# Hedging Barriers. Liuren Wu. Zicklin School of Business, Baruch College (

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1 Hedging Barriers Liuren Wu Zicklin School of Business, Baruch College ( Based on joint work with Peter Carr (Bloomberg) Modeling and Hedging Using FX Options, March 13, 2008 Liuren Wu Hedging Barriers Risk, March 13, / 27

2 Hedging barriers: Overview Barrier options are actively traded in the OTC currency market. Hedging barriers faces two major challenges: Compared to delta hedging of vanilla options, delta hedging of barriers is subject to larger errors in practice. The errors are larger when the delta varies more over time. Reliable barrier option quotes are hardly available, making performance comparison across different models/hedging strategies difficult. The traditional approach: Simulation based on an assumed environment. Liuren Wu Hedging Barriers Risk, March 13, / 27

3 Comparing vanilla call to down-out call Option values and delta (under Black-Scholes) Call Down and out Call Call Down and out Call Option value Delta S S Down-and-out: The option has zero value once the lower barrier is crossed. Delta drops to zero once the barrier is crossed. Model parameters: r = q = 0, σ = 20%, K = 100, t = 6/12, L=90. Liuren Wu Hedging Barriers Risk, March 13, / 27

4 Comparing vanilla put to down-out put Option values and delta (under Black-Scholes) Put Down and out Put Option value Delta S Put Down and out Put S The terminal payoff (conditional on survival) increases with lowering spot, but the down-out feature works against it. Model parameters: r = q = 0, σ = 20%, K = 100, t = 6/12, L=90. Liuren Wu Hedging Barriers Risk, March 13, / 27

5 Hedging barriers: Objective I propose an approach, with which we can systematically examine the barrier hedging effectiveness of several strategies empirically based on historical paths on the FX and vanilla FX option prices, but without resorting to barrier option quotes. Two objectives What is the best hedging strategy really? How many dimensions to hedge. Which models to use. How many dimensions of risk do we need to model? Liuren Wu Hedging Barriers Risk, March 13, / 27

6 Tracking the P&L of a hedging strategy Consider N hedging strategies or models. At each date t, sell the target instrument for an unknown price P t, Put on hedging positions on the underlying currency and/or vanilla currency options (straddles, risk reversals, butterflies). Let {Ht n } N n=1 denotes the hedging cost of the N strategies, observable based on the market quotes on currency prices and vanilla option prices. Track the aggregate hedging cost to maturity, {H n t+τ } N n=1. The terminal cost of selling the barrier, P t+τ, is observable based on the underlying currency path, even though the barrier price is not before expiry. Terminal P&L of hedging a barrier sold at time t with strategy n is: PL n t = P t e rτ P t+τ H n t+τ Compare the time-series variation of PL n t for different strategies. Issue: How to calculate PL n without observing the barrier selling price P t? Liuren Wu Hedging Barriers Risk, March 13, / 27

7 Hedging performance comparison without observing the target instrument price We consider two solutions, each with different underlying assumptions: Compute the P&L excluding the barrier sales revenue: Replace PL n t = P t e rτ P t+τ H n t+τ with PL n t = P t+τ H n t+τ. If P t is constant over time, Var(PL n t ) = Var( PL n t ). More generally, for hedging strategy m, n, Var(PL m t ) > Var(PL n t ) if Var( PL m t ) > Var( PL n t ) and the covariance terms do not dominate: Cov(PL m t, P t e rτ ) Cov(PL n t, P t e rτ ) < Var(PL m t ) Var(PL n t ). Choose a barrier contract whose value does not vary much over time. Compute the P&L using the model fair value as the selling price. If there exists a model that generates the perfect hedging strategy, then the P&L computed based on the fair value of the model should always be zero. To the point that the P&L is not zero and varies over time, it reflects both the pricing error and the hedging ineffectiveness. Good news: Both solutions generate the same ranking in terms of hedging performance. Liuren Wu Hedging Barriers Risk, March 13, / 27

8 Hedging exercise design Target: One-touch paid at expiry with a lower barrier (OTPE). Maturity: 30 calendar days. Barrier (L) set to 25 Balck-Scholes put delta. Considering barrier in terms of delta instead of percentage (98%) spot generates more stable barrier values. Spot normalized to \$100. Notional set at \$100. Data and sample period: Currencies: dollar-yen, dollar-pound, both with dollar as domestic. Sample period: 1996/1/ /1/28, 2927 days. Instruments: currencies, delta-neutral straddle, 10- and 25-delta risk reversal and butterfly spread at 1 week, and 1,2,3,6,9, 12 months. Liuren Wu Hedging Barriers Risk, March 13, / 27

