in particular, Z (x) = 2σ 2 (x)m(x, ). And finally, let: G(x, t) = t M(x, s) ds Z(x).

26 Then there are two key results: Proposition ( Proof ) For all (x, t) R R + : G(x, t)+ R(x) Optimality (f(s) M(x, s)) ds + Z(x) F(t). Theorem ( Proof ) We have: G(X t, t) is a submartingale, and G(X t τd, t τ D ) is a martingale.

27 Then there are two key results: Proposition ( Proof ) For all (x, t) R R + : G(x, t)+ R(x) Optimality (f(s) M(x, s)) ds + Z(x) F(t). Theorem ( Proof ) We have: G(X t, t) is a submartingale, and G(X t τd, t τ D ) is a martingale.

28 Optimality We can now show optimality. Recall we had: G(x, t)+ R(x) (f(s) M(x, s)) ds + Z(x) F(t). But R(x) (f(s) M(x, s)) ds + Z(x) is just a function of x, so G(X t, t)+h(x t ) F(t).

29 Optimality We can now show optimality. Recall we had: G(x, t)+ R(x) (f(s) M(x, s)) ds + Z(x) F(t). But R(x) (f(s) M(x, s)) ds + Z(x) is just a function of x, so G(X t, t)+h(x t ) F(t).

30 Hedging Strategy Since G(X t, t) is a submartingale, there is a trading strategy which sub-replicates G(X t, t): G(S t, ln S t ) t G x (S r, ln S r ) σ 2 r ds r and H(X t ) can be replicated using the traded calls; moreover, in the case where τ = τ D, we get equality, so this is the best we can do.

31 Hedging Strategy Since G(X t, t) is a submartingale, there is a trading strategy which sub-replicates G(X t, t): G(S t, ln S t ) t G x (S r, ln S r ) σ 2 r ds r and H(X t ) can be replicated using the traded calls; moreover, in the case where τ = τ D, we get equality, so this is the best we can do.

32 Numerical implementation How good is the subhedge in practice? Take an underlying Heston process: ds t S t = r dt + v t db 1 t dv t = κ(θ v t )dt +ξ v t db 2 t where Bt 1, B2 t are correlated Brownian motions, correlation ρ. Compute Barrier and hedging strategies based on the corresponding call prices. How does the subhedging strategy behave under the true model? How does the strategy perform under another model?

33 Numerical Implementation ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 1.,ρ = 1.. Prices: actual , subhedge Asset Price and Exit from Barrier.28 Barrier Asset Price Asset Price Integrated Variance

34 Numerical Implementation ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 1.,ρ = 1.. Prices: actual , subhedge x 1 4 Payoff and Hedge against Integrated Variance Derivative Payoff Subhedging Portfolio Value Integrated Variance

35 Numerical Implementation ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 1.,ρ = 1.. Prices: actual , subhedge x 1 4 Integrated Variance, Payoff and Hedge against Time Derivative Payoff Subhedging Portfolio Value Time

36 Numerical Implementation ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 1.,ρ = 1.. Prices: actual , subhedge x 1 3 Hedging Gap Derivative Value Subhedge Value Time

37 Numerical Implementation ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 1.,ρ = 1.. Prices: actual , subhedge x 1 3 Dynamic and Static Hedge Components Dynamic Hedge Static Hedge 1.5 Value Time

38 Numerical Implementation ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 1.,ρ = 1.. Prices: actual , subhedge Distribution of Underhedge 2 Frequency Underhedge (Truncated at.1) x 1 4

39 Numerical Implementation: Incorrect model ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 4.,ρ =.5. Asset Price and Exit from Barrier.28 Barrier Asset Price Asset Price Integrated Variance

40 Numerical Implementation: Incorrect model ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 4.,ρ = x 1 3 Payoff and Hedge against Integrated Variance Derivative Payoff Subhedging Portfolio 1 Value Integrated Variance

41 Numerical Implementation: Incorrect model ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 4.,ρ = x 1 3 Integrated Variance, Payoff and Hedge against Time Derivative Payoff Subhedging Portfolio 1 Value Time

42 Numerical Implementation: Incorrect model ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 4.,ρ =.5. 1 x 1 4 Hedging Gap Derivative Value Subhedge 8 6 Value Time

43 Numerical Implementation: Incorrect model ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 4.,ρ = x 1 3 Dynamic and Static Hedge Components Dynamic Hedge Static Hedge 1 Value Time

44 Numerical Implementation: Incorrect model ( Payoff: 1 T 2. 2 σ t dt) Parameters: T = 1, r =.5, S =.2,σ 2 =.4,κ = 1,θ =.4,ξ = 4.,ρ =.5. 5 Distribution of Underhedge Frequency Underhedge (Truncated at.15)

45 Numerical Implementation: Variance Call ( ) T Payoff: σ2 t dt K subhedge = Prices: actual =.16, Parameters: T = 1, r =.5, S =.2, σ 2 =.174, κ = , θ =.354, ξ =.3877, ρ =.7165, K =.2.

