Formulating SALCs with Projection Operators

Size: px
Start display at page:

Download "Formulating SALCs with Projection Operators"

Transcription

1 Formulating SALCs with Projection Operators U The mathematical form of a SALC for a particular symmetry species cannot always be deduced by inspection (e.g., e 1g and e u pi-mos of benzene). U A projection operator is a function that acts on one wave function of the basis set of functions that comprise the SALCs (e.g., one of the six p z orbitals on the carbon atoms in the ring of benzene) to project out the SALC function. U A projection operator for each symmetry species must be applied to the reference function to generate all the symmetry-allowed SALCs. U The projection operator for a given symmetry species contains terms for each and every operation of the group (not just each class of operations).

2 Full-Matrix vs. Character Form of the Projection Operator U The full form of the projection operator function for degenerate species, which often directly generates the set of all SALCs belonging to a degenerate symmetry species, requires use of the full operator matrix for each and every operation of the irreducible representation; i.e., the full-matrix form of the irreducible representation. U Because there are no generally available tabulations of the full-matrix forms of the irreducible representations for groups with degenerate species, a simpler form of the projection operator that uses only the characters for each operation is most often used. ; The character form of the projection operator for degenerate species generates only one of the degenerate SALCs, requiring other means to deduce the companion functions. L We will only use the character form of the projection operator function.

3 The Projection Operator in Characters U The projection operator in character form, P i, acting on a reference function of the basis set, φ t, generates the SALC, S i, for the ith allowed symmetry species as S i % P i φ t ' d i h j R χ R i R j φ t in which d i = dimension of the ith irreducible representation, h = order of the group, χ i R = each operation's character in the ith irreducible representation, R j = the operator for the jth operation of the group. U The term R j φ t gives one of the several basis functions of the set of functions forming the SALCs, in a positive or negative sense. U The summation is taken over all operations of the group, not all classes of operations. U The results P i φ t are not the final SALCs; they need cleaning up.

4 1. The function must be normalized. Requirements for a Wave Function N IΨΨ*dτ = 1, where N is the normalization constant. L We can routinely ignore the factor d i /h in the projection operator expression.. All wave functions must be orthogonal. IΨ i Ψ j dτ = 0 if i j

5 Mathematical Simplification When Taking Products of LCAOs U The P i φ t products have the general form U But in general (a i φ i ± a i+1 φ i+1... ± a n φ n )(b j φ j ± b j+1 φ j+1... ±b m φ m ) Iφ i φ j dτ = δ ij Thus, all φ i φ j (i j) terms vanish, and all φ i φ i or φ j φ j terms (i = j) are unity. L Ignore the cross terms when normalizing or testing for orthogonality!

6 The σ-salcs of MX 6 (O h ) Example: Generate the SALCs for sigma-bonding of six ligands to a central atom in an octahedral MX 6 complex. U From the transformation of six octahedrally arranged vectors pointing toward a central atom, the reducible representation for the six SALCs can be generated and reduced as follows: Γ σ = A 1g + E g + T 1u c d b σ 1 a σ 4 σ 6 σ 5 a σ 3 b σ d c

7 Minimizing the Work U O h has 48 operations, so each projection operator will have 48 terms. U The rotational subgroup O has half as many operations (h = 4) but still preserves the essential symmetry. L Carry out the work in O and correlate the results to O h. U In O, Γ σ = A 1 + E + T 1, which has obvious correlations to Γ σ = A 1g + E g + T 1u in O h.

8 Labeling the Symmetry Elements c d b σ 1 a σ 4 σ 6 σ 5 a σ 3 b σ d U 8 in O refers to 4 and 4 whose axes run along the cube diagonals. L Label these by the corners through which they pass: e.g., aa, bb, cc, dd. U 3C, 3C 4, and 3C 4 3 have axes that run through trans-related pairs of ligands. L Label these by the pairs of ligands through which they pass; e.g., 1, 34, 56. U 6C ' have axes that pass through the mid-points of opposite cube edges. L Label these by the two-letter designation of the cube edges through which they pass; e.g., ac, bd, ab, cd, ad, bc. U rotations are clockwise, viewed from the upper cube corner through which the axis passes. U C 4 rotations are clockwise, viewed from the lower-numbered ligand. c

9 The Effect of the Operations of O on a Reference Function σ 1 O E C C C label aa bb cc dd aa bb cc dd R j σ 1 σ 1 σ 5 σ 3 σ 6 σ 4 σ 3 σ 6 σ 4 σ 5 σ 1 σ σ C 4 C 4 C 4 C 4 3 C 4 3 C 4 3 C ' C ' C ' C ' C ' C ' ac bd ab cd ad bc σ 1 σ 5 σ 4 σ 1 σ 6 σ 3 σ σ σ 3 σ 4 σ 5 σ 6 c d b σ 1 a σ 4 σ 6 σ 5 a σ 3 b σ d c

10 Projection Operator for the A 1 Species in O (A 1g in O h ) O E C C C label aa bb cc dd aa bb cc dd R j σ 1 σ 1 σ 5 σ 3 σ 6 σ 4 σ 3 σ 6 σ 4 σ 5 σ 1 σ σ A χ ir R j σ 1 σ 1 σ 5 σ 3 σ 6 σ 4 σ 3 σ 6 σ 4 σ 5 σ 1 σ σ Summing all the χ ir R j σ 1 terms gives C 4 C 4 C 4 3 C 4 3 C 4 3 C 4 C ' C ' C ' C ' C ' C ' ac bd ab cd ad bc σ 1 σ 5 σ 4 σ 1 σ 6 σ 3 σ σ σ 3 σ 4 σ 5 σ σ 1 σ 5 σ 4 σ 1 σ 6 σ 3 σ σ σ 3 σ 4 σ 5 σ 6 P(A 1 )σ 1 % 4σ 1 + 4σ + 4σ 3 + 4σ 4 + 4σ 5 + 4σ 6 % σ 1 + σ + σ 3 + σ 4 + σ 5 + σ 6

11 SALC for A 1g Normalizing: N I(σ 1 + σ + σ 3 + σ 4 + σ 5 + σ 6 ) dτ = N I(σ 1 + σ + σ 3 + σ 4 + σ 5 + σ 6 )dτ = N ( ) = 6N / 1 Y N = 1//6 Therefore, the normalized A 1g SALC is Σ 1 (A 1g ) = 1//6(σ 1 + σ + σ 3 + σ 4 + σ 5 + σ 6 )