9 Hedging exercise design Procedure: Sell a one touch at each date (2897 days) and monitor hedging (rebalancing if needed) over the next 30 days. Set the selling price to (i) zero and (ii) the model fair value. Store the two types of terminal P&L from the hedging exercise. Report/compare the summary statistics on the P&L over the 2897 exercises for each currency pair across different hedging strategies. Also report the summary statistics of no hedging. Liuren Wu Hedging Barriers Risk, March 13, / 27

10 Hedging strategies Dynamic Strategies (Rebalancing based on partial derivatives): Hedging dimensions (sources of risks hedged): Delta (spot sensitivity) using the underlying currency. Vega (volatility/variance sensitivity) using delta-neutral straddle. Vanna (spot/volatility cross sensitivity) using risk reversal. Volga using butterfly spread. Hedging models (used to compute the sensitivities): Black-Scholes (Garman-Kohlhagen). Heston stochastic volatility (daily parameter calibration). Carr-Wu stochastic skew model (daily parameter calibration). Dupire local volatility model Rebalancing frequencies: Daily. Weekly. Once (vanna-volga approach?). Static strategy (No rebalancing until barrier crossing or expiry) Liuren Wu Hedging Barriers Risk, March 13, / 27

11 The Black-Scholes model Black-Scholes model: ds/s = (r q)dt + σdw Definitions of greeks: Delta P S t, Vega P σ, Vanna 2 P S σ, Volga 2 P σ σ, σ input in calculating the greeks is the IV (L, τ), obtained via linear interpolation across strikes. When L is outside the available delta range, using the IV at the boundary (no extrapolation). At maturities shorter than one month (as time goes by), use one month quotes on implied volatility and interest rates. Delta, vega are analytical. Vanna and volga are numerical. Liuren Wu Hedging Barriers Risk, March 13, / 27

12 Pricing and hedging under the Black-Scholes model Vanilla and barrier pricing: with p 1 2 r q σ 2 C t (K, T ) = S t e q(t t) N(d 1 ) Ke r(t t) N(d 2 ), OTPE t (L, T ) = e (N( d r(t t) 2 ) + ( S t ) 2p L N( d3 )),, d 1,2 = ln(st/k)+(r q±σ2 /2)(T t) σ T t Hedging ratios on vanillas and barriers:, d 3 = ln(st/l) (r q σ2 /2)(T t) σ. T t Delta(C t (K, T )) = e q(t t) N(d 1 ), Vega(C t (K, T )) = S t e q(t t) T tn(d 1 ), Vanna(C t (K, T )) = e q(t t) n(d 1 )d 2 /σ. Delta(OTPE t (L, T )) = e r(t t) n(d 2 )d 2 (St ) 2p S t 2p 1 «S N( d3 L L ) + t 2p n(d3 L )d 3 (St ), Vega(OTPE t (L, T )) = e n(d r(t t) S 2 )d 4 + t 2p «S n(d3 L )d 5 + t 2p N( d3 L ) ln( S L ) 4(r q) σ 3, with n(x) e x2 / 2π denoting a normal density function and d 2 (S) = d 3 (S) = 1/(Sσ T t), and d 4 = ln(s t /L) + (r q + σ 2 /2)(T t) σ 2 T t, d 5 = ln(st /L) (r q + σ2 /2)(T t) σ 2. T t Liuren Wu Hedging Barriers Risk, March 13, / 27

13 The Heston stochastic volatility model Heston model: ds/s = (r q)dt+ v t dw, dv t = κ(θ v t )dt+σ v vt dz, ρdt = E[dWdZ] Definitions of greeks: Delta P, Vega P, Vanna 2 P, S t v t S v t Model parameters and v t are obtained from daily calibration to vanilla options (1m to 12m). κ is fixed to one. Use finite-difference method to determine values and greeks for the barrier. Use FFT method to determine greeks for vanilla options. Also consider adjusted delta: Adj. Delta dotpe t(s t ) ds t = OTPE t(s t ) + OTPE t(s t ) S t v t σ v ρ S t. Liuren Wu Hedging Barriers Risk, March 13, / 27

