ON A PROBLEM BY SCHWEIZER AND SKLAR

Size: px
Start display at page:

Download "ON A PROBLEM BY SCHWEIZER AND SKLAR"

Transcription

1 K Y B E R N E T I K A V O L U M E 4 8 ( ), N U M B E R 2, P A G E S ON A PROBLEM BY SCHWEIZER AND SKLAR Fabrizio Durante We give a representation of the class of all n dimensional copulas such that, for a fixed m N, 2 m < n, all their m dimensional margins are equal to the independence copula. Such an investigation originated from an open problem posed by Schweizer and Sklar. Keywords: Classification: copulas, distributions with given marginals, Fréchet Hoeffding bounds, partial mutual independence 6E5, 62E1 1. INTRODUCTION The representation and the construction of n dimensional distribution functions (=d.f. s) with given lower dimensional marginal distributions is one of the classical problem in probability theory, due to its relevance to applications. Questions of this kind arise, for example, when one wants to build a multivariate stochastic model and has some idea about the kind of dependence, or knows exactly certain marginal distributions (see, for instance, [1, 2, 3, 7, 9] and the references therein). In this note, we investigate a special problem of this type, namely we consider the class of all possible joint d.f. s of a random vector X = (X 1, X 2,, X n ) such that: (a) X i has a continuous d.f. F i, for each i {1, 2,, n}; (b) for a given m N, 2 m < n, every sub-vector of m elements in X is formed by independent random variables (=r.v. s). Such a problem has been originally posed by Schweizer and Sklar (see [1, Problem 6.7.3]) in the class of all distribution functions whose univariate margins are uniformly distributed on [, 1], i.e., in the class of copulas. In fact, in view of Sklar s Theorem [12], copulas are exactly the objects that allow to capture the dependence properties of a random vector. Therefore, in this note, we investigate the above-stated problem in terms of multivariate copulas and its lowerdimensional margins. The paper is organized as follows. First, in section 2 we define the basic elements that are necessary in order to make the paper self-contained. Then, in section 3, we characterize the dependence structures of the previous type by providing also some upper and lower bounds.

2 288 F. DURANTE 2. PRELIMINARIES Let n, m be in N, 2 m < n. We denote by P n,m the class of all permutations σ = (σ 1, σ 2,, σ n ) of (1, 2,, n) such that σ 1 < σ 2 < < σ m and σ m+1 < σ m+2 < < σ n. For example, P 3,2 = {(1, 2, 3), (1, 3, 2), (2, 3, 1)}. We denote by x = (x 1,, x n ) any point in R n and by I n the product of n copies of the unit interval I = [, 1]. For basic definitions and properties about copulas, we refer to [6, 8]. Here we recall that an n copula is a function C : I n I satisfying the following properties: (C1) C(u) = whenever u I n has at least one argument equal to ; (C2) C(u) = u i whenever u I n has all the arguments equal to 1 except possibly the ith one, which is equal to u i ; (C3) C is n increasing, viz., for each n box B = n [u i, v i ] I n, u i v i for any i {1, 2,, n}, V C (B) = sgn(z)c(z), z B where where the sum is taken over all vertices z in B, z i {u i, v i } for every i in {1, 2,, n}, and sgn(z) = 1, if the number of u i s among the arguments of z is odd, and sgn(z) = 1, otherwise. We denote by C n the set of all n copulas. For all C C n and for all u I n, W n (u) C(u) M n (u), (1) { n } W n (u) = max u i n + 1,, M n (u) = min{u 1, u 2,, u n }. These inequalities are called Fréchet Hoeffding bounds [7, 8]. Notice that M n C n, but W n C n only for n = 2. Another important n copula is Π n (u) = n u i, which is associated with independent r.v. s. Given C C n, the m marginals of C, 2 m < n, are the ( n m) m copulas obtained by setting (n m) of the arguments of C equal to 1. Moreover, we denote by C n (Π m ) the class of all n copulas such that all their m marginals are equal to Π m. Here, we present a method for constructing n copulas, which we shall use in the sequel. Proposition 2.1. Let C = {C t } t I m 1 t I m 1. Then C : I n I given by be a family in C n m+1 indexed by a parameter C(u) = um 1 is in C n, provided that the above integral exists. C t (u m,, u n ) dt 1 dt m 1. (2)

3 On a problem by Schweizer and Sklar 289 P r o o f. It is immediate to prove that the function C given by (2) satisfies (C1) and (C2). In order to prove that C is n increasing, consider the n box B = n [u i, v i ] in I n, u i v i for any i {1, 2,, n}. Then, we have that V C (B) = v1 u 1 vm 1 u m 1 V Ct ([u m, v m ] [u n, v n ]) dt 1 dt m 1, which is non negative because, for any t I m 1, C t belongs to C n m+1 and hence t V Ct ([u m, v m ] [u n, v n ]) is non negative. Example 2.2. Let C and {C t } t I m 1 be in C n m+1 and suppose that C t = C for every t I m 1. Then elementary integration yields ( m 1 ) D(u) = u i C(u m,, u n ), which is the n d.f. of the random vector (U 1, U 2,, U n ) such that: U i are uniformly distributed on I, C is the d.f. of (U m, U m+1,, U n ), Π m 1 is the d.f. of (U 1,, U m 1 ), and (U 1,, U m 1 ) and (U m,, U n ) are independent random vectors. Example 2.3. Let {C (t1,t 2)} (t1,t 2) I 2 be in C 2 defined by { Π 2 (u 1, u 2 ), t 1 1 C (t1,t 2)(u 1, u 2 ) = 2, M 2 (u 1, u 2 ), otherwise. Then, by using Proposition 2.1, we obtain that C 4 : I 4 I given by C 4 (u 1, u 2, u 3, u 4 ) = = u2 C (t1,t 2)(u 3, u 4 ) dt 1 dt 2 { Π 4 (u 1, u 2, u 3, u 4 ), u 1 1 2, u 2u 3u ( u 1 2) 1 u2 M 2 (u 3, u 4 ), otherwise, is a 4 copula such that all its 2 marginals are equal to Π 2. The copula C 4 can be also obtained by means of a gluing construction [11]. Remark 2.4. If C C n, then, for every permutation σ = (σ 1, σ 2,, σ n ) of (1, 2,, n) the function C σ : I n I given by C σ (u 1,, u n ) = C(u σ1,, u σn ) is also in C n. In particular, if C is the copula given by (2), then, for every permutation σ = (σ 1, σ 2,, σ n ) of (1, 2,, n), C σ : I n I given by is also in C n. C σ (u) = uσm 1 C t (u σm,, u σn ) dt 1 dt m 1 (3)