12 Projection Operator for the First of Two E SALCs (E u in O h ) U In O, χ R = 0 for 6C 4 and 6C '. Therefore, ignore the last 1 terms. O E C C C label aa bb cc dd aa bb cc dd R j σ 1 σ 1 σ 5 σ 3 σ 6 σ 4 σ 3 σ 6 σ 4 σ 5 σ 1 σ σ E χ ir R j σ 1 σ 1 -σ 5 -σ 3 -σ 6 -σ 4 -σ 3 -σ 6 -σ 4 -σ 5 σ 1 σ σ Summing across all χ ir R j σ 1 gives P(E)σ 1 % 4σ 1 + 4σ σ 3 σ 4 σ 5 σ 6 % σ 1 + σ σ 3 σ 4 σ 5 σ 6 Normalizing: N I(σ 1 + σ σ 3 σ 4 σ 5 σ 6 ) dτ After normalization: = N I(4σ 1 + 4σ + σ 3 + σ 4 + σ 5 + σ 6 )dτ = N ( ) = 1N / 1 Y N = 1//1 = 1/(/3) Σ (E) = 1/(/3)(σ 1 + σ σ 3 σ 4 σ 5 σ 6 )

13 Test of Orthogonality with Σ 1 (A 1 ) I[Σ 1 (A)][Σ (E)]dτ = I(σ 1 + σ + σ 3 + σ 4 + σ 5 + σ 6 )(σ 1 + σ σ 3 σ 4 σ 5 σ 6 )dτ = = 0 L But Σ (E) is only the first of two degenerate functions. ; How do we find the partner?

14 Finding the Partner to Σ (E) - Method I Method I: Apply the E Projection operator to another reference function, here σ 3. O E C C C label aa bb cc dd aa bb cc dd R j σ 3 σ 3 σ 1 σ 6 σ σ 6 σ 5 σ 1 σ 5 σ σ 4 σ 3 σ 4 E χ ir R j σ 3 σ 3 -σ 1 -σ 6 -σ -σ 6 -σ 5 -σ 1 -σ 5 -σ σ 4 σ 3 σ 4 This gives P(E)σ 3 % σ 1 σ + 4σ 3 + 4σ 4 σ 5 σ 6 % σ 1 σ + σ 3 + σ 4 σ 5 σ 6 Orthogonal to Σ 1 (A)? I(σ 1 + σ + σ 3 + σ 4 + σ 5 + σ 6 )( σ 1 σ + σ 3 + σ 4 σ 5 σ 6 )dτ = = 0 YYes Orthogonal to Σ (E)? I(σ 1 + σ σ 3 σ 4 σ 5 σ 6 )( σ 1 σ + σ 3 + σ 4 σ 5 σ 6 )dτ = = 6 0 YNo! ; What went wrong?

15 Notes on Method I for Finding a Degenerate Partner L Changing the reference function after finding the first member of a degenerate set implicitly changes the axis orientation. The resulting function will be one of the following: A legitimate partner wave function in either its positive or negative form. [Not this time!] The negative of the first member of the degenerate set. [Not a useful result, and not what happened this time.] A linear combination of the first member with its partner(s). If this is the case, try various combinations of the two projected functions to find a function that is orthogonal. [Looks like this must be what we got!]

16 Finding the Partner to Σ (E) - Method I By trial and error with various combinations we find that this works: P(E)σ 1 = σ 1 + σ σ 3 σ 4 σ 5 σ 6 P(E)σ 3 = σ 1 σ + 4σ 3 + 4σ 4 σ 5 σ 6 3σ 3 + 3σ 4 3σ 5 3σ 6 % σ 3 + σ 4 σ 5 σ 6 This result is orthogonal to Σ (E), I(σ 1 + σ σ 3 σ 4 σ 5 σ 6 )(σ 3 + σ 4 σ 5 σ 6 )dτ = = 0 and also to Σ 1 (A 1 ), I(σ 1 + σ + σ 3 + σ 4 + σ 5 + σ 6 )(σ 3 + σ 4 σ 5 σ 6 )dτ = = 0 The normalized partner wave function, then, is Σ 3 (E) = ½(σ 3 + σ 4 σ 5 σ 6 )

17 Finding the Partner to Σ (E) - Method II Method II: Apply an operation of the group to the first function found. Principle: The effect of any group operation on a wave function of a degenerate set is to transform the function into the positive or negative of itself, a partner, or a linear combination of itself and its partner or partners. L Suppose we perform (aa) on our first degenerate function. U Effect of (aa) on the functions of the basis set: U Effect of (aa) on Σ (E): Before σ 1 σ σ 3 σ 4 σ 5 σ 6 After σ 5 σ 6 σ 1 σ σ 3 σ 4 (σ 1 + σ σ 3 σ 4 σ 5 σ 6 ) 6 (σ 5 + σ 6 σ 1 σ σ 3 σ 4 ) Is this new function orthogonal to Σ (E)? = ( σ 1 σ σ 3 σ 4 + σ 5 + σ 6 ) I(σ 1 + σ σ 3 σ 4 σ 5 σ 6 )( σ 1 σ σ 3 σ 4 + σ 5 + σ 6 )dτ = 1 1 = 10 0 YNo!

18 Finding the Partner to Σ (E) - Method II The new function must be a combination of Σ (E) and the partner function. By trial-and-error: Σ : σ 1 σ + σ 3 + σ 4 + σ 5 + σ 6 {Σ R[ (aa)]} +σ 1 + σ + σ 3 + σ 4 4σ 5 4σ 6 +3σ 3 + 3σ 4 3σ 5 3σ 6 This is the same result as found by Method I: % σ 3 + σ 4 σ 5 σ 6 Σ 3 (E) = ½(σ 3 + σ 4 σ 5 σ 6 )

19 Projection Operator for the First of Three T 1u SALCs Note: In T 1 of the group O, for 8 χ = 0; therefore, skip the first eight terms in the operator expression. O E C C C label aa bb cc dd aa bb cc dd R j σ 1 σ 1 σ 5 σ 3 σ 6 σ 4 σ 3 σ 6 σ 4 σ 5 σ 1 σ σ T χ ir R j σ 1 3σ 1 -σ 1 -σ -σ C 4 C 4 C 4 3 C 4 3 C 4 3 C 4 C ' C ' C ' C ' C ' C ' ac bd ab cd ad bc σ 1 σ 5 σ 4 σ 1 σ 6 σ 3 σ σ σ 3 σ 4 σ 5 σ σ 1 σ 5 σ 4 σ 1 σ 6 σ 3 -σ -σ -σ 3 -σ 4 -σ 5 -σ 6 Summing across the χ ir R j σ 1 : P(T 1 )σ 1 % 4σ 1 4σ % σ 1 σ We can readily show that this is orthogonal to the previous three functions for A and E, and on normalization we obtain the SALC G 4 (T 1 ) = 1// (σ 1 σ )

