managing the currency risk of a futures portfolioabeaul10/presentation.pdf · 2016. 5. 30. ·...
TRANSCRIPT
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Managing the currency risk of a futures portfolio
Alexandre Beaulne, HEC Montreal
supervisors:Bruno Remillard, HEC Montreal
Pierre Laroche, National Bank of Canada
Special thanks to Sandrine Theroux and Innocap InvestmentManagement for providing me with the dataset used in this
presentation
May 3, 2012
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Outline
Introduction
Model
Goodness-of-fitMethodology
Collateral optimization
ResultsDatasetBacktesting
Conclusion
References
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Introduction
Problem: Global investors often need to post collateral in multiplecurrencies, while their performance is measured in one currency,creating exchange rate risk.
Two competing incentives:
I Keep posted collateral low to minimize exchange rate risk
I Keep posted collateral high to minimize margin calls
What collateral levels in each different currencies optimally balancethese two opposing forces?
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Introduction
Optimal collateral
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Introduction
Similar problems:
I Equity portfolio hedging -Minimizing currency risk while minimizing insurance costs
I Transaction costs -Maximizing risk/return while minimizing transaction costs
I Inventory management -Maximizing sales while minimizing shipping costs
I Staff dispatch -Minimizing travel time while minimizing expenses
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Introduction
A good solution needs to properly forecast the underlying pricesand exchange rates, accounting for the higher moments andcomoments of their respective time-series.
What we did:
I Select a few candidate models for the dynamics of theunderlyings’ prices and exchange rates
I Assess and compare their goodness-of-fit
I Optimize the “portfolio” of posted collateral based on thechosen model
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Outline
Introduction
Model
Goodness-of-fitMethodology
Collateral optimization
ResultsDatasetBacktesting
Conclusion
References
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Model
We chose a copula-based multivariate GARCH framework asadvocated in Xiaohong and Yanqin [2006], Patton [2006] andRemillard [2010]:
Xi ,t = µt(θi ) + ht(θi )1/2 εi ,t (1)
where i = 1, . . . ,D and innovations ε1,t , . . . , εD,t are i.i.d. withcontinous multivariate distribution function
K (x1, . . . , xD) = Cθ(F1(x1), . . . ,FD(xD)) (2)
where the Fi are the cumulative distribution functions of themarginal distributions Xi and Cθ is the copula function withparameter(s) θ.
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Model
Two steps:
(i) Find appropriate univariate process for each random variable(e.g. AR(1)-GARCH(1,1), eGARCH, GJR-GARCH, etc) for
Xi ,t = µt(θi ) + ht(θi )1/2 εi ,t
(ii) Find appropriate copula to capture the dependence betweenthe standardized residuals (e.g. Gaussian, Student, Clayton,Frank, Gumbel, etc) for
Cθ(F1(x1), . . . ,FD(xD))
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Outline
Introduction
Model
Goodness-of-fitMethodology
Collateral optimization
ResultsDatasetBacktesting
Conclusion
References
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Goodness-of-fit
How do you choose between the different models and once amodel is chosen, how do you know it is statistically correct?
⇒ parametric bootstrapping
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Goodness-of-fit
H0: Dataset belongs to said distributionH1: Dataset does not belong to said distribution
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Parametric bootstrapping
General procedure:
(i) Estimate the parameters of the chosen parametric distributionthat best fit the dataset
(ii) Calculate a distance ST between the empirical distributionand the parametric distribution (good candidate: Cramer-vonMises statistic)
(iii) Generate a large number N of “bootstrapped” samples of thesame size as the dataset from the parametric distribution
(iv) For each of these bootstrapped samples k = 1, . . . ,N,
(a) Estimate the parameters of the chosen parametric distributionthat best fit the bootstrapped sample
(b) Calculate a distance S(k)T between their empirical distribution
and the parametric distribution
(v) The p-value for the test is given by the fraction of the S(k)T
bigger than ST
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Cramer-von Mises statisticFor univariate distributions, the Cramer-von Mises statistic is givenby
ST =T∑t=1
1
T(FT (xt)− Fθ(xt))2
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Cramer-von Mises statistic
For copulas, the Cramer-von Mises statistic is given by
ST =T∑t=1
1
T(CT (u1,t , . . . , uD,t)− Cθ(u1,t , . . . , uD,t))2
where u1,t , . . . , uD,t are the normalized ranks
ui ,t =1
T − 1
T∑k=1
1(xi ,t ≥ xi ,k),
CT is the empirical copula
CT (u1,t , . . . , uD,t) =1
T − 1
T∑k=1
1(u1,t ≥ ui ,k , . . . , uD,t ≥ uD,k)
and Cθ is the parametric copula chosen.