4 29 F. DURANTE 3. DESCRIPTION OF A SPECIAL CLASS OF COPULAS Following our approach, the description of the class of all possible joint d.f. s of a random vector X = (X 1, X 2,, X n ) such that, for a given m N, 2 m < n, every sub-vector of m elements in X is formed by independent r.v. s, is equivalent to the description of the class C n (Π m ). The elements of such a class are described in the following result. Theorem 3.1. Let n, m N, 2 m < n. The following statements are equivalent: (a) C C n (Π m ); (b) for every σ P n,m, there exists a family C σ = {Ct σ } t I m 1 for every u I n, in C n m+1 such that, C(u) = uσm 1 Ct σ (u σm,, u σn ) dt 1 dt m 1. (4) P r o o f. Let C be in C n (Π m ). Then, there exist a probability space (Ω, F, P) and a random vector U = (U 1, U 2,, U n ), U i uniformly distributed on I for every i {1, 2,, n}, such that C is the joint d.f. of U. Let σ P n,m. Then, for each u I n, C(u) = P(U 1 u 1,, U n u n ) = uσm 1 F σ t (u σm,, u σn ) dt 1 dt m 1, where, for every t = (t 1, t 2,, t m 1 ) I m 1, Ft σ : I n m+1 I defined by ( n ) Ft σ (u σm,, u σn ) = P {U σi u σi } U σ1 = t 1,, U σm 1 = t m 1, i=m is the (conditional) d.f. of (U σm,, U σn ) given (U σ1 = t 1,, U σm 1 = t m 1 ). The one dimensional marginals of Ft σ are uniformly distributed on I, because any subset of m elements in {U 1, U 2,, U n } is composed by independent r.v. s. Therefore, Ft σ is a copula and (b) follows. In the other direction, let C : I n I be such that, for every σ P n,m there exists a family C σ = {Ct σ } t I m 1 C n m+1 such that C can be represented in the form (4). Because of Proposition 2.1 (and Remark 2.4), C is a copula. Therefore, we have only to prove that all the m marginals of C are equal to Π m. To this end, let C m be the m marginal of C obtained by setting equal to 1 the arguments of C with indices ξ 1 < ξ 2 < < ξ n m, viz. C m (u 1,, u m ) = C(ũ), where ũ I n is obtained by setting ũ i = 1 for i {ξ 1,, ξ n m }, and ũ i = u i, otherwise. Consider the (unique) permutation ξ P n,m given by ξ = (ξ n m+1,, ξ n, ξ 1,, ξ n m ).

5 On a problem by Schweizer and Sklar 291 Then there exists a family C bξ ξ = {C b t } t I m 1 in C n m+1 such that uξn m+1 uξn 1 ξ C(u) = C b t (u ξ n, u ξ1, u ξn m ) dt 1 dt m 1. (5) ξ Since C b t satisfies (C2), equality (5) implies that Cm = Π m. For the arbitrariness of ξ 1, ξ 2,, ξ n m, it follows that C C n (Π m ). Remark 3.2. Since Theorem 3.1, if C C n (Π m ), then there exist ( n m) families C σ = {Ct σ } t I m 1 in C n m+1, each family associated with σ P n,m, such that C can be written in ( n m) different forms by means of (4). Moreover, for a fixed σ Pn,m, {Ct σ } t I m 1 is not uniquely determined: in fact, there exist infinitely many families D σ = {Dt σ } t I m 1 such that Ct σ Dt σ for every t belonging to a subset of I m 1 with (m 1) dimensional Lebesgue measure, and C can be represented in terms of D σ by means of (4). In the case n = 3 and m = 2, Theorem 3.1 can be reformulated in this form. Corollary 3.3. A 3 copula C 3 C 3 (Π 2 ) if, and only if, there exist three families of 2 copulas {C (1) t } t I, {C (2) t } t I and {C (3) t } t I, such that C 3 (u 1, u 2, u 3 ) = C (1) t (u 2, u 3 ) dt = u2 C (2) t (u 1, u 3 ) dt = u3 C (3) t (u 1, u 2 ) dt. In particular, we have that, for every i {1, 2, 3}, 1 C (i) t (u 1, u 2 ) dt = u 1 u 2. (6) A method for constructing families of 2 copulas that satisfy (6) is provided in [8, Example 3.1]. Specifically, for any 2 copula C, we can construct the family of 2 copula {C t } t I given by { C(1 t + u 1, u 2 ) C(1 t, u 2 ), u 1 t, C t (u 1, u 2 ) = u 2 C(1 t, u 2 ) + C(u 1 t, u 2 ), u 1 > t, which satisfies condition (6). Example 3.4. Let C θ be a member of the Eyraud Farlie Gumbel Morgenstern family of 3 copulas given by C θ (u 1, u 2, u 3 ) = u 1 u 2 u 3 (1 + θ(1 u 1 )(1 u 2 )(1 u 3 )), where θ [ 1, 1] (see [8]). Then C θ has all the 2 marginals equal to Π 2 and it can be expressed, for example, into the form C θ (u 1, u 2, u 3 ) = C σ t (u σ2, u σ3 ) dt,

6 292 F. DURANTE where σ = (σ 1, σ 2, σ 3 ) P 3,2, and C = {C σ t } t I is the family of 2 copulas given by for every t I and σ P 3,2. C σ t (u, v) = uv + θuv(1 u)(1 v)(1 2t), Theorem 3.1 can be rewritten in a simpler form if we suppose that C C n (Π m ) is exchangeable, viz. it does not change under permutation of its arguments. Corollary 3.5. Let n, m be in N, 2 m < n. Let C be an exchangeable copula. Then C C n (Π m ) if, and only if, there exists a family C = {C t } t I m 1 in C n m+1 such that, for every u I n, um 1 C(u) = C t (u m,, u n ) dt 1 dt m 1. (7) P r o o f. Let n, m be in N, 2 m < n. Let C be exchangeable. If C C n (Π m ), then Theorem 3.1 ensures that there exists a family C = {C t } t I m 1 in C n m+1 such that, C admits the representation (4). Conversely, if C can be represented in the form (7), then C(u 1,, u m, 1,, 1) = m u i, and, because C is exchangeable, all its m marginal d.f. s are equal to m u i, and, thus, C C n (Π m ). Pointwise upper and lower bounds for the class C n (Π m ) have been given in [4] (when n = 3 and m = 2, see also [5]). Theorem 3.1 provides also a way for obtaining them. In fact, for every σ P n,m there exists a family C σ = {Ct σ } t I m 1 in C n m+1 such that C C n can be represented in the form (4). Now, because every copula satisfies the inequalities (1), it follows that, for every u I n m+1 and for every t I m 1, W n m+1 (u) C σ t (u) M n m+1 (u). Thus, the following inequalities can be easily derived: where we define C L (u) = max σ P n,m C U (u) = min σ P n,m C L (u) C(u) C U (u), (8) {( m 1 {( m 1 u σi ) u σi ) W n m+1 (u σm,, u σn ) M n m+1 (u σm,, u σn ) An improvement of these bounds can be achieved by writing the expression for the survival d.f. associated with C and impose that it is non negative (see [7] for this procedure). } },.