20 Finding the Partner T 1u SALCs The partner functions become apparent from considering the form of G 4 (T 1 ). c d b σ 1 a a b σ d c L The other two functions must have the same form along the other two orthogonal axes: G 5 (T 1 ) = 1// (σ 3 σ 4 ) G 6 (T 1 ) = 1// (σ 5 σ 6 ) U Alternately, note the effects of (aa) and (aa) on (σ 1 σ ): R[ (aa)] (σ 1 σ ) = (σ 5 σ 6 ) Y G 6 (T 1 ) R[ (aa)] (σ 1 σ ) = (σ 3 σ 4 ) Y G 5 (T 1 )

21 Summary: The Six σ-salcs of MX 6 (O h ) Σ 1 (A 1g ) = 1//6(σ 1 + σ + σ 3 + σ 4 + σ 5 + σ 6 ) Σ (E u ) = 1/(/3)(σ 1 + σ σ 3 σ 4 σ 5 σ 6 ) Σ 3 (E u ) = ½(σ 3 + σ 4 σ 5 σ 6 ) G 4 (T 1g ) = 1// (σ 1 σ ) G 5 (T 1g ) = 1// (σ 3 σ 4 ) G 6 (T 1g ) = 1// (σ 5 σ 6 )

22 The σ-salcs of CH 4 (T d ) B z A y x D C Γ = A 1 + T U The A 1 SALC is obvious, so only use the projection operator for the T SALCs. U The group T d has h = 4, but its rotational subgroup T has h = 1. Therefore, do the work in T, where the T species corresponds to T of T d. U For the irreducible representation T, the only non-zero characters are E, C (x), C (y), C (z), so the T operator has only four non-vanishing terms: T E... C (x) C (y) C (z) R j s A s A... s C s D s B T χ ir R j s A 3s A... s C s D s B

23 The σ-salcs of CH 4 (T d ) P(T)s A % 3s A s C s D s B U This function is orthogonal to the A SALC, Φ 1 = ½(s A + s B + s C + s D ), and could be normalized to give the function Φ(T) = 1/(/3)(3s A - s C - s D - s B ) ; This SALC is supposed to match with one of the p AOs on carbon. Φ(T) makes no sense geometrically for such overlap! ( P(T)s A must be a combination of all three functions: P(T z )s A % s A + s B s C s D P(T y )s A % s A s B s C + s D P(T x )s A % s A s B + s C s D P(T)s A % 3s A s B s C s d U After normalization, the three T SALCs of MX 4 (T d ) are: Φ = ½{s A + s B s C s D } Φ 3 = ½{s A s B s C + s D } Φ 4 = ½{s A s B + s C s D }

ELEMENTS OF MATRIX MATHEMATICS

ELEMENTS OF MATRIX MATHEMATICS QRMC07 9/7/0 4:45 PM Page 5 CHAPTER SEVEN ELEMENTS OF MATRIX MATHEMATICS 7. AN INTRODUCTION TO MATRICES Investors frequently encounter situations involving numerous potential outcomes, many discrete periods

More information

Chemistry 481 Answer Set #1 Question #1 Identify the symmetry elements and operations present in each of the following molecules:

Chemistry 481 Answer Set #1 Question #1 Identify the symmetry elements and operations present in each of the following molecules: Chemistry 81 Answer Set #1 Question #1 Ientify the symmetry elements an operations present in each of the following molecules: Molecule Structure Point group Symmetry Elements Symmetry Operations (a) Chloroform

More information

SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT. BF360 Operations Research

SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT. BF360 Operations Research SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT BF360 Operations Research Unit 3 Moses Mwale e-mail: moses.mwale@ictar.ac.zm BF360 Operations Research Contents Unit 3: Sensitivity and Duality 3 3.1 Sensitivity

More information

Lecture 3: Factor models in modern portfolio choice

Lecture 3: Factor models in modern portfolio choice Lecture 3: Factor models in modern portfolio choice Prof. Massimo Guidolin Portfolio Management Spring 2016 Overview The inputs of portfolio problems Using the single index model Multi-index models Portfolio

More information

The reciprocal lattice. Daniele Toffoli December 2, / 24

The reciprocal lattice. Daniele Toffoli December 2, / 24 The reciprocal lattice Daniele Toffoli December 2, 2016 1 / 24 Outline 1 Definitions and properties 2 Important examples and applications 3 Miller indices of lattice planes Daniele Toffoli December 2,

More information

Jacob: What data do we use? Do we compile paid loss triangles for a line of business?

Jacob: What data do we use? Do we compile paid loss triangles for a line of business? PROJECT TEMPLATES FOR REGRESSION ANALYSIS APPLIED TO LOSS RESERVING BACKGROUND ON PAID LOSS TRIANGLES (The attached PDF file has better formatting.) {The paid loss triangle helps you! distinguish between

More information

MAT 4250: Lecture 1 Eric Chung

MAT 4250: Lecture 1 Eric Chung 1 MAT 4250: Lecture 1 Eric Chung 2Chapter 1: Impartial Combinatorial Games 3 Combinatorial games Combinatorial games are two-person games with perfect information and no chance moves, and with a win-or-lose

More information

CS134: Networks Spring Random Variables and Independence. 1.2 Probability Distribution Function (PDF) Number of heads Probability 2 0.

