P á l Rakonczai, L á szl ó Varga, Andr á s Zempl é ni Copula fitting to time-dependent data, with applications to wind speed maxima Eötvös Loránd University Faculty of Science Institute of Mathematics Department of Probability Theory and Statistics

Outline 1.Copulae 2.Goodness-of-fit tests 3.Bootstrap methods 4.Serial dependence 5.Applications to wind speed maxima 2

1. Copulae C is a copula, if it is a d-dimensional random vector with marginals ~ Unif [0,1] Existence (Sklar’s Theorem): to any d-dimensional random variable X with c.d.f. H and marginals F i (i=1,...,d) there exists a copula C : H( x 1, …, x d ) = C ( F 1 (x 1 ), …, F d (x d ) ) Uniqueness: if F i are continuous (i=1,...,d) Separation of the marginal model and the dependence 3

Elliptical Copulae – copulae of elliptical distributions – Gaussian: X ~ N n (0,Σ) where Φ: c.d.f. of N(0,1) – Student’s t: X ~ where t v : c.d.f. of Student’s t distribution with v degrees of freedom 1. Copulae – Examples 4

Archimedean Copulae Copula generator function: ϕ is continuous, strictly decreasing and ϕ (1)=0. d-variate Archimedean copula: – Gumbel: where – Clayton: where 1. Copulae - Examples 5

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2. Goodness-of-fit tests in one dimension 1.Estimation of the model parameter 2.Goodness-of fit test: a)Cramér-von Mises tests: F n : empirical c.d.f. F: c.d.f. Φ : weight function  Anderson-Darling: b)Critical value – simulation: 1)Simulate a sample from the copula model C θ under H 0 2)Re-estimate by ML-method 3)Calculate the test statistics Repetition and estimation of p values 7

2. Goodness-of-fit tests in more dimensions Probability integral transformation (PIT) – mapping into the d-dimensional unit cube: ~H ~C, for i=1,...,n Kendall’s transform: (K function) Advantage: one-dimensional – Example: Archimedean copulas: where 8

Empirical version: where Kendall’s process:  favorable asymptotic properties Cramér-von Mises type statistic:  where Φ : weight function 9 2. Goodness-of-fit tests in more dimensions

3. Serial dependence Let X 1, X 2,..., X n be univariate stationary observations; EX i = μ, Var(X i )= σ 2. If X 1, X 2,..., X n are i.i.d., then Serial dependence → higher variance Effective sample size (n e ): where : estimated variance ← bootstrap 10

4. Bootstrap methods - Bootstrap intro Efron (1979) Let X 1, X 2,... be i.i.d. random variables with (unknown) common distribution F –X n ={X 1,..., X n } random sample – T n =t n ( X n ; F) random variable of interest, it’s distribution: G n Goal: approximation of the distribution G n Bootstrap method: – For given X n, we draw a simple random sample of size m (usually m ≈ n) – Common distribution of ’s: – – Repetition 11

4. Bootstrap methods - CBB Nonparametric bootstrap (sample size: n) – Block bootstrap Circular block bootstrap (CBB) 1.Let 2.For some m, let i 1, i 2..., i m be a uniform sample from the set {1, 2,..., n} 3.For block size b, construct n’=m·b (n’ ≈ n) pseudo-data: for j=1,...,b 4.Functional of interest, e.g. bootstrap sample mean: 12

4. Bootstrap methods – Block-length selection D.N.Politis-H. White (2004): automatic block-length selection Minimalize: where and g(.): spectral density function R(.): autocovariance function Optimal block size: Estimation of G and D 13

5. Applications to wind speed maxima Sample: n = 2591 observations of weekly wind speed maxima for 5 German towns Automatic block-length selection results: meteorologically no sense 14

15 5. Applications to wind speed maxima Method: 1.Fitting AR(1) modell to the data:, Z t ~Extreme value distr. 2.Calculation of the theoretical from AR(1) parameters: 3. b* optimal block size: where the simulated variance of the mean first crosses the theoretical value

Bootstrap simulation results b* = Applications to wind speed maxima

Bootstrap simulation results 5. Applications to wind speed maxima 17

Bremerhaven & Fehmarn Applications to wind speed maxima

Bremerhaven & Schleswig Applications to wind speed maxima

Fehmarn & Schleswig Applications to wind speed maxima

Prediction regions (Bremerhaven & Fehmarn) Applications to wind speed maxima Wind speed (m/s) Pred. regions: % lower(5%) bounds upper(95%) bounds block=1 lower bound block=7 lower bound block=30 lower bound block=1 upper bound block=7 upper bound block=30 upper bound

Final remarks Conclusions Copula choice is important Serial dependence largely influences the critical values of GoF tests Block size does not have a major impact on the estimated prediction region Future work Multivariate effective sample size Parametric bootstrap Acknowledgement We are grateful to the Doctoral School of Mathematics of ELTE for supporting L. Varga’s participation at SMTDA Conference.

Thank you for the attention 23

References P. Rakonczai, A. Zempléni: Copulas and goodness of fit tests. Recent advances in stochastic modeling and data analysis, World Scientific, pp , S.N. Lahiri: Resampling Methods for Dependent Data. Springer, D.N.Politis, H.White: Automatic Block-Length Selection for the Dependent Bootstrap. Econometric Reviews, Vol. 23, pp , P. Embrechts, F. Lindskog, A. McNeil: Modelling Dependence with Copulas and Applications to Risk Management. Department of Mathematics, ETHZ, Zürich, L.Kish: Survey Sampling, J. Wiley,