T HE B ANACH SPACE L 1 ( c ) E XTENSION OF THE CAPACITY Feyel and de - PowerPoint PPT Presentation

L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Regular convex risk measures on C b (Ω) C ONVEX RISK MEASURES UNDER MODEL UNCERTAINTY Jocelyne Bion-Nadal CNRS-CMAP Ecole Polytechnique joint work with Magali Kervarec Tamerza october 26, 2010 1/ 32 Jocelyne Bion-Nadal Model Uncertainty

L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Regular convex risk measures on C b (Ω) I NTRODUCTION D YNAMIC R ISK M EASURES ON A FILTERED PROBABILITY SPACE (Ω , F , ( F t ) t ∈ I R + , P ) filtered probability space with a right continuous filtration. Coherent Dynamic Risk Measures: Delbaen (2002) and Artzner, Delbaen, Eber, Heat, Ku (2007) Convex dynamic risk measures considered in many papers, among them: Frittelli and Rosaza Gianin (2002), Klöppel, Schweizer (2007), Cheredito, Delbaen, Kupper (2006), Bion-Nadal (2008 and 2009), Föllmer and Penner (2006) g expectations or Backward Stochastic Differential Equations : Peng (2004), Rosazza Gianin (2004) and Barrieu El Karoui (2009) 2/ 32 Jocelyne Bion-Nadal Model Uncertainty

L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Regular convex risk measures on C b (Ω) D YNAMIC R ISK M EASURES D YNAMIC R ISK M EASURES : on L ∞ (Ω , F , ( F t ) t ∈ I R + , P ) (or L p (Ω , F , ( F t ) t ∈ I R + , P ) , 1 < p < ∞ ). ρ σ,τ : L ∞ (Ω , F τ , P ) → L ∞ (Ω , F σ , P ) , satisfying monotonicity, convexity, translation invariance and continuity from above. Time Consistency : ∀ ν ≤ σ ≤ τ, ρ ν,τ ( X ) = ρ ν,σ ( − ρ σ,τ ( X )) Normalized ( ρ σ,τ ( 0 ) = 0) time consistent dynamic risk measures have càdlàg paths ( J B N 2009). Regularity is satisfied without the normalization assumption under some continuity assumption on the penalty, Families of dynamic risk measures constructed from right continuous BMO martingales generalizing B.S. D. E. and allowing for jumps. (J B N 2008 and 2009). 3/ 32 Jocelyne Bion-Nadal Model Uncertainty

L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Regular convex risk measures on C b (Ω) TIME CONSITENT D YNAMIC R ISK M EASURES FROM BMO MARTINGALES T HEOREM Let ( M i ) i ≤ j be strongly orthogonal right continuous BMO martingales. Let M = { � H i . M i , H i predictable } . Let S be a stable subset of Q ( M ) = { Q M | dQ M dP = E ( M ) M ∈ M} . Let b i be measurable on R j admitting a quadratic bound from below. For M = � H i . M i , R + × Ω × I I τ � α σ,τ ( Q M ) = E Q M ( b i ( s , ω, H 1 ( ω ) , .. H j ( ω )) d [ M i , M i ] s ( ω )) |F σ ) σ If M i are continuous 1 or if the BMO norms of elements of S are bounded by m < 16 , ρ σ,τ ( X ) = esssup Q M ∈S , Q M |F σ = P ( E Q M ( − X |F σ ) − α σ,τ ( Q M )) defines a time consistent dynamic risk measure. 4/ 32 Jocelyne Bion-Nadal Model Uncertainty

L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Regular convex risk measures on C b (Ω) B ROWNIAN FILTRATION These exemples generalize the B.S.D.E. (which are convex and translation invariant) QUADRATIC BACKWARDS Every solution of a BSDE with a convex driver independent of y and quadratic in z admits a dual representation of the preceding form (Barrieu and El Karoui 2009). NORMALIZED TIME CONSISTENT DYNAMIC RISK MEASURES IN A B ROWNIAN FILTRATION For every normalized time consistent dynamic risk measure on the Brownian filtration the penalty term associated to ( dQ dP ) = E ( q . B ) can be written: � τ c σ,τ ( Q ) = E Q ( f ( u , q u ) du |F σ ) ∀ 0 ≤ σ ≤ τ σ Delbaen Peng and Rosazza Gianin (2009) 5/ 32 Jocelyne Bion-Nadal Model Uncertainty

L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Regular convex risk measures on C b (Ω) M ODEL U NCERTAINTY F INANCIAL FRAMEWORK : No reference probability measure is given. Instead a weakly relatively compact set of probability measures is given. Motivations: E XAMPLE OF U NCERTAIN V OLATILITY dX σ t = b t dt + σ t dW t σ t ∈ [ σ, σ ] The set of the laws of X σ t : weakly relatively compact set P of probability measures not all absolutely continuous with respect to some probability measure. D ENIS M ARTINI (2006) R + , I R d ) , B t coordinate process. Ω = C 0 ( I P : weakly relatively compact set of orthogonal martingale measures for B t Pricing function Λ( f ) = sup P ∈P E P ( f ) . 6/ 32 Jocelyne Bion-Nadal Model Uncertainty

