Formulating SALCs with Projection Operators
|
|
- Melanie Wade
- 6 years ago
- Views:
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
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 informationChemistry 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 informationSCHOOL 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 informationLecture 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 informationThe 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 informationJacob: 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 informationMAT 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 informationCS134: 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 informationIn 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 informationFactors 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 informationBianchi (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 informationMATH 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 informationGame 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 informationDevelopmental 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 informationSandringham 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 informationarxiv: 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 informationMarkowitz 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 information9.1 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 informationTopic 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 informationLab 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 informationQuasi-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 informationUnit 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 informationMean-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 informationFinancial 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 informationOption 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 informationLecture 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 informationAlg2A 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 informationOn 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 information3.2 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 informationGame 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 informationName 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 informationApplications 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 information4: 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 informationStatistics 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 information5 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 informationMultiply 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 informationAn 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 informationMIDTERM 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 informationDATA 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 informationGamma 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 informationChapter 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 informationA 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 informationChapter 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 informationGreatest 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 informationThe 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 informationMTP_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 informationIndividual 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 informationCollinear 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 informationA 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 informationLECTURE 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 informationThe 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 informationDiscussion 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 informationIEOR 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 informationA 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 informationAdvanced 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 informationGeoShear 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 informationF/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 informationChapter 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 informationACCUPLACER 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 informationMartingales. 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 informationBinomial 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 informationFDPE 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 informationLecture 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 informationThe 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 informationMATH362 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 informationRobust 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 informationQuadratic 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 informationOutline 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 informationInternet 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 informationTopic 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 informationBARUCH 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 informationFactoring 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 informationQuasi-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 informationAlg2A 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 informationDecision 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 informationMath 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 information3.1 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 informationarxiv: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 informationA 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 informationOn 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 informationTERMINOLOGY 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 informationBOUNDS 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 informationMATH 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 informationThe 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 informationChapter 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 informationCarnegie 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 informationdt+ 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 informationUS 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 informationLecture 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 informationThe 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 informationIndian 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 informationBudget 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 informationMATH4512 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 informationCMSC 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 informationCS 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 informationdt + 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 informationOrder 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 informationb) 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 informationWeb 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 informationGeometric 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