Equity correlations implied by index options: estimation and model uncertainty analysis

Transcription

1 1/18 : estimation and model analysis, EDHEC Business School (joint work with Rama COT) Modeling and managing financial risks Paris, January 2011

2 2/18 Outline 1 2 of multi-asset models Solution to the calibration problem 3 Sensitivity to input option prices Hedging model Monte Carlo Algorithm 4 Simulated data with Merton model Dow Jones Market data 5

3 Figure: Implied Volatility (IV) surface of the Eurostoxx 50 index. 3/18 Multi-asset models I An index is a weighted average of d stocks: d I(t) = x j S j(t) Two types of vanilla exist: Single-name call pay: (S j(t) K) + at maturity T. Index call pay: (I(T) K) + at maturity T. Benchmark : liquid for which, prices are given by the supply/demand j=1

4 4/18 Multi-asset models II Merton model driven by a common Poisson process ( t) t 0 : ( ) ds j(t) = µ j S j(t)dt + σ js j(t) db j(t) + S j(t ) e Y (t) j 1 d(t), with corr(b i(t),b j(t)) = ρ B i,j t, corr(y i,y j) = ρ J i,j CEV diffusion model: ds j(t) = r S j(t)dt + α js β j j db j(t), corr(b i(t),b j(t)) = ρ B i,j t Calibration: we observe prices Ci bid,ci ask of various benchmark option payoffs H i with i I and look for Q (or equivalently the model parameters) such that C bid i E Q [H i] C ask i for i I Goal: Joint calibration to index and single-name Index benchmark (η, ρ B i,j, ρ J i,j) or (ρ B i,j) ill-posed inverse problem if d = 30 (Dow-Jones index) large number of parameters

5 5/18 Calibration : random mixtures of multi-asset models Select multi-asset models Q 1,.., Q M(S) Consider random mixtures Q W 1 k=1 W kq k of these models, where (W 1,, W ) µ (prior distribution on weights) For any joint distribution ν of the weights, if we impose that 1 k=1 Eν [W k ] = 1 then E ν [Q W ] = 1 E ν [W k ]Q k M(S) k=1 defines an arbitrage-free pricing model. Finally, we impose that the pricing model verifies the following calibration constraints [ E ν 1 k=1 W k E Q k 0 [Hi] ] [Ci bid ; Ci ask ] of multi-asset models Solution to the calibration problem

6 6/18 Calibration : random mixtures of multi-asset models Calibration: we want the posterior ν, as close as possible to µ, and under which the calibration constraints are satisfied, i.e. Definition (Minimum entropy random mixture) [ ( )] dν dν inf E(ν µ ) := E µ ν P(R ) dµ ln dµ [ i I, Ci bid E ν 1 under the constraints ] W k E Q k [H i] k=1 [ E ν 1 C ask i ] W k = 1 k=1 of multi-asset models Solution to the calibration problem

7 Solution to the calibration problem Assuming (W k ) are µ -bounded, independent, and (Slater conditions): (a) : ν C s.t. E(ν µ ) < (b) : { ǫ > [ 0 s.t. ]1 ε,1 + ǫ[ E ν 1 ] } k=1 W k, ν P(R ). (c) : ν P(R [ ) s.t. i I Ci bid < E ν 1 ] [ k=1 W k E Q k [H i ] < Ci ask, and E ν 1 ] k=1 W k = 1. Theorem (Solution to the calibration problem) The primal problem has a unique solution ν P(R ) given by [ dν 1 exp dµ (w) = k=1 w ) k ( i I ] (λb i λ a i ) E Q k [H i] + λ 0 Z (λ ) (1) of multi-asset models Solution to the calibration problem where (λ 0, λ b, λ a ) R R 2 I + is the unique maximizer of { max (λ 0,λ b,λ a ) R R 2 I + λ 0 + i I (λ b i C bid i ( λ a i Ci ask ) ln Z (λ)) } (D) (D) is an unconstrained convex problem in finite dimension So, the dual (D) can be solved easily, and by injecting its solution λ into (1), we obtain ν 7/18

