Riemannian Geometry, Key to Homework #1

Size: px
Start display at page:

Download "Riemannian Geometry, Key to Homework #1"

Transcription

1 Riemannian Geometry Key to Homework # Let σu v sin u cos v sin u sin v cos u < u < π < v < π be a parametrization of the unit sphere S {x y z R 3 x + y + z } Fix an angle < θ < π and consider the parallel in short we just write u θ on the unit sphere αt sin θ cos t sin θ sin t cos θ < t < π i Sketch the curve α ii Calculate the normal curvature of α Solution: ii Method : By the calculation n σ u σ v σ u σ v ±sin u cos v sin u sin v cos u On the other hand α t sin θ sin t sin θ cos t arc-length parameterization is It is not unit-speed its Hence αs sin θ coss/ sin θ sin θ sins/ sin θ α s sin θ coss/ sin θ sins/ sin θ Write αs σus vs we find Hence the restriction of n to αs is us θ vs s sin θ ns ±sin θ coss/ sin θ sin θ sins/ sin θ cos θ By the formula the normal curvature of α is κ n s ns α s ± cos s/ sin θ + sin s/ sin θ ±

2 Method : We can also use Theorem 4 ie 45 to calculate the normal curvature where v α t where α must be a unit-speed curve To do so we need to calculate the second fundmental form e f g By a simple calculation we have e f g sin u On the other hand as we did above after re-parameterized by arc-length parameter we get αs sin θ coss/ sin θ sin θ sins/ sin θ Write αs σus vs we find Thus u s v s / sin θ Hence us θ vs s sin θ κ n s IIα s α s eu s +fu sv s+gv s sin θ sin θ Since the principal normal vector can have two directions we kave κ n s ± Show that the normal curvature of any curve on a sphere of radius r is ±/r Solution: Let M {x y z x + y + z r } Then its unit normal is just its normalized position vector ie n ± x + y + z x y z ± x y z r Let αt be a curve on the sphere M then the restriction of n to αt is nt ± r αt Therefore by the formula the normal curvature of α is κ n nt α t ± r αt α t Since α is on the sphere α α r By differentiate it on both sides we have α α Differentiating it on both sides again yields α α + α α Since α is unit-speed α α Hence α α Therefore κ n ± r αt α t ± r

3 3 Show that if a curve on a surface has zero normal and geodesic curvature everywhere then it is part of a straight line Solution: Since κ κ n + κ g The condition that a curve on a surface has zero normal and geodesic curvature everywhere implies that its curvature is identically zero So by the theorem it is part of a straight line 4 Find the Gauss curvature mean curvature principal curvatures and the corresponding principal directions of the following surfaces a σu v au + v bu v 4uv where a and b are constant b The cylinder: σu v a cos u a sin u v Solution: a σ u a b 4v σ v a b 4u σ uu σ vv σ uv 4 The unit normal is n σ u σ v σ u σ v The first fundamental form is bu + v av u ab 4b u + v + 4a u v + a b / E σ u σ u a +b +6v F σ u σ v a b +6uv G σ v σ v a +b +6u The second fundamental form is e n σ uu f n σ uv The Gauss curvature is the mean curvature is 4ab 4b u + v + 4a u v + a b / g n σ vv EG F 6a u v + 6b u + v + 4a b K eg f EG F 4a b 4b u + v + 4a u v + a b H eg ff + ge EG F aba b + 6uv 4b u + v + 4a u v + a b 3/ To calculate the principal curvatures note that E F F G EG F G F F E 3

4 So the matrix of the shape operator S P with respect to the basis {σ u σ v } is A F I F II EG F 3/ G F F E 8ab 8ab 8ab EG F 3/ F G E F Write µ 8ab then EG F 3/ deta λi µf λ µ EG Setting deta λi ie µf λ µ EG we get µf λ ±µ EG Hence the eigenvaluesprincipal curvatures are κ µf + EG κ µf EG where µ E F G are given as above To get the principal directions for κ µf + EG we solve A κ Iv ie µ EG µg µe µ EG ξ η We get we need normalize it to get a unit-vector!!! ξ η + E G E G G E+G E E+G Similarily ξ Hence the principal directions are where µ η G E+G E E+G G E G E e E + G σ u E + G σ v e E + G σ u + E + G σ v 8ab and E F G are given as above EG F 3/ ii By direct calculation the first fundamental form is E a F G 4