CS134: Networks Spring Random Variables and Independence. 1.2 Probability Distribution Function (PDF) Number of heads Probability 2 0. CS134: Networks Spring 2017 Prof. Yaron Singer Section 0 1 Probability 1.1 Random Variables and Independence A real-valued random variable is a variable that can take each of a set of possible values in

More information

In terms of covariance the Markowitz portfolio optimisation problem is:

In terms of covariance the Markowitz portfolio optimisation problem is: Markowitz portfolio optimisation Solver To use Solver to solve the quadratic program associated with tracing out the efficient frontier (unconstrained efficient frontier UEF) in Markowitz portfolio optimisation

More information

Factors of 10 = = 2 5 Possible pairs of factors:

Factors of 10 = = 2 5 Possible pairs of factors: Factoring Trinomials Worksheet #1 1. b 2 + 8b + 7 Signs inside the two binomials are identical and positive. Factors of b 2 = b b Factors of 7 = 1 7 b 2 + 8b + 7 = (b + 1)(b + 7) 2. n 2 11n + 10 Signs

More information

Bianchi (hyper-)cubes and a geometric unification of the Hirota and Miwa equations. W.K. Schief. The University of New South Wales, Sydney

Bianchi (hyper-)cubes and a geometric unification of the Hirota and Miwa equations. W.K. Schief. The University of New South Wales, Sydney Bianchi (hyper-)cubes and a geometric unification of the Hirota and Miwa equations by W.K. Schief The University of New South Wales, Sydney ARC Centre of Excellence for Mathematics and Statistics of Complex

More information

MATH 181-Quadratic Equations (7 )

MATH 181-Quadratic Equations (7 ) MATH 181-Quadratic Equations (7 ) 7.1 Solving a Quadratic Equation by Factoring I. Factoring Terms with Common Factors (Find the greatest common factor) a. 16 1x 4x = 4( 4 3x x ) 3 b. 14x y 35x y = 3 c.

More information

Game Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India October 2012

Game Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India October 2012 Game Theory Lecture Notes By Y. Narahari Department of Computer Science and Automation Indian Institute of Science Bangalore, India October 2012 COOPERATIVE GAME THEORY Coalitional Games: Introduction

More information

Developmental Math An Open Program Unit 12 Factoring First Edition

Developmental Math An Open Program Unit 12 Factoring First Edition Developmental Math An Open Program Unit 12 Factoring First Edition Lesson 1 Introduction to Factoring TOPICS 12.1.1 Greatest Common Factor 1 Find the greatest common factor (GCF) of monomials. 2 Factor

More information

Sandringham School Sixth Form. AS Maths. Bridging the gap

Sandringham School Sixth Form. AS Maths. Bridging the gap Sandringham School Sixth Form AS Maths Bridging the gap Section 1 - Factorising be able to factorise simple expressions be able to factorise quadratics The expression 4x + 8 can be written in factor form,

More information

arxiv: v1 [q-fin.pm] 12 Jul 2012

arxiv: v1 [q-fin.pm] 12 Jul 2012 The Long Neglected Critically Leveraged Portfolio M. Hossein Partovi epartment of Physics and Astronomy, California State University, Sacramento, California 95819-6041 (ated: October 8, 2018) We show that

More information

Markowitz portfolio theory. May 4, 2017

Markowitz portfolio theory. May 4, 2017 Markowitz portfolio theory Elona Wallengren Robin S. Sigurdson May 4, 2017 1 Introduction A portfolio is the set of assets that an investor chooses to invest in. Choosing the optimal portfolio is a complex

More information

9.1 Principal Component Analysis for Portfolios

9.1 Principal Component Analysis for Portfolios Chapter 9 Alpha Trading By the name of the strategies, an alpha trading strategy is to select and trade portfolios so the alpha is maximized. Two important mathematical objects are factor analysis and

More information

Topic 12 Factorisation

Topic 12 Factorisation Topic 12 Factorisation 1. How to find the greatest common factors of an algebraic expression. Definition: A factor of a number is an integer that divides the number exactly. So for example, the factors

More information

Lab 12: Population Viability Analysis- April 12, 2004 DUE: April at the beginning of lab

Lab 12: Population Viability Analysis- April 12, 2004 DUE: April at the beginning of lab Lab 12: Population Viability Analysis- April 12, 2004 DUE: April 19 2004 at the beginning of lab Procedures: A. Complete the workbook exercise (exercise 28). This is a brief exercise and provides needed

More information

Quasi-Monte Carlo for Finance

Quasi-Monte Carlo for Finance Quasi-Monte Carlo for Finance Peter Kritzer Johann Radon Institute for Computational and Applied Mathematics (RICAM) Austrian Academy of Sciences Linz, Austria NCTS, Taipei, November 2016 Peter Kritzer

More information

Unit M2.2 (All About) Stress

Unit M2.2 (All About) Stress Unit M. (All About) Stress Readings: CDL 4., 4.3, 4.4 16.001/00 -- Unified Engineering Department of Aeronautics and Astronautics Massachusetts Institute of Technology LEARNING OBJECTIVES FOR UNIT M. Through

More information

Mean-Variance Portfolio Choice in Excel

Mean-Variance Portfolio Choice in Excel Mean-Variance Portfolio Choice in Excel Prof. Manuela Pedio 20550 Quantitative Methods for Finance August 2018 Let s suppose you can only invest in two assets: a (US) stock index (here represented by the

More information

Financial Market Models. Lecture 1. One-period model of financial markets & hedging problems. Imperial College Business School

Financial Market Models. Lecture 1. One-period model of financial markets & hedging problems. Imperial College Business School Financial Market Models Lecture One-period model of financial markets & hedging problems One-period model of financial markets a 4 2a 3 3a 3 a 3 -a 4 2 Aims of section Introduce one-period model with finite

More information

Option Pricing. Chapter Discrete Time

Option Pricing. Chapter Discrete Time Chapter 7 Option Pricing 7.1 Discrete Time In the next section we will discuss the Black Scholes formula. To prepare for that, we will consider the much simpler problem of pricing options when there are

More information

Lecture 6: Chapter 6

Lecture 6: Chapter 6 Lecture 6: Chapter 6 C C Moxley UAB Mathematics 3 October 16 6.1 Continuous Probability Distributions Last week, we discussed the binomial probability distribution, which was discrete. 6.1 Continuous Probability

More information

Alg2A Factoring and Equations Review Packet

Alg2A Factoring and Equations Review Packet 1 Factoring using GCF: Take the greatest common factor (GCF) for the numerical coefficient. When choosing the GCF for the variables, if all the terms have a common variable, take the one with the lowest

More information

On Sensitivity Value of Pair-Matched Observational Studies

On Sensitivity Value of Pair-Matched Observational Studies On Sensitivity Value of Pair-Matched Observational Studies Qingyuan Zhao Department of Statistics, University of Pennsylvania August 2nd, JSM 2017 Manuscript and slides are available at http://www-stat.wharton.upenn.edu/~qyzhao/.