46 Numerical Implementation: Variance Call ( T Payoff: σ2 t dt K) subhedge = Prices: actual =.16, Asset Price and Exit from Barrier.28 Barrier Asset Price Asset Price Integrated Variance

47 Numerical Implementation: Variance Call ( T Payoff: σ2 t dt K) subhedge = Prices: actual =.16, 5 x 1 3 Payoff and Hedge against Integrated Variance Derivative Payoff Subhedging Portfolio Value Integrated Variance

48 Numerical Implementation: Variance Call ( T Payoff: σ2 t dt K) subhedge = Prices: actual =.16,.25.2 Integrated Variance, Payoff and Hedge against Time Integrated Variance Derivative Payoff Subhedging Portfolio.15.1 Value Time

49 Numerical Implementation: Variance Call ( T Payoff: σ2 t dt K) subhedge = Prices: actual =.16, 16 x Hedging Gap Derivative Value Subhedge 12 1 Value Time

50 Numerical Implementation: Variance Call ( T Payoff: σ2 t dt K) subhedge = Prices: actual =.16,.2.15 Dynamic and Static Hedge Components Dynamic Hedge Static Hedge.1 Value Time

51 Numerical Implementation: Variance Call ( T Payoff: σ2 t dt K) subhedge = Prices: actual =.16, 4 Distribution of Underhedge Frequency Underhedge (Truncated at.5)

52 Conclusion Lower bounds on Pricing Variance options finding Root s barrier Equivalence between Root s Barrier and a Free Boundary Problem New proof of optimality, which allows explicit construction of a pathwise inequality Financial Interpretation: model-free sub-hedges for variance options.

53 Proof of Proposition If t R(x) then the left-hand side is: t f(s) ds R(x) And M(x, s) f(s). If t R(x), we get: t R(x) M(x, s) ds + t R(x) M(x, s) ds = F(t) f(s) ds = t R(x) = F(t). R(x) t f(s) ds + M(x, s) ds R(x) f(s) ds

54 Proof of Proposition If t R(x) then the left-hand side is: t f(s) ds R(x) And M(x, s) f(s). If t R(x), we get: t R(x) M(x, s) ds + t R(x) M(x, s) ds = F(t) f(s) ds = t R(x) = F(t). R(x) t f(s) ds + M(x, s) ds R(x) f(s) ds

55 Optimality Recalling that M(x, t) = E (x,t) f(τ D ), we have: { M(X s, s t + u) u t s E[M(X t, u) F s ] E[M(X t u, ) F s ] u t s. And by Itô: E[Z(X t ) Z(X s ) F s ] = Then it can be shown: t E[G(X t, t) F s ] G(X s, s). s M(X r, ) dr, s t.

56 Optimality Recalling that M(x, t) = E (x,t) f(τ D ), we have: { M(X s, s t + u) u t s E[M(X t, u) F s ] E[M(X t u, ) F s ] u t s. And by Itô: E[Z(X t ) Z(X s ) F s ] = Then it can be shown: t E[G(X t, t) F s ] G(X s, s). s M(X r, ) dr, s t.

57 Proof of Submartingale Condition E[G(X t, t) F s ] = t E[M(X t, u) F s ] du E[Z(X t ) F s ] = G(X s, s)+ s G(X s, s)+ s + t t s t E[M(X t, u) F s ] du M(X s, u) du E[Z(X t ) Z(X s ) F s ] t s M(X s, u) du E[M(X t u, ) F s ] du t M(X s, s t + u) du s E[M(X u, ) F s ] du

58 Proof of Submartingale Condition E[G(X t, t) F s ] = t E[M(X t, u) F s ] du E[Z(X t ) F s ] = G(X s, s)+ s G(X s, s)+ s + t t s t E[M(X t, u) F s ] du M(X s, u) du E[Z(X t ) Z(X s ) F s ] t s M(X s, u) du E[M(X t u, ) F s ] du t M(X s, s t + u) du s E[M(X u, ) F s ] du

59 Proof of Submartingale Condition E[G(X t, t) F s ] = t E[M(X t, u) F s ] du E[Z(X t ) F s ] = G(X s, s)+ s G(X s, s)+ s + t t s t E[M(X t, u) F s ] du M(X s, u) du E[Z(X t ) Z(X s ) F s ] t s M(X s, u) du E[M(X t u, ) F s ] du t M(X s, s t + u) du s E[M(X u, ) F s ] du

60 Proof of Submartingale Condition t E[G(X t, t) F s ] G(X s, s)+ t s s G(X s, s). s E[M(X u, ) F s ] du E[M(X u, ) F s ] du + M(X s, u) du A somewhat similar computation gives: on {s τ D }. s E[G(X t τd, t τ D ) F s ] = G(X s, s) M(X s, u) du

61 Proof of Submartingale Condition t E[G(X t, t) F s ] G(X s, s)+ t s s G(X s, s). s E[M(X u, ) F s ] du E[M(X u, ) F s ] du + M(X s, u) du A somewhat similar computation gives: on {s τ D }. s E[G(X t τd, t τ D ) F s ] = G(X s, s) M(X s, u) du

62 Proof of Submartingale Condition t E[G(X t, t) F s ] G(X s, s)+ t s s G(X s, s). s E[M(X u, ) F s ] du E[M(X u, ) F s ] du + M(X s, u) du A somewhat similar computation gives: on {s τ D }. s E[G(X t τd, t τ D ) F s ] = G(X s, s) M(X s, u) du