5 and the second fundamental form is e a f g Hence the matrix of the shape operator S P with respect to the basis {σ u σ v } is A F I F II /a Solving the equation deta λi we get the principal curvatures κ κ /a To get the principal directions for κ we solve A κ Iv ie /a ξ We get we need normalize it to get a unit-vector!!! ξ η so e σ v Similarily for κ /a we get ξ η so e σ u η The Gauss curvature is K κ κ and the mean curvature is H κ + κ a 5 Calculate the Christoffel symbols for the surface z fx y Solution: Let σu v u v fu v Then σ u f u σ v f v Hence E + f u F f u f v G + f v E u f u f uu E v f u f uv F u f uu f v +f u f uv F v f uv f v +f u f vv G u f v f uv G v f v f vv Hence Γ f u f uu + fu + fv Γ Γ f u f uv + fu + fv Γ Γ f u f vv + fu + fv f v f uu + fu + fv Γ Γ f v f uv + fu + fv 5

6 Γ f v f vv + fu + fv 6 Assume that the the surface σu v has its first fundamental form as E Prove its Gauss curvature K 4 + u + v F G 4 + u + v Solution: Use the following formula directly: K EG Ev EG v + Gu EG u 6

5.1 Gauss Remarkable Theorem

5.1 Gauss Remarkable Theorem 5.1 Gauss Remarkable Theorem Recall that, for a surface M, its Gauss curvature is defined by K = κ 1 κ 2 where κ 1, κ 2 are the principal curvatures of M. The principal curvatures are the eigenvalues of

More information

Surface Curvatures. (Com S 477/577 Notes) Yan-Bin Jia. Oct 23, Curve on a Surface: Normal and Geodesic Curvatures

Surface Curvatures. (Com S 477/577 Notes) Yan-Bin Jia. Oct 23, Curve on a Surface: Normal and Geodesic Curvatures Surface Curvatures (Com S 477/577 Notes) Yan-Bin Jia Oct 23, 2017 1 Curve on a Surface: Normal and Geodesic Curvatures One way to examine how much a surface bends is to look at the curvature of curves

More information

Curves on a Surface. (Com S 477/577 Notes) Yan-Bin Jia. Oct 24, 2017

Curves on a Surface. (Com S 477/577 Notes) Yan-Bin Jia. Oct 24, 2017 Curves on a Surface (Com S 477/577 Notes) Yan-Bin Jia Oct 24, 2017 1 Normal and Geodesic Curvatures One way to examine how much a surface bends is to look at the curvature of curves on the surface. Let

More information

Principal Curvatures

Principal Curvatures Principal Curvatures Com S 477/577 Notes Yan-Bin Jia Oct 26, 2017 1 Definition To furtheranalyze thenormal curvatureκ n, we make useof the firstandsecond fundamental forms: Edu 2 +2Fdudv +Gdv 2 and Ldu

More information

Homework JWR. Feb 6, 2014

Homework JWR. Feb 6, 2014 Homework JWR Feb 6, 2014 1. Exercise 1.5-12. Let the position of a particle at time t be given by α(t) = β(σ(t)) where β is parameterized by arclength and σ(t) = α(t) is the speed of the particle. Denote

More information

Differential Geometry: Curvature, Maps, and Pizza

Differential Geometry: Curvature, Maps, and Pizza Differential Geometry: Curvature, Maps, and Pizza Madelyne Ventura University of Maryland December 8th, 2015 Madelyne Ventura (University of Maryland) Curvature, Maps, and Pizza December 8th, 2015 1 /

More information

k-type null slant helices in Minkowski space-time

k-type null slant helices in Minkowski space-time MATHEMATICAL COMMUNICATIONS 8 Math. Commun. 20(2015), 8 95 k-type null slant helices in Minkowski space-time Emilija Nešović 1, Esra Betül Koç Öztürk2, and Ufuk Öztürk2 1 Department of Mathematics and