7 On a problem by Schweizer and Sklar 293 ACKNOWLEDGEMENTS The support of School of Economics and Management, Free University of Bozen Bolzano, via the project Risk and Dependence is acknowledged. (Received August 4, 211) R E F E R E N C E S [1] V. Beneš and J. Štěpán, eds.: Distributions With Given Marginals and Moment Problems. Kluwer Academic Publishers, Dordrecht [2] C. M. Cuadras, J. Fortiana, and J. A. Rodriguez-Lallena, eds.: Distributions With Given Marginals and Statistical Modelling. Kluwer Academic Publishers, Dordrecht 22. Papers from the meeting held in Barcelona 2. [3] G. Dall Aglio, S. Kotz, and G. Salinetti, eds.: Advances in Probability Distributions with Given Marginals. Mathematics and its Applications 67, Kluwer Academic Publishers Group, Dordrecht Beyond the Copulas, Papers from the Symposium on Distributions with Given Marginals held in Rome 199. [4] P. Deheuvels: Indépendance multivariée partielle et inégalités de Fréchet. In: Studies in Probability and Related Topics, Nagard, Rome 1983, pp [5] F. Durante, E. P. Klement, and J. J. Quesada-Molina: Bounds for trivariate copulas with given bivariate marginals. J. Inequal. Appl. 28 (28), 1 9. [6] P. Jaworski, F. Durante, W. Härdle, and T. Rychlik, eds.: Copula Theory and its Applications. Lecture Notes in Statistics Proceedings 198, Springer, Berlin Heidelberg 21. [7] H. Joe: Multivariate Models and Dependence Concepts. Monographs on Statistics and Applied Probability 73, Chapman & Hall, London [8] R. B. Nelsen: An Introduction to Copulas. Second edition. Springer Series in Statistics, Springer, New York 26. [9] L. Rüschendorf, B. Schweizer, and M. D. Taylor, eds.: Distributions with Fixed Marginals and Related Topics. Institute of Mathematical Statistics, Lecture Notes Monograph Series 28, Hayward [1] B. Schweizer and A. Sklar: Probabilistic Metric Spaces. North-Holland Series in Probability and Applied Mathematics. North-Holland Publishing Co., New York Reprinted, Dover, Mineola 25. [11] K. F. Siburg and P. A. Stoimenov: Gluing copulas. Comm. Statist. Theory Methods 37 (28), 19, [12] A. Sklar: Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris 8 (1959), Fabrizio Durante, School of Economics and Management, Free University of Bozen Bolzano, Bolzano. Italy. fabrizio.durante@unibz.it

Lindner, Szimayer: A Limit Theorem for Copulas

Lindner, Szimayer: A Limit Theorem for Copulas Lindner, Szimayer: A Limit Theorem for Copulas Sonderforschungsbereich 386, Paper 433 (2005) Online unter: http://epub.ub.uni-muenchen.de/ Projektpartner A Limit Theorem for Copulas Alexander Lindner Alexander

More information

INTERNATIONAL JOURNAL FOR INNOVATIVE RESEARCH IN MULTIDISCIPLINARY FIELD ISSN Volume - 3, Issue - 2, Feb

INTERNATIONAL JOURNAL FOR INNOVATIVE RESEARCH IN MULTIDISCIPLINARY FIELD ISSN Volume - 3, Issue - 2, Feb Copula Approach: Correlation Between Bond Market and Stock Market, Between Developed and Emerging Economies Shalini Agnihotri LaL Bahadur Shastri Institute of Management, Delhi, India. Email - agnihotri123shalini@gmail.com

More information

On the Lower Arbitrage Bound of American Contingent Claims

On the Lower Arbitrage Bound of American Contingent Claims On the Lower Arbitrage Bound of American Contingent Claims Beatrice Acciaio Gregor Svindland December 2011 Abstract We prove that in a discrete-time market model the lower arbitrage bound of an American

More information

Introduction to vine copulas

Introduction to vine copulas Introduction to vine copulas Nicole Krämer & Ulf Schepsmeier Technische Universität München [kraemer, schepsmeier]@ma.tum.de NIPS Workshop, Granada, December 18, 2011 Krämer & Schepsmeier (TUM) Introduction

More information

Financial Risk Management

Financial Risk Management Financial Risk Management Professor: Thierry Roncalli Evry University Assistant: Enareta Kurtbegu Evry University Tutorial exercices #4 1 Correlation and copulas 1. The bivariate Gaussian copula is given

More information

Optimal Allocation of Policy Limits and Deductibles

Optimal Allocation of Policy Limits and Deductibles Optimal Allocation of Policy Limits and Deductibles Ka Chun Cheung Email: kccheung@math.ucalgary.ca Tel: +1-403-2108697 Fax: +1-403-2825150 Department of Mathematics and Statistics, University of Calgary,

More information

Synthetic CDO Pricing Using the Student t Factor Model with Random Recovery

Synthetic CDO Pricing Using the Student t Factor Model with Random Recovery Synthetic CDO Pricing Using the Student t Factor Model with Random Recovery UNSW Actuarial Studies Research Symposium 2006 University of New South Wales Tom Hoedemakers Yuri Goegebeur Jurgen Tistaert Tom

More information

Equivalence between Semimartingales and Itô Processes

Equivalence between Semimartingales and Itô Processes International Journal of Mathematical Analysis Vol. 9, 215, no. 16, 787-791 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/1.12988/ijma.215.411358 Equivalence between Semimartingales and Itô Processes

More information

In Discrete Time a Local Martingale is a Martingale under an Equivalent Probability Measure

In Discrete Time a Local Martingale is a Martingale under an Equivalent Probability Measure In Discrete Time a Local Martingale is a Martingale under an Equivalent Probability Measure Yuri Kabanov 1,2 1 Laboratoire de Mathématiques, Université de Franche-Comté, 16 Route de Gray, 253 Besançon,

More information

3.4 Copula approach for modeling default dependency. Two aspects of modeling the default times of several obligors

3.4 Copula approach for modeling default dependency. Two aspects of modeling the default times of several obligors 3.4 Copula approach for modeling default dependency Two aspects of modeling the default times of several obligors 1. Default dynamics of a single obligor. 2. Model the dependence structure of defaults