14 Pricing and hedging under Heston Under the Heston model, we can derive the conditional generalized Fourier transform of the log currency return s t,τ = ln S t+τ /S t in closed form: φ s (u) E t [ e iu ln St+τ /St ] = e iu(r q)τ a(τ) b(τ)vt, u D C, (1) where and η = a(τ) = κ σ 2 v b(τ) = [ ( 2 ln 1 η eκ 2η (1 e ητ ) ) ] + (η κ)τ, 2ψ(1 e ητ ) (2) 2η (η eκ)(1 e ητ ), ( κ) 2 + 2σ 2 v ψ, κ = κ iuρσσ v, ψ = 1 2 σ2 (iu + u 2 ). Given the Fourier transform, we can price European vanilla options numerically using fast Fourier inversion. To price barrier options, we use a finite-difference scheme. Liuren Wu Hedging Barriers Risk, March 13, / 27

15 The Carr-Wu stochastic skew model We implement a simplified (pure diffusion) version of the SSM: ds/s = (r q)dt + v R t dw R + v L t dw L, dv j t = κ(θ v j t )dt + σ v v j t dz j, j = R, L, ρ j dt = E[dW i dz j ] The model generates not only stochastic volatility, but also stochastic skew (random risk reversal). Definitions of greeks: Delta P P, Vega S t (vt R + vt L ), Vanna 2 P vt R vt L, Model parameters and v t are obtained from daily calibration to vanilla options (1m to 12m). κ is fixed to one. ρ R = ρ L > 0. Use simulation to determine greeks for barrier. Use FFT method to determine greeks for vanilla options. Liuren Wu Hedging Barriers Risk, March 13, / 27

16 Pricing and hedging under the Carr-Wu stochastic skew model Under the stochastic skew model, the conditional generalized Fourier transform of the log currency return is also known in closed form: where and φ s (u) = e iu(r q)τ ar (τ) b R (τ)v R t al (τ) b L (τ)v L t, (3) [ a j (τ) = κ σ 2 ln (1 η eκj v 2 b j (τ) = η j = ( 2η j 2ψ 1 e ηj τ )) 1 e ηj τ ] + (η j κ j )τ, 2η j (η j eκ j )(1 e ηj τ ), (4) ( κ j ) 2 + 2σ 2 v ψ, κ j = κ iuρ j σσ v, ψ = 1 2 σ2 (iu + u 2 ). Given the generalized Fourier transform, we price European vanilla options numerically using fast Fourier inversion. We price the barrier option using Monte Carlo simulation. Liuren Wu Hedging Barriers Risk, March 13, / 27

17 The Dupire local volatility model Dupire (1994) local volatility model: ds/s = (r q)dt + σ(s, t)dw, ( ) 2 C(K,T ) σ 2 T + (r q)k C(K,T ) K + qc(k, T ) (K, T ) =. K 2 2 C(K,T ) K 2 Definitions of greeks: Delta P S t, Vega P σ(s, t), Vanna 2 P S σ(s, t), Local volatility space implied from the vanilla implied volatility surface with one week to 12 month options. Use finite difference method to determine greeks. Liuren Wu Hedging Barriers Risk, March 13, / 27

18 Implementation details on the local volatility model Based on the implied volatility quotes IV at each each strike and maturity, we first define the total implied variance as w = IV 2 T. We interpolate the total implied variance against maturity T and moneyness k ln K/F using cubic splines with matching second derivatives to obtain a smooth function w(k, T ). We then directly derive the local volatility in terms of this smooth total implied variance function: σ 2 (K, T ) = 1 kw k w w T + (r q)w k ( w k w k2 w 2 ) + w kk 2, (5) where w T, w k, w kk denote the partial derivative of the total variance against the option maturity and moneyness k, respectively. Liuren Wu Hedging Barriers Risk, March 13, / 27

19 The static hedging strategy: The idea Under the Black-Scholes model, the one-touch can be replicated by a European-style terminal payoff structure, ( ( ) ) 2p ST Payoff T = 1(S T < L) 1 +, p = 1 L 2 r d r f σ 2. (6) The static hedging strategy: At time t, sell one barrier OTPE t (L, T ) and put on a static hedging position with vanilla options with the terminal payoff equal to (6). If the barrier is never hit before expiry, both the one-touch and the hedging portfolio generate zero payoff. If the barrier is crossed at time T 1 (t, T ), rebalance the static hedging position to match the one dollar liability at expiry. 2p. Sell a European option with the terminal payoff 1(S T < L) ST L Buy a European option that pays 1(S T > L). The operation is self-financing under the Black-Scholes model. The net result of this operation is to generate a portfolio that pays one dollar regardless of the terminal stock price. Liuren Wu Hedging Barriers Risk, March 13, / 27