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Rosenblatt transform
Unfortunately, Cθ do not often have a closed form and numericalapproximations are computationally impractical when the numberof dimensions gets high. Fortunately an alternative is proposed inGenest et al. [2009] using Rosenblatt’s transform:
U ∼ C ⇔ T (U) ∼ C⊥
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Rosenblatt transform
T (u1, . . . , uD) = (e1, . . . , eD) given by e1 = u1 and
ei =
δi−1
δu1...δui−1C (u1, . . . , ui , 1, . . . , 1)
δi−1
δu1...δui−1C (u1, . . . , ui−1, 1, . . . , 1)
(3)
[Rosenblatt, 1952].
The recipe to compute the Rosenblatt transform for bothmeta-elliptical and archimedean copulas can be found in Remillardet al. [2011].
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Parametric bootstrapping - Copula-based MultivariateGARCH model
(i) Estimate the parameters of each univariate marginal process
(ii) Estimate the parameter(s) of the chosen copula on thestandardized residuals εt obtained in step (i)
(iii) Compute the normalized ranks ut = u1,t , . . . , uD,t :
ui ,t =1
T − 1
T∑k=1
1(εi ,t ≥ εi ,k)
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Parametric bootstrapping - Copula-based MultivariateGARCH model
(iv) Compute Rosenblatt transforms et = e1,t , . . . , eD,t ,t = 1, . . . ,T using equation (3)
(v) Compute Cramer-von Mises statistic
ST = T
∫[0,1]D{FT (u)− C⊥(u)}2du
=T
3D− 1
2D−1
T∑t=1
D∏i=1
(1− e2i ,t
)+
1
T
T∑t=1
T∑k=1
D∏i=1
(1−max(ei ,t , ei ,k))
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Parametric bootstrapping - Copula-based MultivariateGARCH model
(vi) For some large integer N, repeat the following steps for eachk in (1, . . . ,N):
(a) Generate random trajectories of the processes withparameters found in (i) and (ii) of the same length as theoriginal dataset
(b) Repeat steps (i) to (v) on trajectories generated in (a) to
obtain S(k)T .
(vii) The approximate p-value for the test is given by
p =1
N
N∑k=1
1(S(k)T > ST )
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Outline
Introduction
Model
Goodness-of-fitMethodology
Collateral optimization
ResultsDatasetBacktesting
Conclusion
References
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Collateral optimization
The optimization objective:
Minimize the exchange rate risk on the posted collateral (asmeasured by the tracking error, Value-at-Risk or Tail ConditionalExpectation) subject to a given tolerance on the probability of amargin call
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Collateral optimization
In mathematical terms:
minλ1,t ,...,λD,t
Rα
(D∑i=1
(λi ,t − λ∗i ,t
)× Yi ,t+1
)(4)
subject to
λ∗i ,t ≤ λi ,t ≤ ∞, i = 1, . . . ,D
and
P
((D∏i=1
1(λi ,t + PnLi ,t+1 ≥ λ∗i ,t
))= 0
)≤ Ptol (5)
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Collateral optimization
where
Rα(X ) =
E [|X |] for expected tracking error
−x (α)(X ) for Value-at-Risk
E[X |X ≤ x (α)] for Tail Conditional Expectation(6)
and
PnLi ,t+1 =
ni,t∑j=1
iωj ,t × iWj ,t+1,
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Outline
Introduction
Model
Goodness-of-fitMethodology
Collateral optimization
ResultsDatasetBacktesting
Conclusion
References
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Dataset
The data set consists of daily holdings of five futures contractsdenominated in four non-USD currencies from november 2003 tomarch 2012 for a total of 1941 observations.
