Nested risk computations through non parametric Regression, with - PowerPoint PPT Presentation

Nested risk computations through non parametric Regression, with Markovian design Gersende Fort Institut de Math´ ematiques de Toulouse CNRS and Univ. de Toulouse France Joint work with Emmanuel Gobet (Ecole Polytechnique, France) Eric Moulines (Ecole Polytechnique, France) Talk based on the paper G. Fort, E. Gobet and E. Moulines. MCMC design-based non-parametric regression for rare event. Application to nested risk computation. , Monte Carlo Methods and Applications , 23(1):21–42, 2017. 1 / 22

The problem Numerical method for the approximation of quantities of the form ��� � � � � � E f Y , φ ⋆ Y � Y ∈ A when (outer expectation) the integration w.r.t. to L ( Y | Y ∈ A ) is intractable the event { Y ∈ A} is rare and (inner expectation) The function φ ⋆ is unknown, and is assumed of the form φ ⋆ ( Y ) = E [ R | Y ] a.s. with exact sampling from the conditional distribution L ( R | Y ) . but For all ( y, r ) , the quantity f ( y, r ) can be explicitly computed. 2 / 22

Motivations �� � � � � Y , E [ R | Y ] � Y ∈ A E f Solving dynamical programming equations for stochastic control and optimal stopping problems - see the plenary talk by E. Gobet (Tsitsiklis and Van Roy, 2001; Egloff, 2005; Lemor et al. 2006; Belomestny et al. 2010) Financial and Actuarial Management (Mc Neil et al., 2005) ex. : risk management of portfolios written with derivative options (Gordy and Juneja, 2010) where Y is the underlying asset or financial variables at time T R aggregated cashflows of derivatives at time T ′ > T E [ R | Y ] is the portfolio value at time T given a scenario Y and the aim is to compute the extreme exposure of the portfolio (VaR, CVaR). 3 / 22

A solution based on nested Monte Carlo (1/2) ◮ Step 1: An outer Monte Carlo step � � �� � � X ( m ) �� N out � � 1 � X ( m ) , φ ⋆ f Y , φ ⋆ ( Y ) � Y ∈ A ≈ f E N out m =1 How to draw the points X ( m ) ? 4 / 22

A solution based on nested Monte Carlo (1/2) ◮ Step 1: An outer Monte Carlo step � � �� � � X ( m ) �� N out � � 1 � X ( m ) , φ ⋆ f Y , φ ⋆ ( Y ) � Y ∈ A ≈ f E N out m =1 How to draw the points X ( m ) ? Rejection algorithm i.e. exact sampling under L ( Y | Y ∈ A ) Repeat Draw independently, samples Y ( m ) with distribution Y until Y ( m ) ∈ A ֒ → inefficient in the rare event setting: the mean number of loops to accept one sample is 1 / P ( Y ∈ A ) . 4 / 22

A solution based on nested Monte Carlo (1/2) ◮ Step 1: An outer Monte Carlo step �� � � � � X ( m ) �� N out � � 1 � X ( m ) , φ ⋆ E f Y , φ ⋆ ( Y ) � Y ∈ A ≈ f N out m =1 How to draw the points X ( m ) ? Rejection algorithm i.e. exact sampling under L ( Y | Y ∈ A ) Importance sampling (Rubinstein and Kroese, 2008; Blanchet and Lam, 2012) - efficient in small dimension, fails to deal with larger dimensions - relies heavily on particular types of models for Y , and on suitable information about the problem. 4 / 22

A solution based on nested Monte Carlo (1/2) ◮ Step 1: An outer Monte Carlo step � � �� � � X ( m ) �� N out � � 1 � X ( m ) , φ ⋆ f Y , φ ⋆ ( Y ) � Y ∈ A ≈ f E N out m =1 How to draw the points X ( m ) ? Rejection algorithm i.e. exact sampling under L ( Y | Y ∈ A ) Importance sampling (Rubinstein and Kroese, 2008; Blanchet and Lam, 2012) MCMC approach: { X (1) , · · · , X ( m ) , · · · } is a Markov chain having the conditional distribution L ( Y | Y ∈ A ) as the unique invariant distribution. 4 / 22

A solution based on nested Monte Carlo (2/2) ◮ Step 2: An inner Monte Carlo step � � �� � N out � � � 1 � φ ( m ) φ ( m ) X ( m ) , � � ≈ φ ⋆ ( X ( m ) ) E f Y , φ ⋆ ( Y ) � Y ∈ A ≈ f ⋆ ⋆ N out m =1 φ ⋆ ( Y ) = E [ R | Y ] , exact sampling from L ( R | Y ) : available Crude Monte Carlo. 5 / 22

A solution based on nested Monte Carlo (2/2) ◮ Step 2: An inner Monte Carlo step � � �� � N out � � � 1 � φ ( m ) φ ( m ) X ( m ) , � ≈ φ ⋆ ( X ( m ) ) � Y , φ ⋆ ( Y ) � Y ∈ A ≈ E f f ⋆ ⋆ N out m =1 φ ⋆ ( Y ) = E [ R | Y ] , exact sampling from L ( R | Y ) : available Crude Monte Carlo. cost: N in × N out draws for each sample X ( m ) , i.i.d. draw { R ( m, 1) , R ( m, 2) , · · · , R ( m,N in ) } ∼ L ( R | Y = X ( m ) ) set N in � 1 φ ( m ) def � R ( m,n ) = ⋆ N in n =1 Regression. 5 / 22