More information

Understanding the dependence in financial models with copulas

Understanding the dependence in financial models with copulas Understanding the dependence in financial models with copulas Stochastic Models in Finance Ecole Polytechnique, Paris, 04/23/2001 Thierry Roncalli Groupe de Recherche Opérationnelle Crédit Lyonnais http://gro.creditlyonnais.fr

More information

An Introduction to Copulas with Applications

An Introduction to Copulas with Applications An Introduction to Copulas with Applications Svenska Aktuarieföreningen Stockholm 4-3- Boualem Djehiche, KTH & Skandia Liv Henrik Hult, University of Copenhagen I Introduction II Introduction to copulas

More information

A Property Equivalent to n-permutability for Infinite Groups

A Property Equivalent to n-permutability for Infinite Groups Journal of Algebra 221, 570 578 (1999) Article ID jabr.1999.7996, available online at http://www.idealibrary.com on A Property Equivalent to n-permutability for Infinite Groups Alireza Abdollahi* and Aliakbar

More information

Basic Concepts and Examples in Finance

Basic Concepts and Examples in Finance Basic Concepts and Examples in Finance Bernardo D Auria email: bernardo.dauria@uc3m.es web: www.est.uc3m.es/bdauria July 5, 2017 ICMAT / UC3M The Financial Market The Financial Market We assume there are

More information

Introduction to Probability Theory and Stochastic Processes for Finance Lecture Notes

Introduction to Probability Theory and Stochastic Processes for Finance Lecture Notes Introduction to Probability Theory and Stochastic Processes for Finance Lecture Notes Fabio Trojani Department of Economics, University of St. Gallen, Switzerland Correspondence address: Fabio Trojani,

More information

4: SINGLE-PERIOD MARKET MODELS

4: SINGLE-PERIOD MARKET MODELS 4: SINGLE-PERIOD MARKET MODELS Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2016 M. Rutkowski (USydney) Slides 4: Single-Period Market Models 1 / 87 General Single-Period

More information

Ruin with Insurance and Financial Risks Following a Dependent May 29 - June Structure 1, / 40

Ruin with Insurance and Financial Risks Following a Dependent May 29 - June Structure 1, / 40 1 Ruin with Insurance and Financial Risks Following a Dependent May 29 - June Structure 1, 2014 1 / 40 Ruin with Insurance and Financial Risks Following a Dependent Structure Jiajun Liu Department of Mathematical

More information

Extreme Return-Volume Dependence in East-Asian. Stock Markets: A Copula Approach

Extreme Return-Volume Dependence in East-Asian. Stock Markets: A Copula Approach Extreme Return-Volume Dependence in East-Asian Stock Markets: A Copula Approach Cathy Ning a and Tony S. Wirjanto b a Department of Economics, Ryerson University, 350 Victoria Street, Toronto, ON Canada,

More information

PORTFOLIO OPTIMIZATION AND SHARPE RATIO BASED ON COPULA APPROACH

PORTFOLIO OPTIMIZATION AND SHARPE RATIO BASED ON COPULA APPROACH VOLUME 6, 01 PORTFOLIO OPTIMIZATION AND SHARPE RATIO BASED ON COPULA APPROACH Mária Bohdalová I, Michal Gregu II Comenius University in Bratislava, Slovakia In this paper we will discuss the allocation

More information

A lower bound on seller revenue in single buyer monopoly auctions

A lower bound on seller revenue in single buyer monopoly auctions A lower bound on seller revenue in single buyer monopoly auctions Omer Tamuz October 7, 213 Abstract We consider a monopoly seller who optimally auctions a single object to a single potential buyer, with

More information

THE NUMBER OF UNARY CLONES CONTAINING THE PERMUTATIONS ON AN INFINITE SET

THE NUMBER OF UNARY CLONES CONTAINING THE PERMUTATIONS ON AN INFINITE SET THE NUMBER OF UNARY CLONES CONTAINING THE PERMUTATIONS ON AN INFINITE SET MICHAEL PINSKER Abstract. We calculate the number of unary clones (submonoids of the full transformation monoid) containing the

More information

OPTIMAL PORTFOLIO OF THE GOVERNMENT PENSION INVESTMENT FUND BASED ON THE SYSTEMIC RISK EVALUATED BY A NEW ASYMMETRIC COPULA

OPTIMAL PORTFOLIO OF THE GOVERNMENT PENSION INVESTMENT FUND BASED ON THE SYSTEMIC RISK EVALUATED BY A NEW ASYMMETRIC COPULA Advances in Science, Technology and Environmentology Special Issue on the Financial & Pension Mathematical Science Vol. B13 (2016.3), 21 38 OPTIMAL PORTFOLIO OF THE GOVERNMENT PENSION INVESTMENT FUND BASED

More information

Non-semimartingales in finance

Non-semimartingales in finance Non-semimartingales in finance Pricing and Hedging Options with Quadratic Variation Tommi Sottinen University of Vaasa 1st Northern Triangular Seminar 9-11 March 2009, Helsinki University of Technology

More information

Synthetic CDO Pricing Using the Student t Factor Model with Random Recovery

Synthetic CDO Pricing Using the Student t Factor Model with Random Recovery Synthetic CDO Pricing Using the Student t Factor Model with Random Recovery Yuri Goegebeur Tom Hoedemakers Jurgen Tistaert Abstract A synthetic collateralized debt obligation, or synthetic CDO, is a transaction

More information

Martingales. by D. Cox December 2, 2009

Martingales. by D. Cox December 2, 2009 Martingales by D. Cox December 2, 2009 1 Stochastic Processes. Definition 1.1 Let T be an arbitrary index set. A stochastic process indexed by T is a family of random variables (X t : t T) defined on a

More information

6.254 : Game Theory with Engineering Applications Lecture 3: Strategic Form Games - Solution Concepts

6.254 : Game Theory with Engineering Applications Lecture 3: Strategic Form Games - Solution Concepts 6.254 : Game Theory with Engineering Applications Lecture 3: Strategic Form Games - Solution Concepts Asu Ozdaglar MIT February 9, 2010 1 Introduction Outline Review Examples of Pure Strategy Nash Equilibria

More information

Catastrophic crop insurance effectiveness: does it make a difference how yield losses are conditioned?