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

Game Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India August 2012

Game Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India August 2012 Game Theory Lecture Notes By Y. Narahari Department of Computer Science and Automation Indian Institute of Science Bangalore, India August 2012 Chapter 6: Mixed Strategies and Mixed Strategy Nash Equilibrium

More information

Name Class Date. Adding and Subtracting Polynomials

Name Class Date. Adding and Subtracting Polynomials 8-1 Reteaching Adding and Subtracting Polynomials You can add and subtract polynomials by lining up like terms and then adding or subtracting each part separately. What is the simplified form of (3x 4x

More information

Applications of Good s Generalized Diversity Index. A. J. Baczkowski Department of Statistics, University of Leeds Leeds LS2 9JT, UK

Applications of Good s Generalized Diversity Index. A. J. Baczkowski Department of Statistics, University of Leeds Leeds LS2 9JT, UK Applications of Good s Generalized Diversity Index A. J. Baczkowski Department of Statistics, University of Leeds Leeds LS2 9JT, UK Internal Report STAT 98/11 September 1998 Applications of Good s Generalized

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

Statistics for Business and Economics

Statistics for Business and Economics Statistics for Business and Economics Chapter 5 Continuous Random Variables and Probability Distributions Ch. 5-1 Probability Distributions Probability Distributions Ch. 4 Discrete Continuous Ch. 5 Probability

More information

5 Deduction in First-Order Logic

5 Deduction in First-Order Logic 5 Deduction in First-Order Logic The system FOL C. Let C be a set of constant symbols. FOL C is a system of deduction for the language L # C. Axioms: The following are axioms of FOL C. (1) All tautologies.

More information

Multiply the binomials. Add the middle terms. 2x 2 7x 6. Rewrite the middle term as 2x 2 a sum or difference of terms. 12x 321x 22

Multiply the binomials. Add the middle terms. 2x 2 7x 6. Rewrite the middle term as 2x 2 a sum or difference of terms. 12x 321x 22 Section 5.5 Factoring Trinomials 349 Factoring Trinomials 1. Factoring Trinomials: AC-Method In Section 5.4, we learned how to factor out the greatest common factor from a polynomial and how to factor

More information

An Intertemporal Capital Asset Pricing Model

An Intertemporal Capital Asset Pricing Model I. Assumptions Finance 400 A. Penati - G. Pennacchi Notes on An Intertemporal Capital Asset Pricing Model These notes are based on the article Robert C. Merton (1973) An Intertemporal Capital Asset Pricing

More information

MIDTERM ANSWER KEY GAME THEORY, ECON 395

MIDTERM ANSWER KEY GAME THEORY, ECON 395 MIDTERM ANSWER KEY GAME THEORY, ECON 95 SPRING, 006 PROFESSOR A. JOSEPH GUSE () There are positions available with wages w and w. Greta and Mary each simultaneously apply to one of them. If they apply

More information

DATA SUMMARIZATION AND VISUALIZATION

DATA SUMMARIZATION AND VISUALIZATION APPENDIX DATA SUMMARIZATION AND VISUALIZATION PART 1 SUMMARIZATION 1: BUILDING BLOCKS OF DATA ANALYSIS 294 PART 2 PART 3 PART 4 VISUALIZATION: GRAPHS AND TABLES FOR SUMMARIZING AND ORGANIZING DATA 296

More information

Gamma Distribution Fitting

Gamma Distribution Fitting Chapter 552 Gamma Distribution Fitting Introduction This module fits the gamma probability distributions to a complete or censored set of individual or grouped data values. It outputs various statistics

More information

Chapter 5 Finite Difference Methods. Math6911 W07, HM Zhu

Chapter 5 Finite Difference Methods. Math6911 W07, HM Zhu Chapter 5 Finite Difference Methods Math69 W07, HM Zhu References. Chapters 5 and 9, Brandimarte. Section 7.8, Hull 3. Chapter 7, Numerical analysis, Burden and Faires Outline Finite difference (FD) approximation

More information

A Hybrid Commodity and Interest Rate Market Model

A Hybrid Commodity and Interest Rate Market Model A Hybrid Commodity and Interest Rate Market Model University of Technology, Sydney June 1 Literature A Hybrid Market Model Recall: The basic LIBOR Market Model The cross currency LIBOR Market Model LIBOR

More information

Chapter 8. Markowitz Portfolio Theory. 8.1 Expected Returns and Covariance

Chapter 8. Markowitz Portfolio Theory. 8.1 Expected Returns and Covariance Chapter 8 Markowitz Portfolio Theory 8.1 Expected Returns and Covariance The main question in portfolio theory is the following: Given an initial capital V (0), and opportunities (buy or sell) in N securities

More information

Greatest Common Factor and Factoring by Grouping

Greatest Common Factor and Factoring by Grouping mil84488_ch06_409-419.qxd 2/8/12 3:11 PM Page 410 410 Chapter 6 Factoring Polynomials Section 6.1 Concepts 1. Identifying the Greatest Common Factor 2. Factoring out the Greatest Common Factor 3. Factoring

More information

The Binomial Distribution

The Binomial Distribution The Binomial Distribution Properties of a Binomial Experiment 1. It consists of a fixed number of observations called trials. 2. Each trial can result in one of only two mutually exclusive outcomes labeled

More information

MTP_FOUNDATION_Syllabus 2012_Dec2016 SET - I. Paper 4-Fundamentals of Business Mathematics and Statistics

MTP_FOUNDATION_Syllabus 2012_Dec2016 SET - I. Paper 4-Fundamentals of Business Mathematics and Statistics SET - I Paper 4-Fundamentals of Business Mathematics and Statistics Full Marks: 00 Time allowed: 3 Hours Section A (Fundamentals of Business Mathematics) I. Answer any two questions. Each question carries

More information

Individual and Moving Range Charts. Measurement (observation) for the jth unit (sample) of subgroup i

Individual and Moving Range Charts. Measurement (observation) for the jth unit (sample) of subgroup i Appendix 3: SPCHART Notation SPSS creates ne types of Shewhart control charts. In this appendix, the charts are grouped into five sections: X-Bar and R Charts X-Bar and s Charts Individual and Moving Range

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

A Spreadsheet-Literate Non-Statistician s Guide to the Beta-Geometric Model

A Spreadsheet-Literate Non-Statistician s Guide to the Beta-Geometric Model A Spreadsheet-Literate Non-Statistician s Guide to the Beta-Geometric Model Peter S Fader wwwpetefadercom Bruce G S Hardie wwwbrucehardiecom December 2014 1 Introduction The beta-geometric (BG) distribution

More information

LECTURE 2: MULTIPERIOD MODELS AND TREES

LECTURE 2: MULTIPERIOD MODELS AND TREES LECTURE 2: MULTIPERIOD MODELS AND TREES 1. Introduction One-period models, which were the subject of Lecture 1, are of limited usefulness in the pricing and hedging of derivative securities. In real-world

More information

The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2011, Mr. Ruey S. Tsay. Solutions to Final Exam.

