In order to value fixed income derivatives cash flows, relevant forward rates and discount factors have to be defined from bootstrapped zero-coupon curve. In a Libor world, we use cash and FRA contracts (or futures contracts) in a short-end of the curve, while in a long-end of the curve we use swap contracts. Let us assume for a moment, that we bootstrap USD 3M zero-coupon curve, in order to get 3M forward rates on a quarterly basis. While bootstrapping approach usually gives smooth curve within short-end of the curve, it will usually fail to do this within long-end of the curve. This happens, because there is no swap contracts available in a long-end of the curve and hereby, we need to do a lot of interpolations for required swap rates. The resulting forward curve then looks like a chain of waterfalls and for the long-end of the curve, smoothness will be lost. This topic has been thoroughly covered in chapter three within this excellent book by Richard Flavell.
In a nutshell, we first define the shortest end of the curve (using spot cash rate) and then we just guess the rest of the forward rates. After this, we minimize sum of squared differences between adjacent forward rates, subject to constraints. In this case, constraints are present values of swaps, which by definition will be zero. When this optimization task has been performed, the resulting forward curve will successfully re-price the current swap market.
Bloomberg forward curve screen for the same set of data is available in here. Finally, thanks again for reading my blog.
-Mike
import numpy as np import scipy.optimize as opt import matplotlib.pyplot as pl # sum of squared errors of all decision variables # args: 0 = array of given initial rates, 1 = scaling factor def ObjectiveFunction(x, args): # concatenate given initial rates and given 'guesses' for forward rates x = np.concatenate([args[0], x]) return np.sum(np.power(np.diff(x), 2) * args[1]) # x = array of Libor forward rates # args: 0 = swap rate, 1 = years to maturity, 2 = floating leg payments per year, # 3 = notional, 4 = array of given initial rates def VanillaSwapPV(x, args): # PV (fixed payer swap) = -swapRate * Q(T) + (1 - DF(T)) # where Q(T) is sumproduct of fixed leg discount factors and corresponding time accrual factors # assumption : fixed leg is always paid annually # since fixed leg is paid annually and maturity is always integer, # time accrual factor will always be exactly 1.0 # concatenate given initial rates and given 'guesses' for forward rates x = np.concatenate([args[4], x]) DF = 0.0 Q = 0.0 floatingLegTenor = 1 / args[2] nextFixedLegCouponDate = 1 currentTimePoint = 0.0 nCashFlows = int(args[1] * args[2]) for i in range(nCashFlows): currentTimePoint += floatingLegTenor # first rate is always spot rate: calculate first spot discount factor if (i == 0): DF = 1 / (1 + x[i] * floatingLegTenor) # other rates are always forward rates if (i > 0): # solve current spot discount factor using previous spot discount factor # and current forward discount factor: DF(0,T) = DF(0,t) * DF(t,T), where (0 < t < T) DF *= (1 / (1 + x[i] * floatingLegTenor)) if ((currentTimePoint - nextFixedLegCouponDate) == 0): Q += DF nextFixedLegCouponDate += 1 return (-args[0] * Q + (1.0 - DF)) * args[3] # array of given initial rates (the first given rate has to be spot cash rate) # here, we give only one: 3M spot Libor fixing rate ('cash-to-first-future') initialRates = np.array([0.006276]) scalingFactor = 1000000.0 # vanilla swap pricing functions as constraints # args: swapRate, yearsToMaturity, floatingLegPaymentFrequency, notional, array of given initial rates swaps = ({'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.0078775, 1, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.009295, 2, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.0103975, 3, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.01136, 4, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.012268, 5, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.013183, 6, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.01404, 7, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.014829, 8, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.01554, 9, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.016197, 10, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.016815, 11, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.017367, 12, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.018645, 15, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.01996, 20, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.020615, 25, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.02097, 30, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.021155, 40, 4, scalingFactor, initialRates]]}, {'type': 'eq', 'fun': VanillaSwapPV, 'args': [[0.021017, 50, 4, scalingFactor, initialRates]]}) # initial guesses for Libor forward rates up to 50 years # number of rates: 50 * 4 - 1 (years to maturity * floating leg payments per year - first given cash spot rate) initialGuesses = np.full(199, 0.006) model = opt.minimize(ObjectiveFunction, initialGuesses, args = ([initialRates, scalingFactor]), method = 'SLSQP', options = {'maxiter': 500}, constraints = swaps) # print selected model results print('Success: ' + str(model.success)) print('Message: ' + str(model.message)) print('Number of iterations: ' + str(model.nit)) print('Objective function (sum of squared errors): ' + str(model.fun)) #print('Changing variables (Libor forward rates): ' + str(model.x * 100)) pl.plot(model.x) pl.show()
No comments:
Post a Comment