Catastrophic crop insurance effectiveness: does it make a difference how yield losses are conditioned? Paper prepared for the 23 rd EAAE Seminar PRICE VOLATILITY AND FARM INCOME STABILISATION Modelling Outcomes and Assessing Market and Policy Based Responses Dublin, February 23-24, 202 Catastrophic crop

More information

On the Distribution and Its Properties of the Sum of a Normal and a Doubly Truncated Normal

On the Distribution and Its Properties of the Sum of a Normal and a Doubly Truncated Normal The Korean Communications in Statistics Vol. 13 No. 2, 2006, pp. 255-266 On the Distribution and Its Properties of the Sum of a Normal and a Doubly Truncated Normal Hea-Jung Kim 1) Abstract This paper

More information

Martingale Pricing Theory in Discrete-Time and Discrete-Space Models

Martingale Pricing Theory in Discrete-Time and Discrete-Space Models IEOR E4707: Foundations of Financial Engineering c 206 by Martin Haugh Martingale Pricing Theory in Discrete-Time and Discrete-Space Models These notes develop the theory of martingale pricing in a discrete-time,

More information

Lossy compression of permutations

Lossy compression of permutations Lossy compression of permutations The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation As Published Publisher Wang, Da, Arya Mazumdar,

More information

Last Time. Martingale inequalities Martingale convergence theorem Uniformly integrable martingales. Today s lecture: Sections 4.4.1, 5.

Last Time. Martingale inequalities Martingale convergence theorem Uniformly integrable martingales. Today s lecture: Sections 4.4.1, 5. MATH136/STAT219 Lecture 21, November 12, 2008 p. 1/11 Last Time Martingale inequalities Martingale convergence theorem Uniformly integrable martingales Today s lecture: Sections 4.4.1, 5.3 MATH136/STAT219

More information

On Complexity of Multistage Stochastic Programs

On Complexity of Multistage Stochastic Programs On Complexity of Multistage Stochastic Programs Alexander Shapiro School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0205, USA e-mail: ashapiro@isye.gatech.edu

More information

Lesson 3: Basic theory of stochastic processes

Lesson 3: Basic theory of stochastic processes Lesson 3: Basic theory of stochastic processes Dipartimento di Ingegneria e Scienze dell Informazione e Matematica Università dell Aquila, umberto.triacca@univaq.it Probability space We start with some

More information

Pricing Exotic Options Under a Higher-order Hidden Markov Model

Pricing Exotic Options Under a Higher-order Hidden Markov Model Pricing Exotic Options Under a Higher-order Hidden Markov Model Wai-Ki Ching Tak-Kuen Siu Li-min Li 26 Jan. 2007 Abstract In this paper, we consider the pricing of exotic options when the price dynamic

More information

Building Infinite Processes from Regular Conditional Probability Distributions

Building Infinite Processes from Regular Conditional Probability Distributions Chapter 3 Building Infinite Processes from Regular Conditional Probability Distributions Section 3.1 introduces the notion of a probability kernel, which is a useful way of systematizing and extending

More information

Stochastic Integral Representation of One Stochastically Non-smooth Wiener Functional

Stochastic Integral Representation of One Stochastically Non-smooth Wiener Functional Bulletin of TICMI Vol. 2, No. 2, 26, 24 36 Stochastic Integral Representation of One Stochastically Non-smooth Wiener Functional Hanna Livinska a and Omar Purtukhia b a Taras Shevchenko National University

More information

Lecture Quantitative Finance Spring Term 2015

Lecture Quantitative Finance Spring Term 2015 implied Lecture Quantitative Finance Spring Term 2015 : May 7, 2015 1 / 28 implied 1 implied 2 / 28 Motivation and setup implied the goal of this chapter is to treat the implied which requires an algorithm

More information

The Limiting Distribution for the Number of Symbol Comparisons Used by QuickSort is Nondegenerate (Extended Abstract)

The Limiting Distribution for the Number of Symbol Comparisons Used by QuickSort is Nondegenerate (Extended Abstract) The Limiting Distribution for the Number of Symbol Comparisons Used by QuickSort is Nondegenerate (Extended Abstract) Patrick Bindjeme 1 James Allen Fill 1 1 Department of Applied Mathematics Statistics,

More information

GUESSING MODELS IMPLY THE SINGULAR CARDINAL HYPOTHESIS arxiv: v1 [math.lo] 25 Mar 2019

GUESSING MODELS IMPLY THE SINGULAR CARDINAL HYPOTHESIS arxiv: v1 [math.lo] 25 Mar 2019 GUESSING MODELS IMPLY THE SINGULAR CARDINAL HYPOTHESIS arxiv:1903.10476v1 [math.lo] 25 Mar 2019 Abstract. In this article we prove three main theorems: (1) guessing models are internally unbounded, (2)

More information

INTRODUCTION TO ARBITRAGE PRICING OF FINANCIAL DERIVATIVES

INTRODUCTION TO ARBITRAGE PRICING OF FINANCIAL DERIVATIVES INTRODUCTION TO ARBITRAGE PRICING OF FINANCIAL DERIVATIVES Marek Rutkowski Faculty of Mathematics and Information Science Warsaw University of Technology 00-661 Warszawa, Poland 1 Call and Put Spot Options

More information

6.207/14.15: Networks Lecture 10: Introduction to Game Theory 2

6.207/14.15: Networks Lecture 10: Introduction to Game Theory 2 6.207/14.15: Networks Lecture 10: Introduction to Game Theory 2 Daron Acemoglu and Asu Ozdaglar MIT October 14, 2009 1 Introduction Outline Review Examples of Pure Strategy Nash Equilibria Mixed Strategies

More information

Non replication of options

Non replication of options Non replication of options Christos Kountzakis, Ioannis A Polyrakis and Foivos Xanthos June 30, 2008 Abstract In this paper we study the scarcity of replication of options in the two period model of financial

More information

CHARACTERIZATION OF CLOSED CONVEX SUBSETS OF R n

CHARACTERIZATION OF CLOSED CONVEX SUBSETS OF R n CHARACTERIZATION OF CLOSED CONVEX SUBSETS OF R n Chebyshev Sets A subset S of a metric space X is said to be a Chebyshev set if, for every x 2 X; there is a unique point in S that is closest to x: Put

More information

Interest rate models in continuous time

Interest rate models in continuous time slides for the course Interest rate theory, University of Ljubljana, 2012-13/I, part IV József Gáll University of Debrecen Nov. 2012 Jan. 2013, Ljubljana Continuous time markets General assumptions, notations

More information

Forecast Horizons for Production Planning with Stochastic Demand

Forecast Horizons for Production Planning with Stochastic Demand Forecast Horizons for Production Planning with Stochastic Demand Alfredo Garcia and Robert L. Smith Department of Industrial and Operations Engineering Universityof Michigan, Ann Arbor MI 48109 December