More information

arxiv: v1 [math.dg] 31 Mar 2014 Generalized Similar Frenet Curves

arxiv: v1 [math.dg] 31 Mar 2014 Generalized Similar Frenet Curves arxiv:14037908v1 [mathdg] 31 Mar 2014 Generalize Similar Frenet Curves Fatma GÖKÇELİK, Seher KAYA, Yusuf YAYLI, an F Nejat EKMEKCİ Abstract The paper is evote to ifferential geometric invariants etermining

More information

ON GENERALIZED NULL MANNHEIM CURVES IN MINKOWSKI SPACE-TIME. Milica Grbović, Kazim Ilarslan, and Emilija Nešović

ON GENERALIZED NULL MANNHEIM CURVES IN MINKOWSKI SPACE-TIME. Milica Grbović, Kazim Ilarslan, and Emilija Nešović PUBLICATIONS DE L INSTITUT MATHÉMATIQUE Nouvelle série, tome 99(113 (016, 77 98 DOI: 10.98/PIM1613077G ON GENERALIZED NULL MANNHEIM CURVES IN MINKOWSKI SPACE-TIME Milica Grbović, Kazim Ilarslan, and Emilija

More information

1-TYPE CURVES AND BIHARMONIC CURVES IN EUCLIDEAN 3-SPACE

1-TYPE CURVES AND BIHARMONIC CURVES IN EUCLIDEAN 3-SPACE International Electronic Journal of Geometry Volume 4 No. 1 pp. 97-101 (2011) c IEJG 1-TYPE CURVES AND BIHARMONIC CURVES IN EUCLIDEAN 3-SPACE (Communicated by Shyuichi Izumiya) Abstract. We study 1-type

More information

1.17 The Frenet-Serret Frame and Torsion. N(t) := T (t) κ(t).

1.17 The Frenet-Serret Frame and Torsion. N(t) := T (t) κ(t). Math 497C Oct 1, 2004 1 Curves and Surfaces Fall 2004, PSU Lecture Notes 7 1.17 The Frenet-Serret Frame and Torsion Recall that if α: I R n is a unit speed curve, then the unit tangent vector is defined

More information

DENSITY OF PERIODIC GEODESICS IN THE UNIT TANGENT BUNDLE OF A COMPACT HYPERBOLIC SURFACE

DENSITY OF PERIODIC GEODESICS IN THE UNIT TANGENT BUNDLE OF A COMPACT HYPERBOLIC SURFACE DENSITY OF PERIODIC GEODESICS IN THE UNIT TANGENT BUNDLE OF A COMPACT HYPERBOLIC SURFACE Marcos Salvai FaMAF, Ciudad Universitaria, 5000 Córdoba, Argentina. e-mail: salvai@mate.uncor.edu Abstract Let S

More information

Chapter 3 Common Families of Distributions. Definition 3.4.1: A family of pmfs or pdfs is called exponential family if it can be expressed as

Chapter 3 Common Families of Distributions. Definition 3.4.1: A family of pmfs or pdfs is called exponential family if it can be expressed as Lecture 0 on BST 63: Statistical Theory I Kui Zhang, 09/9/008 Review for the previous lecture Definition: Several continuous distributions, including uniform, gamma, normal, Beta, Cauchy, double exponential

More information

"Pricing Exotic Options using Strong Convergence Properties

Pricing Exotic Options using Strong Convergence Properties Fourth Oxford / Princeton Workshop on Financial Mathematics "Pricing Exotic Options using Strong Convergence Properties Klaus E. Schmitz Abe schmitz@maths.ox.ac.uk www.maths.ox.ac.uk/~schmitz Prof. Mike

More information

The Smarandache Curves on H 0

The Smarandache Curves on H 0 Gazi University Journal of Science GU J Sci 9():69-77 (6) The Smarandache Curves on H Murat SAVAS, Atakan Tugkan YAKUT,, Tugba TAMIRCI Gazi University, Faculty of Sciences, Department of Mathematics, 65

More information

A Characterization for Bishop Equations of Parallel Curves according to Bishop Frame in E 3

A Characterization for Bishop Equations of Parallel Curves according to Bishop Frame in E 3 Bol. Soc. Paran. Mat. (3s.) v. 33 1 (2015): 33 39. c SPM ISSN-2175-1188 on line ISSN-00378712 in press SPM: www.spm.uem.br/bspm doi:10.5269/bspm.v33i1.21712 A Characterization for Bishop Equations of Parallel