8 8/18 Motivation for considering random mixtures I If the weights are chosen to be deterministic, the model analysis which relies on the statistical to the problem (particularly on the posterior distribution of the weights) can no longer be carried out. Moreover, for deterministic weights, the choice of the objective function is less obvious. The use of the relative entropy as an objective function has several advantages: 1 It leads to a convex problem 2 The dual problem can be easily solved by gradient descent algorithm in finite dimension 3 The dimension of the dual problem does not depend on the number of model considered, but only on the number of constraints If duality cannot be exploited, the dimension of the optimization problem can be high. Indeed, in order to insure the existence of a solution, one would have to increase the number of models, which in turn would increase the dimension of the optimization problem. of multi-asset models Solution to the calibration problem

9 9/18 Motivation for considering random mixtures II Brigo and Mercurio (2002) propose log-normal mixtures with deterministic weights. They minimize the calibration error over the weights and the parameters of the models, which leads to a non-convex optimization problem! The Bayesian flavor of this relies on the fact that the weights are random with a distribution updated with market observation. Possible extensions of this static framework would require the weights to evolve randomly in time (e.g. Hidden Markov models). of multi-asset models Solution to the calibration problem

10 10/18 Sensitivity to benchmark option prices Knowing ν, the price of any exotic payoff X is given by: [ ] [ ] 1 Π(X) = E ν W k E Q k dν 1 [X] = E µ W dµ k E Q k [X] k=1 As in (Avellaneda et al, 2001), the price Π depends on the benchmark option prices (C i) i I via the Lagrange multipliers (λ i ) i I Theorem (Sensitivities to input option prices) k=1 Sensitivity to input option prices Hedging model Monte Carlo Algorithm By denoting i the sensitivity of the exotic price Π(X) to the input price C i, we have i = Π(X) = ( ) (H 1 1 ) ij Cov ν W k E Q k[x], 1 W k E Q k [H j ], C i j I ( k=1 k=1 where H ij = Cov ν 1 k=1 W ke Q k ) [H i], 1 k=1 W ke Q k [H j].

11 11/18 Hedging model The sensitivities ( i) i I correspond to the linear regression coefficients of 1 k=1 W ke Q k 1 [X] w.r.t. k=1 W ke Q k [H i] under ν. Therefore, the sensitivities ( i) i I solve ( [ 1 min {Var ν W k E Q k X β 0 ])} β ih i. β i I k=1 i ihi may be viewed as a control variate to reduce the variance of the MC estimator. The i s represent a static hedge that minimizes the exposure to model as measured by the variance. The calibration procedure provides the sensitivities with no additional computational cost. Sensitivity to input option prices Hedging model Monte Carlo Algorithm

12 12/18 MC algorithm for calibration/pricing/hedging 1 Generate reference models Q 1,.., Q 2 Compute model prices of index vanilla : E Q k [H i] for all i I and k = 1,..,. 3 Solve the dual problem. 4 Generate L IID samples W l = (W l 1,.., W l ), for l = 1,.., L of the model weights from the prior µ. 5 Adjust each weight W k with density dν k (W k ) = exp [ 1 W k ( i I (λb i dµ k Z k (λ ) )] λ a i ) E Q k [H i] + λ 0. Sensitivity to input option prices Hedging model Monte Carlo Algorithm 6 Compute model prices of the multi-asset exotic option: E Q k [X] for all k = 1,..,. 7 An arbitrage-free price of the exotic payoff X is given by 1 L L l=1 1 k=1 dν k dµ k (W l k) W l k π k L Π(X) 8 Without running additional Monte Carlo simulation, compute the sensitivity i of Π(X) w.r.t. C i, by linear regression.

13 13/18 Calibration procedure: We consider 4 classes of multi-asset models: (1) Merton model with intensity η = 1 (2) Merton model with intensity η = 2 (3) Merton model with intensity η = 3 (4) CEV model We calibrate these models to single-name vanilla available on the market Simulated data with Merton model Dow Jones Market data We use the random mixture to calibrate the remaining parameters: θ = {ρ B i,j} for the CEV, and θ = {ρ B i,j, ρ J i,j} for the Merton (using index vanilla ): (1) Choose a correlation structures θ = (ρ B i,j, ρj i,j ) for each multi-asset model Q 1,..., Q (2) Choose a prior distribution µ for the weights: IID uniform, IID truncated exponential,... (3) Solve the dual problem (D) with a gradient descent algorithm λ (4) From λ we get ν