More information

Operational risk Dependencies and the Determination of Risk Capital

Operational risk Dependencies and the Determination of Risk Capital Operational risk Dependencies and the Determination of Risk Capital Stefan Mittnik Chair of Financial Econometrics, LMU Munich & CEQURA finmetrics@stat.uni-muenchen.de Sandra Paterlini EBS Universität

More information

The ruin probabilities of a multidimensional perturbed risk model

The ruin probabilities of a multidimensional perturbed risk model MATHEMATICAL COMMUNICATIONS 231 Math. Commun. 18(2013, 231 239 The ruin probabilities of a multidimensional perturbed risk model Tatjana Slijepčević-Manger 1, 1 Faculty of Civil Engineering, University

More information

Copula-Based Pairs Trading Strategy

Copula-Based Pairs Trading Strategy Copula-Based Pairs Trading Strategy Wenjun Xie and Yuan Wu Division of Banking and Finance, Nanyang Business School, Nanyang Technological University, Singapore ABSTRACT Pairs trading is a technique that

More information

4 Martingales in Discrete-Time

4 Martingales in Discrete-Time 4 Martingales in Discrete-Time Suppose that (Ω, F, P is a probability space. Definition 4.1. A sequence F = {F n, n = 0, 1,...} is called a filtration if each F n is a sub-σ-algebra of F, and F n F n+1

More information

MATH 5510 Mathematical Models of Financial Derivatives. Topic 1 Risk neutral pricing principles under single-period securities models

MATH 5510 Mathematical Models of Financial Derivatives. Topic 1 Risk neutral pricing principles under single-period securities models MATH 5510 Mathematical Models of Financial Derivatives Topic 1 Risk neutral pricing principles under single-period securities models 1.1 Law of one price and Arrow securities 1.2 No-arbitrage theory and

More information

ADVANCED OPERATIONAL RISK MODELLING IN BANKS AND INSURANCE COMPANIES

ADVANCED OPERATIONAL RISK MODELLING IN BANKS AND INSURANCE COMPANIES Small business banking and financing: a global perspective Cagliari, 25-26 May 2007 ADVANCED OPERATIONAL RISK MODELLING IN BANKS AND INSURANCE COMPANIES C. Angela, R. Bisignani, G. Masala, M. Micocci 1

More information

Finite Memory and Imperfect Monitoring

Finite Memory and Imperfect Monitoring Federal Reserve Bank of Minneapolis Research Department Finite Memory and Imperfect Monitoring Harold L. Cole and Narayana Kocherlakota Working Paper 604 September 2000 Cole: U.C.L.A. and Federal Reserve

More information

3.2 No-arbitrage theory and risk neutral probability measure

3.2 No-arbitrage theory and risk neutral probability measure Mathematical Models in Economics and Finance Topic 3 Fundamental theorem of asset pricing 3.1 Law of one price and Arrow securities 3.2 No-arbitrage theory and risk neutral probability measure 3.3 Valuation

More information

Department of Econometrics and Business Statistics

Department of Econometrics and Business Statistics ISSN 1440-771X Australia Department of Econometrics and Business Statistics http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/ Assessing Dependence Changes in the Asian Financial Market Returns Using

More information

- Introduction to Mathematical Finance -

- Introduction to Mathematical Finance - - Introduction to Mathematical Finance - Lecture Notes by Ulrich Horst The objective of this course is to give an introduction to the probabilistic techniques required to understand the most widely used

More information

Constructive martingale representation using Functional Itô Calculus: a local martingale extension

Constructive martingale representation using Functional Itô Calculus: a local martingale extension Mathematical Statistics Stockholm University Constructive martingale representation using Functional Itô Calculus: a local martingale extension Kristoffer Lindensjö Research Report 216:21 ISSN 165-377

More information

Markov Decision Processes II

Markov Decision Processes II Markov Decision Processes II Daisuke Oyama Topics in Economic Theory December 17, 2014 Review Finite state space S, finite action space A. The value of a policy σ A S : v σ = β t Q t σr σ, t=0 which satisfies

More information

Arbitrage Theory without a Reference Probability: challenges of the model independent approach

Arbitrage Theory without a Reference Probability: challenges of the model independent approach Arbitrage Theory without a Reference Probability: challenges of the model independent approach Matteo Burzoni Marco Frittelli Marco Maggis June 30, 2015 Abstract In a model independent discrete time financial

More information

arxiv: v1 [q-fin.pm] 13 Mar 2014

arxiv: v1 [q-fin.pm] 13 Mar 2014 MERTON PORTFOLIO PROBLEM WITH ONE INDIVISIBLE ASSET JAKUB TRYBU LA arxiv:143.3223v1 [q-fin.pm] 13 Mar 214 Abstract. In this paper we consider a modification of the classical Merton portfolio optimization

More information

Mean-Variance Optimal Portfolios in the Presence of a Benchmark with Applications to Fraud Detection

Mean-Variance Optimal Portfolios in the Presence of a Benchmark with Applications to Fraud Detection Mean-Variance Optimal Portfolios in the Presence of a Benchmark with Applications to Fraud Detection Carole Bernard (University of Waterloo) Steven Vanduffel (Vrije Universiteit Brussel) Fields Institute,

More information

MEASURING PORTFOLIO RISKS USING CONDITIONAL COPULA-AR-GARCH MODEL

MEASURING PORTFOLIO RISKS USING CONDITIONAL COPULA-AR-GARCH MODEL MEASURING PORTFOLIO RISKS USING CONDITIONAL COPULA-AR-GARCH MODEL Isariya Suttakulpiboon MSc in Risk Management and Insurance Georgia State University, 30303 Atlanta, Georgia Email: suttakul.i@gmail.com,

More information

Estimation of Value at Risk and ruin probability for diffusion processes with jumps

Estimation of Value at Risk and ruin probability for diffusion processes with jumps Estimation of Value at Risk and ruin probability for diffusion processes with jumps Begoña Fernández Universidad Nacional Autónoma de México joint work with Laurent Denis and Ana Meda PASI, May 21 Begoña

More information

Approximation of Continuous-State Scenario Processes in Multi-Stage Stochastic Optimization and its Applications

Approximation of Continuous-State Scenario Processes in Multi-Stage Stochastic Optimization and its Applications Approximation of Continuous-State Scenario Processes in Multi-Stage Stochastic Optimization and its Applications Anna Timonina University of Vienna, Abraham Wald PhD Program in Statistics and Operations

More information

Approximating a multifactor di usion on a tree.