A solution based on nested Monte Carlo (2/2) ◮ Step 2: An inner Monte Carlo step �� N out � � � � � � 1 � φ ( m ) φ ( m ) X ( m ) , � � ≈ φ ⋆ ( X ( m ) ) E f Y , φ ⋆ ( Y ) � Y ∈ A ≈ f ⋆ ⋆ N out m =1 φ ⋆ ( Y ) = E [ R | Y ] , exact sampling from L ( R | Y ) : available Crude Monte Carlo. cost: N in × N out draws Regression. cost: N out draws; Take into account cross-information between points X ( m ) for each sample X ( m ) , draw a single R ( m ) ∼ L ( R | Y = X ( m ) ) φ ( m ) def set � = � φ ⋆ ( X ( m ) ) where ⋆ N out � 1 def � R ( m ) − φ ( X ( m ) ) � 2 . � φ ⋆ ( x ) = argmin φ ∈F N out m =1 5 / 22

MCMC combined with Regression �� � � � N out � � � 1 � X ( m ) , � φ ⋆ ( X ( m ) ) � Y ∈ A ≈ E f Y , φ ⋆ ( Y ) f N out m =1 (I) samples points X (1) , · · · , X ( N out ) from a MCMC targeting L ( Y | Y ∈ A ) (II) choose L basis functions φ 1 , · · · , φ L and set � φ ⋆ = � α 1 φ 1 + · · · + � α L φ L where N out L � � � R ( m ) − α ℓ φ ℓ ( X ( m ) ) � 2 . ( � α 1 , · · · , � α L ) = argmin ( α 1 , ··· ,α L ) ∈ R L m =1 ℓ =1 For alternatives to this regression approach, see e.g.: kernel estimators (Hong and Juneja, 2009); kriging techniques (Liu and Staum, 2010) For alternatives to this MCMC design, see e.g. (Broadie et al., 2015) with a weighted regression 6 / 22

Our contribution 1 Convergence analysis when the outer Monte Carlo step relies on (non stationary) MCMC samples - existing results on the regression error address the case of a i.i.d. or a stationary design X (1) , · · · , X ( m ) , · · · (Gyorfi et al. 2002, Ren and Mojirsheibani 2010, Delattre and Ga¨ ıffas, 2011) - consistent numerical method under weaker conditions on the basis functions φ 1 , · · · , φ L and on the distribution of the design (Broadie et al. 2015) 2 Ergodic properties of a MCMC sampler, designed to sample distributions restricted to a rare event. 7 / 22

On the MCMC step An efficient algorithm to sample from the distribution π d λ ≡ L ( Y | Y ∈ A ) 8 / 22

A MCMC sampler Choose a proposal kernel q ( x, z ) d λ ( z ) such that for all x, z ∈ A q ( x, z ) π ( z ) = π ( x ) q ( x, z ) (reversible w.r.t. π ) MCMC sampler (Gobet and Liu, 2015) Init: X (0) ∼ ξ - a distribution on A For m = 1 : N out , repeat: X ( m ) ∼ q ( X ( m ) , z ) d λ ( z ) Draw a candidate � Update the chain: set � � X ( m ) ∈ A if � X ( m ) X ( m +1) = X ( m ) otherwise Return X ( m ) , m = 0 : N out . 9 / 22

Application: sampling a N (0 , 1) in the left tail (1/3) ◮ Goal: P ( Y ∈ ·| Y ≤ y ⋆ ) , Y ∼ N (0 , 1) . ◮ Displayed: The histogram of the draws obtained by rejection (bottom left), by the MCMC sampler GL (top left) and the MCMC sampler NR (top right). The associated empirical cdf’s (bottom right). � GL : q ( x, · ) = ρx + 1 − ρ 2 N (0 , 1) (reversible) � NR : q ( x, · ) = ρx + (1 − ρ ) y ⋆ + 1 − ρ 2 N (0 , 1) (non reversible) ◮ Num. Appl.: 1 e 6 draws for each algorithm ( ⇒ 50 − 60 accepted draws for the rejection algorithm). ρ = 0 . 85 . P ( Y ≤ y ⋆ ) = 5 . 6 e − 5 4.5 4.5 4 4 3.5 3.5 3 3 2.5 2.5 2 2 1.5 1.5 1 1 0.5 0.5 0 0 -6 -5.5 -5 -4.5 -4 -6 -5.5 -5 -4.5 -4 4.5 4 10 0 3.5 10 -1 3 2.5 10 -2 2 10 -3 1.5 1 True 10 -4 IID 0.5 Kernel GL Kernel NR 0 10 -5 -6 -5.5 -5 -4.5 -4 -6 -5.5 -5 -4.5 -4 -3.5 10 / 22

Application: sampling a N (0 , 1) in the (left) tail (2/3) ◮ Goal: Role of the design parameter ρ in the efficiency of the samplers. ◮ Displayed: The autocorrelation function, averaged over 100 estimations with lag 0 to 50 . For different values of ρ ∈ (0 , 1) . For the MCMC sampler GL (left) and NR (right). � 1 − ρ 2 N (0 , 1) GL : q ( x, · ) = ρx + (reversible) � NR : q ( x, · ) = ρx + (1 − ρ ) y ⋆ + 1 − ρ 2 N (0 , 1) (non reversible) 1 1 0 0 0.1 0.1 0.8 0.2 0.8 0.2 0.3 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.8 0.8 0.9 0.9 0.4 0.4 0.99 0.99 0.2 0.2 0 0 -0.2 -0.2 0 10 20 30 40 50 0 10 20 30 40 50 11 / 22