L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Regular convex risk measures on C b (Ω) I NTRODUCTION S. P ENG : G-E XPECTATIONS (2007), (2008) G -expectation E is defined on Lip , subset of C b (Ω) using PDE. D ENIS H U P ENG (2010) Every G expectation admits the representation ∀ f ∈ Lip E ( f ) = sup P ∈P 1 E P ( f ) P 1 is weakly relatively compact. In both cases, Π( f ) = sup P ∈P E P ( f ) P weakly relatively compact. Π is sublinear monotone translation invariant and regular Π( X n ) → 0 when X n ↓ 0. S ONER , T OUZI , Z HANG (2010), N UTZ (2010) R + , I R d ) , P is a set of probability measures. Same framework Ω = C 0 ( I Π( f ) = sup P ∈P E P ( f ) Either f ∈ C b (Ω) and P is weakly relatively compact. Or f ∈ UC b (Ω) and no restriction on P . 7/ 32 Jocelyne Bion-Nadal Model Uncertainty

L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Regular convex risk measures on C b (Ω) I NTRODUCTION R EGULAR CONVEX RISK MEASURES ON C b (Ω) R + , I R d ) or Ω is a Polish space. For example Ω = C ( I Ω = D ([ 0 , ∞ [ , I R d ) the space of càdlàg functions, endowed with the Skorokhod topology. Regularity (for sublinear risk measures): ρ ( − X n ) → 0 when X n ↓ 0. Regularity ⇐ ⇒ continuity with respect to a certain capacity c . If ρ is sublinear, c ( X ) = ρ ( −| X | ) c ( f ) = sup P ∈P E P ( | f | )) P weakly relatively compact. L 1 ( c ) Banach space obtained by completion and separation of C b (Ω) for the semi-norm c . L 1 ( c ) : introduced by Feyel and de la Pradelle (1989). Thus we study L 1 ( c ) and convex risk measures on L 1 ( c ) . We prove that there is an equivalence class of probability measures canonically associated to ρ , characterizing the riskless elements. 8/ 32 Jocelyne Bion-Nadal Model Uncertainty

L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Regular convex risk measures on C b (Ω) O UTLINE 1 L 1 ( c ) Topological properties of the dual space of L 1 ( c ) Convex risk measures on L 1 ( c ) 2 E QUIVALENCE CLASS OF PROBABILITY MEASURES ASSOCIATED TO A NON DOMINATED SET OF PROBABILITY MEASURES 3 R EGULAR CONVEX RISK MEASURES ON C b (Ω) Examples 9/ 32 Jocelyne Bion-Nadal Model Uncertainty

L 1 ( c ) Topological properties of the dual space of L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Convex risk measures on L 1 ( c ) Regular convex risk measures on C b (Ω) O UTLINE 1 L 1 ( c ) Topological properties of the dual space of L 1 ( c ) Convex risk measures on L 1 ( c ) Equivalence class of probability measures associated to a non dominated 2 set of probability measures Regular convex risk measures on C b (Ω) 3 Examples 10/ 32 Jocelyne Bion-Nadal Model Uncertainty

L 1 ( c ) Topological properties of the dual space of L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Convex risk measures on L 1 ( c ) Regular convex risk measures on C b (Ω) C APACITY Ω : Polish space (metrizable and separable space and complete for some metric defining the topology) L : linear vector subspace of C b (Ω) containing the constants, generating the topology of Ω and which is a vector lattice. C APACITY D EFINITION a capacity on L is a semi norm c defined on L satisfying the following properties: monotonicity: ∀ f , g ∈ L such that | f | ≤ | g | , c ( f ) ≤ c ( g ) 1 regularity along sequences: for every sequence f n ∈ L decreasing to 0, 2 lim c ( f n ) = 0 11/ 32 Jocelyne Bion-Nadal Model Uncertainty

L 1 ( c ) Topological properties of the dual space of L 1 ( c ) Equivalence class of probability measures associated to a non dominated set of probability measures Convex risk measures on L 1 ( c ) Regular convex risk measures on C b (Ω) T HE B ANACH SPACE L 1 ( c ) E XTENSION OF THE CAPACITY Feyel and de la Predelle (1989) The semi-norm c is extended to all real functions on Ω : ∀ f l . s . c ., f ≥ 0 , c ( f ) = sup { c ( φ ) | 0 ≤ φ ≤ f , φ ∈ L} (1) ∀ g , c ( g ) = inf { c ( f ) | f ≥ | g | , f l . s . c . } (2) T HE B ANACH SPACE L 1 ( c ) L 1 ( c ) : closure of L in the set { g | c ( g ) < ∞} . L 1 ( c ) contains C b (Ω) . (Feyel and de la Pradelle) Let L 1 ( c ) be the quotient of L 1 ( c ) by the c null elements. L 1 ( c ) is a Banach space. 12/ 32 Jocelyne Bion-Nadal Model Uncertainty