14 Figure: Means of the weights under the posterior distribution. Data simulated with Merton (η = 1) 14/18 Simulated data with Merton model (intensity η = 1) 29.4 Mid IV Calibrated IV calibrated IV Bid IV Ask IV IV T K/F 0 IV T K/F 0 Figure: Calibrated IV of the single-names (left) and the index (right). Simulated data with Merton model Dow Jones Market data 12 CEV models Merton (η=1) models Merton (η=2) models Merton (η=3) models Model number

15 Figure: Biggest calibration error α(q) on the DJ index vanilla (nominal = 10 5 ) 15/18 Dow Jones market data IV calibrated IV Bid IV Ask IV T K/F 0 Figure: Calibrated IV of the DJ index. Terminal η ρ B ρ J correlation 1 27 % 93% 30% 5 14 % 95% 32% 5 22 % 77% 30% % 84% 29% % 85% 28% % 70% 28% % 81% 28% Table: 7 models appear to give DJ index vanilla prices well within the bid/ask spread Simulated data with Merton model Dow Jones Market data x CEV models Merton (η=1) models Merton (η=5) models Merton (η=10) models α(q) Model number

16 16/18 Quantifying model (Cont, 2006) We define a set Q of martingale measures on Ω (not necessary calibrated) Then, we penalize each element Q Q by the biggest pricing error on the benchmark H 1,..., H I using Q: bid α(q) = maxmax{(ci E Q [H i]) +,(E Q [H i] Ci ask ) + } i I ote: The nominal of H i is determined by the (maximal) quantity of the i-th option available to the investor. For a given payoff X, we compute the convex risk measures: { } π (X) = sup E Q [X] α(q) Q Q { } π (X) = π ( X) = inf Q Q E Q [X] + α(q) Simulated data with Merton model Dow Jones Market data and build a model measure ε as: ε(x) = π (X) π (X).

17 17/18 Model analysis Exotic Payoff Strike Confidence interval ǫ(x)/ Π 0 (X) X K for Π 0 (X) (I(T) K) [ ; ] 3.28% (I(T) K) [0.4591; ] 12.35% (I(T) K) + 1 max0 t T I(t)<B 138 [1.2661; ] 11.85% F T 0 F T 0 ( ) S min j (T) + 1 j d S j (0) K 0.8 [9.6993; ] 5.59% ( ) S max j (T) + 1 j d S j (0) K 1.1 [ ; ] 14.87% Simulated data with Merton model Dow Jones Market data Table: Model measures and 95% confidence intervals for model prices of different multi-asset exotic. The maturity of the is T = 11 weeks. The barrier for the knock-out option is B = 145.

18 18/18 We propose a method for constructing an arbitrage-free multi-asset pricing model which is consistent with a set of observed single- and multi-asset derivative prices. CEV models cannot be simultaneously calibrated to index and single-name vanilla Our results are consistent with previous findings, (Branger and Schlag, 2004), which point to common jumps as an explanation for the steepness of the index smile. Among the Merton models which can be perfectly calibrated, the common jump intensity and the Brownian correlations can be very different! onetheless, all calibrated models exhibit the same terminal correlation: ρ T σ iσ jρ B i,j + (m i m j + ρ J i,j vi v j)η i,j = σ 2 i + (mi 2 + v i)η σj 2 + (mj 2 + v j)η Low model for ATM and worst-of call option. High model for best-of, barrier, deep OTM call.

19 19/18 Comparison with the Weighted Monte Carlo Algorithm (Avellaneda et al, 2001) Our can match the Weighted Monte Carlo setting if: 1 The weights are deterministic 2 The reference probabilities Q k are chosen to be Dirac masses δ ωk, at specific market scenarios ω k Ω. It is immediate to see that δ ωk is no longer a martingale probability since it corresponds to a specific path. Therefore, many constraints need to be added to restore the martingality of the weighted average 1 k=1 w k δ ωk : ( 1) 1 constraints in discrete time (one for each pair of time) 2 2 Infinitely many in continuous time. The duality would therefore no longer be useful to transform the calibration procedure into a finite dimensional optimization problem. Hence, our can be seen as an arbitrage-free version of the Weighted Monte Carlo method.