20 The static hedging strategy: Simplification Simplification: Set p = 1 2 which is exact when r = q. The replicating portfolio becomes ( ( ) ) 2p ST Payoff T = 1(S T < L) 1 + = 2(S T < L) (L S T ) + L L Long 2 binary puts, short 1/L put. The payoff is zero if the barrier is never hit. When the barrier is hit, we do the following: P T1 (K,T ) K P T 1 (L,T ) K=L Sell one binary put and buy 1/L put: L, Buy a binary call: C T 1 (K,T ) K. K=L The operation is self-financing under the model assumption. The terminal payoff of this rebalanced portfolio would be one dollar, just like the one touch. The strategy works even in the presence of stochastic volatility (as long as it is independent of currency return). Liuren Wu Hedging Barriers Risk, March 13, / 27

21 The static hedging strategy: Implementation We assume that we can readily trade the binary puts and binary calls. We explore two methods for valuation: Take derivatives of the Black-Scholes formula against strike, BP(L) = BSP(K,IV ) K, regarding implied volatility as a constant. K=L Adjust for the implied-volatility smile effect: BP(L) =. K=L K=L BSP(K,IV (K)) K BSP(K,IV (K)) IV IV (K) K To obtain the derivative, we apply cubic spline on the implied volatility as a function of log strike, and then evaluate the derivative at the barrier. One touch valuation: OTPE t (L, T ) = 2BP(L) Pt(L,T ) L. The payoff is zero if the barrier is never hit. When the barrier is hit, we sell one binary put, buy 1/L put, and buy a binary call to generate \$1 payoff. The rebalancing is self-financing in theory, but can generate a cost/benefit when model assumptions are violated. We carry the actual rebalancing cost to maturity as terminal P&L. Liuren Wu Hedging Barriers Risk, March 13, / 27

22 Dynamic hedging on Black Scholes Currency JPYUSD GBPUSD Strategy Mean Std Skew Kurt Mean Std Skew Kurt A. Terminal PL excluding sales from one touch No Hedge Delta Vega Vanna B. Terminal PL including one-touch model value as revenue No Hedge Delta Vega Vanna C. Model value with implied volatility at the barrier Value Liuren Wu Hedging Barriers Risk, March 13, / 27

23 Dynamic hedging on Heston Strategy Mean Std Skew Kurt Mean Std Skew Kurt A. Terminal PL excluding sales from one touch No Hedge Delta Vega Vanna Adj. Delta B. Terminal PL including one-touch model value as revenue No Hedge Delta Vega Vanna Adj. Delta C. Model value with implied volatility at the barrier Value Liuren Wu Hedging Barriers Risk, March 13, / 27

24 Dynamic hedging on local volatility model Strategy Mean Std Skew Kurt Mean Std Skew Kurt A. Terminal PL excluding sales from one touch No Hedge Delta B. Terminal PL including one-touch model value as revenue No Hedge Delta C. Model value with implied volatility at the barrier Value Liuren Wu Hedging Barriers Risk, March 13, / 27

25 Dynamic hedging on stochastic skew model We still need to work on the numerical issues in computing the barrier hedging ratios. Liuren Wu Hedging Barriers Risk, March 13, / 27

26 Static hedging Currency JPYUSD GBPUSD Strategy Mean Std Skew Kurt Mean Std Skew Kurt A. Terminal PL excluding sales from one touch Non-Adjusted Smile-Adjusted B. Terminal PL including one-touch model value as revenue Non-Adjusted Smile-Adjusted C. Model value with implied volatility at the barrier Non-Adjusted Smile-Adjusted Liuren Wu Hedging Barriers Risk, March 13, / 27

27 Concluding remarks Among dynamic hedging strategies, delta hedging significantly reduces the P&L variation. Adding vega hedging on top of delta hedging also helps. But hedging additional risk dimension no longer generate visible reduction in risk. For each dynamic strategy considered, computing the greeks using Heston generates better performance than computing the greeks uing BS. The simple static strategy works better than all dynamic strategies. Remaining issue: For pricing, which one is the most accurate? Liuren Wu Hedging Barriers Risk, March 13, / 27

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