The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2011, Mr. Ruey S. Tsay. Solutions to Final Exam. The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2011, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (32 pts) Answer briefly the following questions. 1. Suppose

More information

Discussion Paper No. DP 07/05

Discussion Paper No. DP 07/05 SCHOOL OF ACCOUNTING, FINANCE AND MANAGEMENT Essex Finance Centre A Stochastic Variance Factor Model for Large Datasets and an Application to S&P data A. Cipollini University of Essex G. Kapetanios Queen

More information

IEOR 3106: Introduction to Operations Research: Stochastic Models SOLUTIONS to Final Exam, Sunday, December 16, 2012

IEOR 3106: Introduction to Operations Research: Stochastic Models SOLUTIONS to Final Exam, Sunday, December 16, 2012 IEOR 306: Introduction to Operations Research: Stochastic Models SOLUTIONS to Final Exam, Sunday, December 6, 202 Four problems, each with multiple parts. Maximum score 00 (+3 bonus) = 3. You need to show

More information

A relation on 132-avoiding permutation patterns

A relation on 132-avoiding permutation patterns Discrete Mathematics and Theoretical Computer Science DMTCS vol. VOL, 205, 285 302 A relation on 32-avoiding permutation patterns Natalie Aisbett School of Mathematics and Statistics, University of Sydney,

More information

Advanced Operations Research Prof. G. Srinivasan Dept of Management Studies Indian Institute of Technology, Madras

Advanced Operations Research Prof. G. Srinivasan Dept of Management Studies Indian Institute of Technology, Madras Advanced Operations Research Prof. G. Srinivasan Dept of Management Studies Indian Institute of Technology, Madras Lecture 23 Minimum Cost Flow Problem In this lecture, we will discuss the minimum cost

More information

GeoShear Demonstration #3 Strain and Matrix Algebra. 1. Linking Pure Shear to the Transformation Matrix- - - Diagonal Matrices.

GeoShear Demonstration #3 Strain and Matrix Algebra. 1. Linking Pure Shear to the Transformation Matrix- - - Diagonal Matrices. GeoShear Demonstration #3 Strain and Matrix Algebra Paul Karabinos Department of Geosciences, Williams College, Williamstown, MA 01267 This demonstration is suitable for an undergraduate structural geology

More information

F/6 6/19 A MONTE CARLO STUDY DESMAT ICS INC STATE

F/6 6/19 A MONTE CARLO STUDY DESMAT ICS INC STATE AD-AXI 463 UNCLASSIFIED F/6 6/19 A MONTE CARLO STUDY DESMAT ICS INC STATE OF COLLEGE THE USE PA OF AUXILIARY INFORMATION IN THE --ETC(UI SEP AX_ D E SMITH, J J PETERSON N00014-79-C-012R TR-112- - TATISTICS-

More information

Chapter 5. Continuous Random Variables and Probability Distributions. 5.1 Continuous Random Variables

Chapter 5. Continuous Random Variables and Probability Distributions. 5.1 Continuous Random Variables Chapter 5 Continuous Random Variables and Probability Distributions 5.1 Continuous Random Variables 1 2CHAPTER 5. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS Probability Distributions Probability

More information

ACCUPLACER Elementary Algebra Assessment Preparation Guide

ACCUPLACER Elementary Algebra Assessment Preparation Guide ACCUPLACER Elementary Algebra Assessment Preparation Guide Please note that the guide is for reference only and that it does not represent an exact match with the assessment content. The Assessment Centre

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

Binomial Coefficient

Binomial Coefficient Binomial Coefficient This short text is a set of notes about the binomial coefficients, which link together algebra, combinatorics, sets, binary numbers and probability. The Product Rule Suppose you are

More information

FDPE Microeconomics 3 Spring 2017 Pauli Murto TA: Tsz-Ning Wong (These solution hints are based on Julia Salmi s solution hints for Spring 2015.

FDPE Microeconomics 3 Spring 2017 Pauli Murto TA: Tsz-Ning Wong (These solution hints are based on Julia Salmi s solution hints for Spring 2015. FDPE Microeconomics 3 Spring 2017 Pauli Murto TA: Tsz-Ning Wong (These solution hints are based on Julia Salmi s solution hints for Spring 2015.) Hints for Problem Set 3 1. Consider the following strategic

More information

Lecture 10-12: CAPM.

Lecture 10-12: CAPM. Lecture 10-12: CAPM. I. Reading II. Market Portfolio. III. CAPM World: Assumptions. IV. Portfolio Choice in a CAPM World. V. Minimum Variance Mathematics. VI. Individual Assets in a CAPM World. VII. Intuition

More information

The rm can buy as many units of capital and labour as it wants at constant factor prices r and w. p = q. p = q

The rm can buy as many units of capital and labour as it wants at constant factor prices r and w. p = q. p = q 10 Homework Assignment 10 [1] Suppose a perfectly competitive, prot maximizing rm has only two inputs, capital and labour. The rm can buy as many units of capital and labour as it wants at constant factor

More information

MATH362 Fundamentals of Mathematical Finance. Topic 1 Mean variance portfolio theory. 1.1 Mean and variance of portfolio return

MATH362 Fundamentals of Mathematical Finance. Topic 1 Mean variance portfolio theory. 1.1 Mean and variance of portfolio return MATH362 Fundamentals of Mathematical Finance Topic 1 Mean variance portfolio theory 1.1 Mean and variance of portfolio return 1.2 Markowitz mean-variance formulation 1.3 Two-fund Theorem 1.4 Inclusion

More information

Robust Optimization Applied to a Currency Portfolio

Robust Optimization Applied to a Currency Portfolio Robust Optimization Applied to a Currency Portfolio R. Fonseca, S. Zymler, W. Wiesemann, B. Rustem Workshop on Numerical Methods and Optimization in Finance June, 2009 OUTLINE Introduction Motivation &

More information

Quadratic Algebra Lesson #2

Quadratic Algebra Lesson #2 Quadratic Algebra Lesson # Factorisation Of Quadratic Expressions Many of the previous expansions have resulted in expressions of the form ax + bx + c. Examples: x + 5x+6 4x 9 9x + 6x + 1 These are known