Approximating a multifactor di usion on a tree. Approximating a multifactor di usion on a tree. September 2004 Abstract A new method of approximating a multifactor Brownian di usion on a tree is presented. The method is based on local coupling of the

More information

GPD-POT and GEV block maxima

GPD-POT and GEV block maxima Chapter 3 GPD-POT and GEV block maxima This chapter is devoted to the relation between POT models and Block Maxima (BM). We only consider the classical frameworks where POT excesses are assumed to be GPD,

More information

Girsanov s Theorem. Bernardo D Auria web: July 5, 2017 ICMAT / UC3M

Girsanov s Theorem. Bernardo D Auria   web:   July 5, 2017 ICMAT / UC3M Girsanov s Theorem Bernardo D Auria email: bernardo.dauria@uc3m.es web: www.est.uc3m.es/bdauria July 5, 2017 ICMAT / UC3M Girsanov s Theorem Decomposition of P-Martingales as Q-semi-martingales Theorem

More information

No-arbitrage theorem for multi-factor uncertain stock model with floating interest rate

No-arbitrage theorem for multi-factor uncertain stock model with floating interest rate Fuzzy Optim Decis Making 217 16:221 234 DOI 117/s17-16-9246-8 No-arbitrage theorem for multi-factor uncertain stock model with floating interest rate Xiaoyu Ji 1 Hua Ke 2 Published online: 17 May 216 Springer

More information

OPTION PRICE WHEN THE STOCK IS A SEMIMARTINGALE

OPTION PRICE WHEN THE STOCK IS A SEMIMARTINGALE DOI: 1.1214/ECP.v7-149 Elect. Comm. in Probab. 7 (22) 79 83 ELECTRONIC COMMUNICATIONS in PROBABILITY OPTION PRICE WHEN THE STOCK IS A SEMIMARTINGALE FIMA KLEBANER Department of Mathematics & Statistics,

More information

Viability, Arbitrage and Preferences

Viability, Arbitrage and Preferences Viability, Arbitrage and Preferences H. Mete Soner ETH Zürich and Swiss Finance Institute Joint with Matteo Burzoni, ETH Zürich Frank Riedel, University of Bielefeld Thera Stochastics in Honor of Ioannis

More information

Exponential utility maximization under partial information

Exponential utility maximization under partial information Exponential utility maximization under partial information Marina Santacroce Politecnico di Torino Joint work with M. Mania AMaMeF 5-1 May, 28 Pitesti, May 1th, 28 Outline Expected utility maximization

More information

2. Copula Methods Background

2. Copula Methods Background 1. Introduction Stock futures markets provide a channel for stock holders potentially transfer risks. Effectiveness of such a hedging strategy relies heavily on the accuracy of hedge ratio estimation.

More information

American Foreign Exchange Options and some Continuity Estimates of the Optimal Exercise Boundary with respect to Volatility

American Foreign Exchange Options and some Continuity Estimates of the Optimal Exercise Boundary with respect to Volatility American Foreign Exchange Options and some Continuity Estimates of the Optimal Exercise Boundary with respect to Volatility Nasir Rehman Allam Iqbal Open University Islamabad, Pakistan. Outline Mathematical

More information

BROWNIAN MOTION II. D.Majumdar

BROWNIAN MOTION II. D.Majumdar BROWNIAN MOTION II D.Majumdar DEFINITION Let (Ω, F, P) be a probability space. For each ω Ω, suppose there is a continuous function W(t) of t 0 that satisfies W(0) = 0 and that depends on ω. Then W(t),

More information

Outline of Lecture 1. Martin-Löf tests and martingales

Outline of Lecture 1. Martin-Löf tests and martingales Outline of Lecture 1 Martin-Löf tests and martingales The Cantor space. Lebesgue measure on Cantor space. Martin-Löf tests. Basic properties of random sequences. Betting games and martingales. Equivalence

More information

KIER DISCUSSION PAPER SERIES

KIER DISCUSSION PAPER SERIES KIER DISCUSSION PAPER SERIES KYOTO INSTITUTE OF ECONOMIC RESEARCH http://www.kier.kyoto-u.ac.jp/index.html Discussion Paper No. 657 The Buy Price in Auctions with Discrete Type Distributions Yusuke Inami

More information

Dynamic Programming: An overview. 1 Preliminaries: The basic principle underlying dynamic programming

Dynamic Programming: An overview. 1 Preliminaries: The basic principle underlying dynamic programming Dynamic Programming: An overview These notes summarize some key properties of the Dynamic Programming principle to optimize a function or cost that depends on an interval or stages. This plays a key role

More information

Optimal stopping problems for a Brownian motion with a disorder on a finite interval

Optimal stopping problems for a Brownian motion with a disorder on a finite interval Optimal stopping problems for a Brownian motion with a disorder on a finite interval A. N. Shiryaev M. V. Zhitlukhin arxiv:1212.379v1 [math.st] 15 Dec 212 December 18, 212 Abstract We consider optimal

More information

Module 10:Application of stochastic processes in areas like finance Lecture 36:Black-Scholes Model. Stochastic Differential Equation.

Module 10:Application of stochastic processes in areas like finance Lecture 36:Black-Scholes Model. Stochastic Differential Equation. Stochastic Differential Equation Consider. Moreover partition the interval into and define, where. Now by Rieman Integral we know that, where. Moreover. Using the fundamentals mentioned above we can easily

More information

The Azema Yor embedding in non-singular diusions

The Azema Yor embedding in non-singular diusions Stochastic Processes and their Applications 96 2001 305 312 www.elsevier.com/locate/spa The Azema Yor embedding in non-singular diusions J.L. Pedersen a;, G. Peskir b a Department of Mathematics, ETH-Zentrum,

More information

Yao s Minimax Principle

Yao s Minimax Principle Complexity of algorithms The complexity of an algorithm is usually measured with respect to the size of the input, where size may for example refer to the length of a binary word describing the input,

More information

Stochastic Calculus, Application of Real Analysis in Finance

Stochastic Calculus, Application of Real Analysis in Finance , Application of Real Analysis in Finance Workshop for Young Mathematicians in Korea Seungkyu Lee Pohang University of Science and Technology August 4th, 2010 Contents 1 BINOMIAL ASSET PRICING MODEL Contents

More information

The Capital Asset Pricing Model as a corollary of the Black Scholes model

The Capital Asset Pricing Model as a corollary of the Black Scholes model he Capital Asset Pricing Model as a corollary of the Black Scholes model Vladimir Vovk he Game-heoretic Probability and Finance Project Working Paper #39 September 6, 011 Project web site: http://www.probabilityandfinance.com

More information

Copulas: A Tool For Modelling Dependence In Finance

Copulas: A Tool For Modelling Dependence In Finance Copulas: A Tool For Modelling Dependence In Finance Statistical Methods in Integrated Risk Management Frontières en Finance, Paris, 01/26/2001 Thierry Roncalli Groupe de Recherche Opérationnelle Crédit

