machine learning for quantitative finance€¦ · machine learning for quantitative finance: fast...
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Fast derivative pricing
Machine Learningfor Quantitative Finance
Sofie Reyners
Joint work with Jan De Spiegeleer, Dilip Madan andWim Schoutens
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Derivative pricing is time-consuming...
I Vanilla option pricing• European-type→ Fast Fourier transform
• American-type→ Tree methods
I Exotic option pricing→ Monte Carlo simulations
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... but time is money!
time-consuming algorithms
continuously moving markets
→ prices are outdated when available, overnight calculationscannot be performed in one night, ...
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Let a machine learn the pricing function
product, market andmodel parameters model price
time-consuming method
machine learning
Expensive pricing function is summarized with machine learning.
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Let a machine learn the pricing function
product, market andmodel parameters model price
time-consuming method
machine learning
When training is completed, prediction is extremely fast!
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Gaussian process regression (GPR)
Consider a training set
(X,y) = {(xi, yi) | i = 1, . . . , n}.
Find a relation between inputs and outputs:
yi = f(xi) + εi
where f(x) is a Gaussian process and εi ∼ N (0, σ2n) are i.i.d. random
variables representing the noise in the data.
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Gaussian process
A Gaussian process f(x) is a, possibly infinite, collection of randomvariables, any finite subset of it having a joint Gaussian distribution.
I Mean function: m(x) = E[f(x)
]I Kernel function: k(x,x′) = Cov(f(x), f(x′))
=⇒ f(x) ∼ GP (m(x), k(x,x′))
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Gaussian process
If f(x) ∼ GP (0, k(x,x′)), then
f ∼ N (0,K(X,X))
where (X,f) = {(xi, fi) | i = 1, . . . , n} is a sample from f(x) and
K(X,X) =
k(x1,x1) . . . k(x1,xn)... . . . ...
k(xn,x1) . . . k(xn,xn)
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GPR: a Bayesian method
I Don’t model the relation as one function, but as a distributionover functions.
I Procedure:
1 Start from a prior GP→ prior knowledge: smooth function, periodic function, ...→ prior distribution over functions
2 Include observed data points
3 Compute a posterior GP
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Posterior distribution
Only consider functions that agree with the data.
I Take new inputs X∗, with corresponding (unknown) functionvalues f∗
I Joint distribution of training outputs and function values:[yf∗
]∼ N
(0 ,
[K(X,X) + σ2
nI K(X,X∗)K(X∗, X) K(X∗, X∗)
])
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Posterior distribution
I Condition on the observations:
f∗|X∗, X,y ∼ N(µ,Σ
)with
µ = K(X∗, X)[K(X,X) + σ2
nI]−1y
Σ = K(X∗, X∗)−K(X∗, X)[K(X,X) + σ2
nI]−1
K(X,X∗)
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Kernel function
Squared exponential kernel function
k(x,x′) = σ2f exp
(−|x− x′|2
2`2
)
with hyperparameters σf and `:I σ2
f = signal varianceI ` = length-scale parameter
→ Hyperparameters (including σn) are estimated from the trainingdata, usually with MLE.
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Mean function
Often set to zero, but can be modelled using basis functions h(x).
g(x) = f(x) + h(x)Tβ ∼ GP (h(x)Tβ, k(x,x′))
whereI f(x) ∼ GP (0, k(x,x′))I β should be estimated from the training data
Common choice:h(x) = (1, x, x2)
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Application set-up
I Construct a training set:
product, market andmodel parameters model price
sample n randomcombinations xi
compute ncorresponding prices yi
time-consuming method
I Fit a Gaussian process regression (GPR) model.
I Fast prediction of new model prices.
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Pricing European call options
Training set:
Product/market VG Heston
K ∈ [40%, 160%] σ ∈ [0.05, 0.45] κ ∈ [1.4, 2.6]T ∈ [11M, 1Y ] ν ∈ [0.55, 0.95] ρ ∈ [−0.85,−0.55]r ∈ [1.5%, 2.5%] θ ∈ [−0.35,−0.05] θ ∈ [0.45, 0.75]q ∈ [0%, 5%] η ∈ [0.01, 0.1]
v0 ∈ [0.01, 0.1]
→ sample n values of each parameter→ calculate n FFT-based model prices
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Pricing European call options
I Fit the GPR model
K, T , r − q,model parameters
FFTmodel price
GPR
I Construct a test set:• Similarly as training set• Slightly smaller parameter intervals
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Out-of-sample prediction
(a) Variance Gamma (b) Heston
→ model trained on 10 000 points, tested on 100 000 points.
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Performance summary
VG Heston
Size of training set 5000 10 000 20 000 5000 10 000 20 000
In-sample predictionMAE 0.0016 0.0017 0.0013 0.0036 0.0027 0.0033AAE 2.5763e-04 1.9627e-04 1.4747e-04 5.2260e-04 4.0347e-04 3.4524e-04
Out-of-sample predictionMAE 0.0028 0.0022 0.0016 0.0060 0.0054 0.0048AAE 2.2508e-04 1.6942e-04 1.2828e-04 5.8991e-04 4.4112e-04 3.6623e-04
Speed-up × 30 × 15 × 7 × 40 × 20 × 10
withMAE = max {|ECF F T (i)− ECGP R(i)|, i = 1, . . . , n}
AAE = 1n
n∑i=1
|ECF F T (i)− ECGP R(i)|
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Pricing American options
I Put options with strike K and maturity T
I Use binomial tree model (daily steps) with volatility
σ ∈ [0.05, 0.55]
K, T , r, q, σ binomial treemodel price
GPR
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Out-of-sample performance
MAE 0.0086
AAE 9.1684e-04
Speed-up ×70
→ model trained on 10 000 points, tested on 100 000 points.
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Pricing barrier options
I Down-and-out barrier put options with barrier level H, strike Kand maturity T with
H ∈ [55%, 99%]
I Use Monte Carlo simulation, according to Heston’s model
H, K, T , r, q,κ, ρ, θ, η, v0
MC Hestonmodel price
GPR
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Out-of-sample performance
MAE 0.0086
AAE 6.7386e-04
Speed-up ×5850
→ model trained and tested on 10 000 points.
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Conclusion
I Time-consuming pricing methods
I Gaussian process regression• Matrix inversion• Hyperparameter optimization
I Apply GPR on existing methods• Speed-up of several orders of magnitude• Some trade-off with accuracy
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Thank you!
More information:
De Spiegeleer, J., Madan, D. B., Reyners, S. and Schoutens, W. (2018),Machine Learning for Quantitative Finance: Fast Derivative Pricing, Hedgingand Fitting, Quantitative Finance, forthcoming.
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