More information

Outline for today. Stat155 Game Theory Lecture 19: Price of anarchy. Cooperative games. Price of anarchy. Price of anarchy

Outline for today. Stat155 Game Theory Lecture 19: Price of anarchy. Cooperative games. Price of anarchy. Price of anarchy Outline for today Stat155 Game Theory Lecture 19:.. Peter Bartlett Recall: Linear and affine latencies Classes of latencies Pigou networks Transferable versus nontransferable utility November 1, 2016 1

More information

Internet Appendix for Asymmetry in Stock Comovements: An Entropy Approach

Internet Appendix for Asymmetry in Stock Comovements: An Entropy Approach Internet Appendix for Asymmetry in Stock Comovements: An Entropy Approach Lei Jiang Tsinghua University Ke Wu Renmin University of China Guofu Zhou Washington University in St. Louis August 2017 Jiang,

More information

Topic 6 - Continuous Distributions I. Discrete RVs. Probability Density. Continuous RVs. Background Reading. Recall the discrete distributions

Topic 6 - Continuous Distributions I. Discrete RVs. Probability Density. Continuous RVs. Background Reading. Recall the discrete distributions Topic 6 - Continuous Distributions I Discrete RVs Recall the discrete distributions STAT 511 Professor Bruce Craig Binomial - X= number of successes (x =, 1,...,n) Geometric - X= number of trials (x =,...)

More information

BARUCH COLLEGE MATH 2003 SPRING 2006 MANUAL FOR THE UNIFORM FINAL EXAMINATION

BARUCH COLLEGE MATH 2003 SPRING 2006 MANUAL FOR THE UNIFORM FINAL EXAMINATION BARUCH COLLEGE MATH 003 SPRING 006 MANUAL FOR THE UNIFORM FINAL EXAMINATION The final examination for Math 003 will consist of two parts. Part I: Part II: This part will consist of 5 questions similar

More information

Factoring Quadratic Expressions VOCABULARY

Factoring Quadratic Expressions VOCABULARY 5-5 Factoring Quadratic Expressions TEKS FOCUS Foundational to TEKS (4)(F) Solve quadratic and square root equations. TEKS (1)(C) Select tools, including real objects, manipulatives, paper and pencil,

More information

Quasi-Monte Carlo Methods in Financial Engineering: An Equivalence Principle and Dimension Reduction

Quasi-Monte Carlo Methods in Financial Engineering: An Equivalence Principle and Dimension Reduction Quasi-Monte Carlo Methods in Financial Engineering: An Equivalence Principle and Dimension Reduction Xiaoqun Wang,2, and Ian H. Sloan 2,3 Department of Mathematical Sciences, Tsinghua University, Beijing

More information

Alg2A Factoring and Equations Review Packet

Alg2A Factoring and Equations Review Packet 1 Multiplying binomials: We have a special way of remembering how to multiply binomials called FOIL: F: first x x = x 2 (x + 7)(x + 5) O: outer x 5 = 5x I: inner 7 x = 7x x 2 + 5x +7x + 35 (then simplify)

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

Math 101, Basic Algebra Author: Debra Griffin

Math 101, Basic Algebra Author: Debra Griffin Math 101, Basic Algebra Author: Debra Griffin Name Chapter 5 Factoring 5.1 Greatest Common Factor 2 GCF, factoring GCF, factoring common binomial factor 5.2 Factor by Grouping 5 5.3 Factoring Trinomials

More information

3.1 Factors and Multiples of Whole Numbers

3.1 Factors and Multiples of Whole Numbers 3.1 Factors and Multiples of Whole Numbers LESSON FOCUS: Determine prime factors, greatest common factors, and least common multiples of whole numbers. The prime factorization of a natural number is the

More information

arxiv:physics/ v2 [math-ph] 13 Jan 1997

arxiv:physics/ v2 [math-ph] 13 Jan 1997 THE COMPLETE COHOMOLOGY OF E 8 LIE ALGEBRA arxiv:physics/9701004v2 [math-ph] 13 Jan 1997 H. R. Karadayi and M. Gungormez Dept.Physics, Fac. Science, Tech.Univ.Istanbul 80626, Maslak, Istanbul, Turkey Internet:

More information

A VALUATION MODEL FOR INDETERMINATE CONVERTIBLES by Jayanth Rama Varma

A VALUATION MODEL FOR INDETERMINATE CONVERTIBLES by Jayanth Rama Varma A VALUATION MODEL FOR INDETERMINATE CONVERTIBLES by Jayanth Rama Varma Abstract Many issues of convertible debentures in India in recent years provide for a mandatory conversion of the debentures into

More information

On Equivalent Resistance of Electrical Circuits. Mikhail Kagan 1 The Pennsylvania State University, Abington College

On Equivalent Resistance of Electrical Circuits. Mikhail Kagan 1 The Pennsylvania State University, Abington College On Equivalent Resistance of Electrical Circuits Mikhail Kagan 1 The Pennsylvania State University, Abington College Abstract One of the basic tasks related to electrical circuits is computing equivalent

More information

TERMINOLOGY 4.1. READING ASSIGNMENT 4.2 Sections 5.4, 6.1 through 6.5. Binomial. Factor (verb) GCF. Monomial. Polynomial.

TERMINOLOGY 4.1. READING ASSIGNMENT 4.2 Sections 5.4, 6.1 through 6.5. Binomial. Factor (verb) GCF. Monomial. Polynomial. Section 4. Factoring Polynomials TERMINOLOGY 4.1 Prerequisite Terms: Binomial Factor (verb) GCF Monomial Polynomial Trinomial READING ASSIGNMENT 4. Sections 5.4, 6.1 through 6.5 160 READING AND SELF-DISCOVERY

More information

BOUNDS FOR THE LEAST SQUARES RESIDUAL USING SCALED TOTAL LEAST SQUARES

BOUNDS FOR THE LEAST SQUARES RESIDUAL USING SCALED TOTAL LEAST SQUARES BOUNDS FOR THE LEAST SQUARES RESIDUAL USING SCALED TOTAL LEAST SQUARES Christopher C. Paige School of Computer Science, McGill University Montreal, Quebec, Canada, H3A 2A7 paige@cs.mcgill.ca Zdeněk Strakoš

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

The University of Sydney School of Mathematics and Statistics. Computer Project

The University of Sydney School of Mathematics and Statistics. Computer Project The University of Sydney School of Mathematics and Statistics Computer Project MATH2070/2970: Optimisation and Financial Mathematics Semester 2, 2018 Web Page: http://www.maths.usyd.edu.au/u/im/math2070/