More information

Solutions of Bimatrix Coalitional Games

Solutions of Bimatrix Coalitional Games Applied Mathematical Sciences, Vol. 8, 2014, no. 169, 8435-8441 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.410880 Solutions of Bimatrix Coalitional Games Xeniya Grigorieva St.Petersburg

More information

SHORT-TERM RELATIVE ARBITRAGE IN VOLATILITY-STABILIZED MARKETS

SHORT-TERM RELATIVE ARBITRAGE IN VOLATILITY-STABILIZED MARKETS SHORT-TERM RELATIVE ARBITRAGE IN VOLATILITY-STABILIZED MARKETS ADRIAN D. BANNER INTECH One Palmer Square Princeton, NJ 8542, USA adrian@enhanced.com DANIEL FERNHOLZ Department of Computer Sciences University

More information

Pricing theory of financial derivatives

Pricing theory of financial derivatives Pricing theory of financial derivatives One-period securities model S denotes the price process {S(t) : t = 0, 1}, where S(t) = (S 1 (t) S 2 (t) S M (t)). Here, M is the number of securities. At t = 1,

More information

Kutay Cingiz, János Flesch, P. Jean-Jacques Herings, Arkadi Predtetchinski. Doing It Now, Later, or Never RM/15/022

Kutay Cingiz, János Flesch, P. Jean-Jacques Herings, Arkadi Predtetchinski. Doing It Now, Later, or Never RM/15/022 Kutay Cingiz, János Flesch, P Jean-Jacques Herings, Arkadi Predtetchinski Doing It Now, Later, or Never RM/15/ Doing It Now, Later, or Never Kutay Cingiz János Flesch P Jean-Jacques Herings Arkadi Predtetchinski

More information

Monte Carlo Methods in Option Pricing. UiO-STK4510 Autumn 2015

Monte Carlo Methods in Option Pricing. UiO-STK4510 Autumn 2015 Monte Carlo Methods in Option Pricing UiO-STK4510 Autumn 015 The Basics of Monte Carlo Method Goal: Estimate the expectation θ = E[g(X)], where g is a measurable function and X is a random variable such

More information

Collinear Triple Hypergraphs and the Finite Plane Kakeya Problem

Collinear Triple Hypergraphs and the Finite Plane Kakeya Problem Collinear Triple Hypergraphs and the Finite Plane Kakeya Problem Joshua Cooper August 14, 006 Abstract We show that the problem of counting collinear points in a permutation (previously considered by the

More information

EFFICIENT MONTE CARLO ALGORITHM FOR PRICING BARRIER OPTIONS

EFFICIENT MONTE CARLO ALGORITHM FOR PRICING BARRIER OPTIONS Commun. Korean Math. Soc. 23 (2008), No. 2, pp. 285 294 EFFICIENT MONTE CARLO ALGORITHM FOR PRICING BARRIER OPTIONS Kyoung-Sook Moon Reprinted from the Communications of the Korean Mathematical Society

More information

Hints on Some of the Exercises

Hints on Some of the Exercises Hints on Some of the Exercises of the book R. Seydel: Tools for Computational Finance. Springer, 00/004/006/009/01. Preparatory Remarks: Some of the hints suggest ideas that may simplify solving the exercises

More information

Blackwell Optimality in Markov Decision Processes with Partial Observation

Blackwell Optimality in Markov Decision Processes with Partial Observation Blackwell Optimality in Markov Decision Processes with Partial Observation Dinah Rosenberg and Eilon Solan and Nicolas Vieille April 6, 2000 Abstract We prove the existence of Blackwell ε-optimal strategies

More information

MODELING DEPENDENCY RELATIONSHIPS WITH COPULAS

MODELING DEPENDENCY RELATIONSHIPS WITH COPULAS MODELING DEPENDENCY RELATIONSHIPS WITH COPULAS Joseph Atwood jatwood@montana.edu and David Buschena buschena.@montana.edu SCC-76 Annual Meeting, Gulf Shores, March 2007 REINSURANCE COMPANY REQUIREMENT

More information

1.1 Basic Financial Derivatives: Forward Contracts and Options

1.1 Basic Financial Derivatives: Forward Contracts and Options Chapter 1 Preliminaries 1.1 Basic Financial Derivatives: Forward Contracts and Options A derivative is a financial instrument whose value depends on the values of other, more basic underlying variables

More information

Hedging of Credit Derivatives in Models with Totally Unexpected Default

Hedging of Credit Derivatives in Models with Totally Unexpected Default Hedging of Credit Derivatives in Models with Totally Unexpected Default T. Bielecki, M. Jeanblanc and M. Rutkowski Carnegie Mellon University Pittsburgh, 6 February 2006 1 Based on N. Vaillant (2001) A

More information

Decision theoretic estimation of the ratio of variances in a bivariate normal distribution 1

Decision theoretic estimation of the ratio of variances in a bivariate normal distribution 1 Decision theoretic estimation of the ratio of variances in a bivariate normal distribution 1 George Iliopoulos Department of Mathematics University of Patras 26500 Rio, Patras, Greece Abstract In this

More information

Optimizing Portfolios

Optimizing Portfolios Optimizing Portfolios An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan 2010 Introduction Investors may wish to adjust the allocation of financial resources including a mixture

More information

A THREE-FACTOR CONVERGENCE MODEL OF INTEREST RATES

A THREE-FACTOR CONVERGENCE MODEL OF INTEREST RATES Proceedings of ALGORITMY 01 pp. 95 104 A THREE-FACTOR CONVERGENCE MODEL OF INTEREST RATES BEÁTA STEHLÍKOVÁ AND ZUZANA ZÍKOVÁ Abstract. A convergence model of interest rates explains the evolution of the

More information

LECTURE 4: BID AND ASK HEDGING

LECTURE 4: BID AND ASK HEDGING LECTURE 4: BID AND ASK HEDGING 1. Introduction One of the consequences of incompleteness is that the price of derivatives is no longer unique. Various strategies for dealing with this exist, but a useful

More information

SOME APPLICATIONS OF OCCUPATION TIMES OF BROWNIAN MOTION WITH DRIFT IN MATHEMATICAL FINANCE

SOME APPLICATIONS OF OCCUPATION TIMES OF BROWNIAN MOTION WITH DRIFT IN MATHEMATICAL FINANCE c Applied Mathematics & Decision Sciences, 31, 63 73 1999 Reprints Available directly from the Editor. Printed in New Zealand. SOME APPLICAIONS OF OCCUPAION IMES OF BROWNIAN MOION WIH DRIF IN MAHEMAICAL

More information