More information

Part 3: Trust-region methods for unconstrained optimization. Nick Gould (RAL)

Part 3: Trust-region methods for unconstrained optimization. Nick Gould (RAL) Part 3: Trust-region methods for unconstrained optimization Nick Gould (RAL) minimize x IR n f(x) MSc course on nonlinear optimization UNCONSTRAINED MINIMIZATION minimize x IR n f(x) where the objective

More information

Application of an Interval Backward Finite Difference Method for Solving the One-Dimensional Heat Conduction Problem

Application of an Interval Backward Finite Difference Method for Solving the One-Dimensional Heat Conduction Problem Application of an Interval Backward Finite Difference Method for Solving the One-Dimensional Heat Conduction Problem Malgorzata A. Jankowska 1, Andrzej Marciniak 2 and Tomasz Hoffmann 2 1 Poznan University

More information

Smarandache Curves on S 2. Key Words: Smarandache Curves, Sabban Frame, Geodesic Curvature. Contents. 1 Introduction Preliminaries 52

Smarandache Curves on S 2. Key Words: Smarandache Curves, Sabban Frame, Geodesic Curvature. Contents. 1 Introduction Preliminaries 52 Bol. Soc. Paran. Mat. (s.) v. (04): 5 59. c SPM ISSN-75-88 on line ISSN-00787 in press SPM: www.spm.uem.br/bspm doi:0.569/bspm.vi.94 Smarandache Curves on S Kemal Taşköprü and Murat Tosun abstract: In

More information

Invariant Variational Problems & Integrable Curve Flows. Peter J. Olver University of Minnesota olver

Invariant Variational Problems & Integrable Curve Flows. Peter J. Olver University of Minnesota   olver Invariant Variational Problems & Integrable Curve Flows Peter J. Olver University of Minnesota http://www.math.umn.edu/ olver Cocoyoc, November, 2005 1 Variational Problems x = (x 1,..., x p ) u = (u 1,...,

More information

Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.

Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise. Math 224 Q Exam 3A Fall 217 Tues Dec 12 Version A Problem 1. Let X be the continuous random variable defined by the following pdf: { 1 x/2 when x 2, f(x) otherwise. (a) Compute the mean µ E[X]. E[X] x

More information

ECON 815. A Basic New Keynesian Model II

ECON 815. A Basic New Keynesian Model II ECON 815 A Basic New Keynesian Model II Winter 2015 Queen s University ECON 815 1 Unemployment vs. Inflation 12 10 Unemployment 8 6 4 2 0 1 1.5 2 2.5 3 3.5 4 4.5 5 Core Inflation 14 12 10 Unemployment

More information

Financial Giffen Goods: Examples and Counterexamples

Financial Giffen Goods: Examples and Counterexamples Financial Giffen Goods: Examples and Counterexamples RolfPoulsen and Kourosh Marjani Rasmussen Abstract In the basic Markowitz and Merton models, a stock s weight in efficient portfolios goes up if its

More information

Economathematics. Problem Sheet 1. Zbigniew Palmowski. Ws 2 dw s = 1 t

Economathematics. Problem Sheet 1. Zbigniew Palmowski. Ws 2 dw s = 1 t Economathematics Problem Sheet 1 Zbigniew Palmowski 1. Calculate Ee X where X is a gaussian random variable with mean µ and volatility σ >.. Verify that where W is a Wiener process. Ws dw s = 1 3 W t 3

More information

Final Projects Introduction to Numerical Analysis Professor: Paul J. Atzberger

Final Projects Introduction to Numerical Analysis Professor: Paul J. Atzberger Final Projects Introduction to Numerical Analysis Professor: Paul J. Atzberger Due Date: Friday, December 12th Instructions: In the final project you are to apply the numerical methods developed in the

More information

Math F412: Homework 3 Solutions February 14, Solution: By the Fundamental Theorem of Calculus applied to the coordinates of α we have