More information

Chapter 13 Exercise 13.1

Chapter 13 Exercise 13.1 Chapter 1 Exercise 1.1 Q. 1. Q.. Q.. Q. 4. Q.. x + 1 + x 1 (x + 1) + 4x + (x 1) + 9x 4x + + 9x 1x 1 p p + (p ) p 1 (p + ) + p 4 p 1 p 4 p 19 y 4 4 y (y 4) 4(y ) 1 y 1 8y + 1 y + 8 1 y 1 + y 1 + 1 1 1y

More information

Carnegie Mellon University Graduate School of Industrial Administration

Carnegie Mellon University Graduate School of Industrial Administration Carnegie Mellon University Graduate School of Industrial Administration Chris Telmer Winter 2005 Final Examination Seminar in Finance 1 (47 720) Due: Thursday 3/3 at 5pm if you don t go to the skating

More information

dt+ ρσ 2 1 ρ2 σ 2 κ i and that A is a rather lengthy expression that we may or may not need. (Brigo & Mercurio Lemma Thm , p. 135.

dt+ ρσ 2 1 ρ2 σ 2 κ i and that A is a rather lengthy expression that we may or may not need. (Brigo & Mercurio Lemma Thm , p. 135. A 2D Gaussian model (akin to Brigo & Mercurio Section 4.2) Suppose where ( κ1 0 dx(t) = 0 κ 2 r(t) = δ 0 +X 1 (t)+x 2 (t) )( X1 (t) X 2 (t) ) ( σ1 0 dt+ ρσ 2 1 ρ2 σ 2 )( dw Q 1 (t) dw Q 2 (t) ) In this

More information

US Code (Unofficial compilation from the Legal Information Institute)

US Code (Unofficial compilation from the Legal Information Institute) US Code (Unofficial compilation from the Legal Information Institute) TITLE 26 - INTERNAL REVENUE CODE Subtitle A - Income Taxes CHAPTER 3 WITHHOLDING OF TAX ON NONRESIDENT ALIENS AND FOREIGN CORPORATIONS

More information

Lecture 3: Return vs Risk: Mean-Variance Analysis

Lecture 3: Return vs Risk: Mean-Variance Analysis Lecture 3: Return vs Risk: Mean-Variance Analysis 3.1 Basics We will discuss an important trade-off between return (or reward) as measured by expected return or mean of the return and risk as measured

More information

The Markowitz framework

The Markowitz framework IGIDR, Bombay 4 May, 2011 Goals What is a portfolio? Asset classes that define an Indian portfolio, and their markets. Inputs to portfolio optimisation: measuring returns and risk of a portfolio Optimisation

More information

Indian Association of Alternative Investment Funds (IAAIF) Swapnil Pawar Scient Capital

Indian Association of Alternative Investment Funds (IAAIF) Swapnil Pawar Scient Capital Indian Association of Alternative Investment Funds (IAAIF) Swapnil Pawar Scient Capital Contents Quick introduction to hedge funds and the idea of market inefficiencies Types of hedge funds Background

More information

Budget Setting Strategies for the Company s Divisions

Budget Setting Strategies for the Company s Divisions Budget Setting Strategies for the Company s Divisions Menachem Berg Ruud Brekelmans Anja De Waegenaere November 14, 1997 Abstract The paper deals with the issue of budget setting to the divisions of a

More information

MATH4512 Fundamentals of Mathematical Finance. Topic Two Mean variance portfolio theory. 2.1 Mean and variance of portfolio return

MATH4512 Fundamentals of Mathematical Finance. Topic Two Mean variance portfolio theory. 2.1 Mean and variance of portfolio return MATH4512 Fundamentals of Mathematical Finance Topic Two Mean variance portfolio theory 2.1 Mean and variance of portfolio return 2.2 Markowitz mean-variance formulation 2.3 Two-fund Theorem 2.4 Inclusion

More information

CMSC 474, Introduction to Game Theory 20. Shapley Values

CMSC 474, Introduction to Game Theory 20. Shapley Values CMSC 474, Introduction to Game Theory 20. Shapley Values Mohammad T. Hajiaghayi University of Maryland Shapley Values Recall that a pre-imputation is a payoff division that is both feasible and efficient

More information

CS 798: Homework Assignment 4 (Game Theory)

CS 798: Homework Assignment 4 (Game Theory) 0 5 CS 798: Homework Assignment 4 (Game Theory) 1.0 Preferences Assigned: October 28, 2009 Suppose that you equally like a banana and a lottery that gives you an apple 30% of the time and a carrot 70%

More information

dt + ρσ 2 1 ρ2 σ 2 B i (τ) = 1 e κ iτ κ i

dt + ρσ 2 1 ρ2 σ 2 B i (τ) = 1 e κ iτ κ i A 2D Gaussian model (akin to Brigo & Mercurio Section 4.2) Suppose where dx(t) = ( κ1 0 0 κ 2 ) ( X1 (t) X 2 (t) In this case we find (BLACKBOARD) that r(t) = δ 0 + X 1 (t) + X 2 (t) ) ( σ1 0 dt + ρσ 2

More information

Order book resilience, price manipulations, and the positive portfolio problem

Order book resilience, price manipulations, and the positive portfolio problem Order book resilience, price manipulations, and the positive portfolio problem Alexander Schied Mannheim University PRisMa Workshop Vienna, September 28, 2009 Joint work with Aurélien Alfonsi and Alla

More information

b) According to the statistics above the graph, the slope is What are the units and meaning of this value?

b) According to the statistics above the graph, the slope is What are the units and meaning of this value? ! Name: Date: Hr: LINEAR MODELS Writing Motion Equations 1) Answer the following questions using the position vs. time graph of a runner in a race shown below. Be sure to show all work (formula, substitution,

More information

Web Extension: Continuous Distributions and Estimating Beta with a Calculator

Web Extension: Continuous Distributions and Estimating Beta with a Calculator 19878_02W_p001-008.qxd 3/10/06 9:51 AM Page 1 C H A P T E R 2 Web Extension: Continuous Distributions and Estimating Beta with a Calculator This extension explains continuous probability distributions

More information

Geometric tools for the valuation of performance-dependent options

Geometric tools for the valuation of performance-dependent options Computational Finance and its Applications II 161 Geometric tools for the valuation of performance-dependent options T. Gerstner & M. Holtz Institut für Numerische Simulation, Universität Bonn, Germany

More information