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Dataset
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Dataset
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Dataset
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Dataset
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Dataset
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Dataset
Japanese10YrFuture
Mini
Can10YrFuture
Euro
BundFuture
AUD
10YrFuture
AU
1-3
year
Future
AUD-U
SD
CAD-U
SD
EUR-U
SD
JPY-U
SD
AR(1)-GARCH(1,1),gaussian innovations
0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00
AR(2)-GARCH(2,2),gaussian innovations
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00
AR(1)-GARCH(1,1),student innovations
0.56 0.69 0.37 0.58 0.50 0.45 0.60 0.54 0.52
Table : p-values from the goodness-of-fit tests on marginal processes
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Dataset
p valuesMV Gaussian 0.00
AR(1)-GARCH(1,1) & gaussian copula 0.01AR(1)-GARCH(1,1) & student copula 0.12AR(1)-GARCH(1,1) & Clayton copula 0.00AR(1)-GARCH(1,1) & Frank copula 0.00AR(1)-GARCH(1,1) & Gumbel copula 0.00
Table : p-values from the goodness-of-fit tests on copula-based MVGARCH models
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Backtesting
I Two alternative strategies:
(i) Naive: Always post as collateral 2x the minimum marginsrequirements
(ii) Model the nine time series with a multivariate Gaussian
I 500 days buffer left at beginning of sample for calibration
I Daily recalibration
I GOF tests run every year
I Ptol = 0.05, α = 0.05
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Backtesting
Figure : Optimal posted collateral in JPY, June 2011 - January 2012
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Backtesting
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Backtesting
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Results
Naive MV Gaussian AR-GARCH & t-copula
Avg. daily tracking error (% collateral) 0.54 0.55 0.55# of margin calls (out of 1440 days) 104 94 71Frequency of margin call 0.0722 0.0653 0.0493
Table : Objective: Minimize the daily tracking error while keeping theprobability of a margin call under 0.05
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Results
Naive MV Gaussian AR-GARCH & t-copula
Realized daily VaR (% collateral) 1.19 1.21 1.24# of margin calls (out of 1440 days) 104 105 78Frequency of margin call 0.0722 0.0729 0.0542
Table : Objective: Minimize the Value-at-Risk while keeping theprobability of a margin call under 0.05
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Results
Naive MV Gaussian AR-GARCH & t-copula
Avg. daily tail loss (% collateral) -1.83 -1.85 -1.85# of margin calls (out of 1440 days) 104 100 71Frequency of margin call 0.0722 0.0694 0.0493
Table : Objective: Minimize the Tail Conditional Expectation whilekeeping the probability of a margin call under 0.05
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Outline
Introduction
Model
Goodness-of-fitMethodology
Collateral optimization
ResultsDatasetBacktesting
Conclusion
References
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Conclusion
Copula-based GARCH:
I are amongst the best model available for multivariate financialtime series
I have absolute goodness-of-fit tests now available (parametricbootstrapping)
I provides for better and more robust portfolio engineering andrisk management
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Outline
Introduction
Model
Goodness-of-fitMethodology
Collateral optimization
ResultsDatasetBacktesting
Conclusion
References
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References
Christian Genest, Bruno Remillard, and David Beaudoin.Goodness-of-fit tests for copulas: A review and a power study.Insurance: Mathematics and Economics, 44:199–213, 2009.
Andrew J. Patton. Modelling asymmetric exchange ratedependence. International Economic Review, 47(2):527–556,2006.
Bruno Remillard. Goodness-of-fit tests for copulas of multivariatetime series. Technical Report, 2010.
Bruno Remillard, Nicholas Papageorgiou, and Frederic Soustra.Dynamic copulas. Technical Report G-2010-18, Gerad, 2011.
Murray Rosenblatt. Remarks on a multivariate transformation.The Annals of Mathematical Statistics, 23(3):470–472, 1952.
Chen Xiaohong and Fan Yanqin. Estimation and model selection ofsemiparametric copula-based multivariate dynamic models undercopula misspecification. Journal of Econometrics, 135(1-2):125–154, 2006.