Math F412: Homework 3 Solutions February 14, Solution: By the Fundamental Theorem of Calculus applied to the coordinates of α we have 1. Let k() be a mooth function on R. Let α() = ( θ() = co(θ(u)) du, k(u) du in(θ(u)) du). Show that α i a mooth unit peed curve with igned curvature κ p () = k(). By the Fundamental Theorem of Calculu

More information

On Toponogov s Theorem

On Toponogov s Theorem On Toponogov s Theorem Viktor Schroeder 1 Trigonometry of constant curvature spaces Let κ R be given. Let M κ be the twodimensional simply connected standard space of constant curvature κ. Thus M κ is

More information

e.g. + 1 vol move in the 30delta Puts would be example of just a changing put skew

e.g. + 1 vol move in the 30delta Puts would be example of just a changing put skew Calculating vol skew change risk (skew-vega) Ravi Jain 2012 Introduction An interesting and important risk in an options portfolio is the impact of a changing implied volatility skew. It is not uncommon

More information

EE 521 Instrumentation and Measurements Fall 2007 Solutions for homework assignment #2

EE 521 Instrumentation and Measurements Fall 2007 Solutions for homework assignment #2 Problem 1 (1) EE 51 Instrumentation and Measurements Fall 007 Solutions for homework assignment # f(x) = 1 σ π e () If the height of the peaks in the distribution as drawn are assumed to be 1, then the

More information

1) 4(7 + 4) = 2(x + 6) 2) x(x + 5) = (x + 1)(x + 2) 3) (x + 2)(x + 5) = 2x(x + 2) 10.6 Warmup Solve the equation. Tuesday, March 24, 2:56

1) 4(7 + 4) = 2(x + 6) 2) x(x + 5) = (x + 1)(x + 2) 3) (x + 2)(x + 5) = 2x(x + 2) 10.6 Warmup Solve the equation. Tuesday, March 24, 2:56 10.6 Warmup Solve the equation. 1) 4(7 + 4) = ( + 6) ) ( + 5) = ( + 1)( + ) 3) ( + )( + 5) = ( + ) 1 Geometry 10.6 Segment Relationships in Circles 10.6 Essential Question What relationships eist among

More information

Lecture Notes 6. Assume F belongs to a family of distributions, (e.g. F is Normal), indexed by some parameter θ.

Lecture Notes 6. Assume F belongs to a family of distributions, (e.g. F is Normal), indexed by some parameter θ. Sufficient Statistics Lecture Notes 6 Sufficiency Data reduction in terms of a particular statistic can be thought of as a partition of the sample space X. Definition T is sufficient for θ if the conditional

More information

CURVATURE AND TORSION FORMULAS FOR CONFLICT SETS

CURVATURE AND TORSION FORMULAS FOR CONFLICT SETS GEOMETRY AND TOPOLOGY OF CAUSTICS CAUSTICS 0 BANACH CENTER PUBLICATIONS, VOLUME 6 INSTITUTE OF MATHEMATICS POLISH ACADEMY OF SCIENCES WARSZAWA 004 CURVATURE AND TORSION FORMULAS FOR CONFLICT SETS MARTIJN

More information

MATH 121 GAME THEORY REVIEW

MATH 121 GAME THEORY REVIEW MATH 121 GAME THEORY REVIEW ERIN PEARSE Contents 1. Definitions 2 1.1. Non-cooperative Games 2 1.2. Cooperative 2-person Games 4 1.3. Cooperative n-person Games (in coalitional form) 6 2. Theorems and

More information

5.3 Interval Estimation

5.3 Interval Estimation 5.3 Interval Estimation Ulrich Hoensch Wednesday, March 13, 2013 Confidence Intervals Definition Let θ be an (unknown) population parameter. A confidence interval with confidence level C is an interval

More information

Research Article The Smarandache Curves on S 2 1 and Its Duality on H2 0

Research Article The Smarandache Curves on S 2 1 and Its Duality on H2 0 Hindawi Publishing Corporation Journal of Applied Mathematics Volume 04, Article ID 93586, pages http://dx.doi.org/0.55/04/93586 Research Article The Smarandache Curves on S and Its Duality on H 0 Atakan

More information

Dynamic Replication of Non-Maturing Assets and Liabilities

Dynamic Replication of Non-Maturing Assets and Liabilities Dynamic Replication of Non-Maturing Assets and Liabilities Michael Schürle Institute for Operations Research and Computational Finance, University of St. Gallen, Bodanstr. 6, CH-9000 St. Gallen, Switzerland

More information

Extended Libor Models and Their Calibration

Extended Libor Models and Their Calibration Extended Libor Models and Their Calibration Denis Belomestny Weierstraß Institute Berlin Vienna, 16 November 2007 Denis Belomestny (WIAS) Extended Libor Models and Their Calibration Vienna, 16 November

More information

SAQ KONTROLL AB Box 49306, STOCKHOLM, Sweden Tel: ; Fax:

SAQ KONTROLL AB Box 49306, STOCKHOLM, Sweden Tel: ; Fax: ProSINTAP - A Probabilistic Program for Safety Evaluation Peter Dillström SAQ / SINTAP / 09 SAQ KONTROLL AB Box 49306, 100 29 STOCKHOLM, Sweden Tel: +46 8 617 40 00; Fax: +46 8 651 70 43 June 1999 Page

More information

Empirical Approach to the Heston Model Parameters on the Exchange Rate USD / COP

Empirical Approach to the Heston Model Parameters on the Exchange Rate USD / COP Empirical Approach to the Heston Model Parameters on the Exchange Rate USD / COP ICASQF 2016, Cartagena - Colombia C. Alexander Grajales 1 Santiago Medina 2 1 University of Antioquia, Colombia 2 Nacional

More information

Systems of Ordinary Differential Equations. Lectures INF2320 p. 1/48

Systems of Ordinary Differential Equations. Lectures INF2320 p. 1/48 Systems of Ordinary Differential Equations Lectures INF2320 p. 1/48 Lectures INF2320 p. 2/48 ystems of ordinary differential equations Last two lectures we have studied models of the form y (t) = F(y),

More information

arxiv:math/ v1 [math.lo] 9 Dec 2006

arxiv:math/ v1 [math.lo] 9 Dec 2006 arxiv:math/0612246v1 [math.lo] 9 Dec 2006 THE NONSTATIONARY IDEAL ON P κ (λ) FOR λ SINGULAR Pierre MATET and Saharon SHELAH Abstract Let κ be a regular uncountable cardinal and λ > κ a singular strong

More information

Heterogeneous Firm, Financial Market Integration and International Risk Sharing

Heterogeneous Firm, Financial Market Integration and International Risk Sharing Heterogeneous Firm, Financial Market Integration and International Risk Sharing Ming-Jen Chang, Shikuan Chen and Yen-Chen Wu National DongHwa University Thursday 22 nd November 2018 Department of Economics,

More information

Math F412: Homework 4 Solutions February 20, κ I = s α κ α

Math F412: Homework 4 Solutions February 20, κ I = s α κ α All prts of this homework to be completed in Mple should be done in single worksheet. You cn submit either the worksheet by emil or printout of it with your homework. 1. Opre 1.4.1 Let α be not-necessrily

More information

Local Volatility Dynamic Models

Local Volatility Dynamic Models René Carmona Bendheim Center for Finance Department of Operations Research & Financial Engineering Princeton University Columbia November 9, 27 Contents Joint work with Sergey Nadtochyi Motivation 1 Understanding

More information

DRAFT. 1 exercise in state (S, t), π(s, t) = 0 do not exercise in state (S, t) Review of the Risk Neutral Stock Dynamics

DRAFT. 1 exercise in state (S, t), π(s, t) = 0 do not exercise in state (S, t) Review of the Risk Neutral Stock Dynamics Chapter 12 American Put Option Recall that the American option has strike K and maturity T and gives the holder the right to exercise at any time in [0, T ]. The American option is not straightforward

More information

Two hours. To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER

Two hours. To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER Two hours MATH20802 To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER STATISTICAL METHODS Answer any FOUR of the SIX questions.

More information

LEAST-SQUARES VERSUS MINIMUM-ZONE FORM DEVIATIONS

LEAST-SQUARES VERSUS MINIMUM-ZONE FORM DEVIATIONS Vienna, AUSTRIA,, September 5-8 LEAST-SQUARES VERSUS MIIMUM-ZOE FORM DEVIATIOS D Janecki and S Adamczak Center for Laser Technology of Metals and Faculty of Mechanical Engineering Kielce University of

More information

Euler Savary s Formula On Complex Plane C

Euler Savary s Formula On Complex Plane C Applied Mathematics E-Notes, 606, 65-7 c ISSN 607-50 Available free at mirror sites of http://www.math.nthu.edu.tw/ amen/ Euler Savary s Formula On Complex Plane C Mücahit Akbıyık, Salim Yüce Received

More information

X(t) = B(t), t 0,

X(t) = B(t), t 0, IEOR 4106: Introduction to Operations Research: Stochastic Models Spring 2007, Professor Whitt, Final Exam Chapters 4-7 and 10 in Ross, Wednesday, May 9, 1:10pm-4:00pm Open Book: but only the Ross textbook,

More information

Chapter 7: Portfolio Theory

Chapter 7: Portfolio Theory Chapter 7: Portfolio Theory 1. Introduction 2. Portfolio Basics 3. The Feasible Set 4. Portfolio Selection Rules 5. The Efficient Frontier 6. Indifference Curves 7. The Two-Asset Portfolio 8. Unrestriceted

More information

Parametric Inference and Dynamic State Recovery from Option Panels. Nicola Fusari

Parametric Inference and Dynamic State Recovery from Option Panels. Nicola Fusari Parametric Inference and Dynamic State Recovery from Option Panels Nicola Fusari Joint work with Torben G. Andersen and Viktor Todorov July 2012 Motivation Under realistic assumptions derivatives are nonredundant

More information

A continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x)

A continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x) Section 6-2 I. Continuous Probability Distributions A continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x) to represent a probability density

More information

The stochastic calculus

The stochastic calculus Gdansk A schedule of the lecture Stochastic differential equations Ito calculus, Ito process Ornstein - Uhlenbeck (OU) process Heston model Stopping time for OU process Stochastic differential equations

More information

On multivariate Multi-Resolution Analysis, using generalized (non homogeneous) polyharmonic splines. or: A way for deriving RBF and associated MRA

On multivariate Multi-Resolution Analysis, using generalized (non homogeneous) polyharmonic splines. or: A way for deriving RBF and associated MRA MAIA conference Erice (Italy), September 6, 3 On multivariate Multi-Resolution Analysis, using generalized (non homogeneous) polyharmonic splines or: A way for deriving RBF and associated MRA Christophe

More information

Saddlepoint Approximation Methods for Pricing. Financial Options on Discrete Realized Variance

Saddlepoint Approximation Methods for Pricing. Financial Options on Discrete Realized Variance Saddlepoint Approximation Methods for Pricing Financial Options on Discrete Realized Variance Yue Kuen KWOK Department of Mathematics Hong Kong University of Science and Technology Hong Kong * This is

More information

Final exam solutions

Final exam solutions EE365 Stochastic Control / MS&E251 Stochastic Decision Models Profs. S. Lall, S. Boyd June 5 6 or June 6 7, 2013 Final exam solutions This is a 24 hour take-home final. Please turn it in to one of the

More information

Brownian Motion. Richard Lockhart. Simon Fraser University. STAT 870 Summer 2011

Brownian Motion. Richard Lockhart. Simon Fraser University. STAT 870 Summer 2011 Brownian Motion Richard Lockhart Simon Fraser University STAT 870 Summer 2011 Richard Lockhart (Simon Fraser University) Brownian Motion STAT 870 Summer 2011 1 / 33 Purposes of Today s Lecture Describe

More information

From Discrete Time to Continuous Time Modeling

From Discrete Time to Continuous Time Modeling From Discrete Time to Continuous Time Modeling Prof. S. Jaimungal, Department of Statistics, University of Toronto 2004 Arrow-Debreu Securities 2004 Prof. S. Jaimungal 2 Consider a simple one-period economy

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

Stochastic Volatility (Working Draft I)

Stochastic Volatility (Working Draft I) Stochastic Volatility (Working Draft I) Paul J. Atzberger General comments or corrections should be sent to: paulatz@cims.nyu.edu 1 Introduction When using the Black-Scholes-Merton model to price derivative

More information

INTEREST RATES AND FX MODELS

INTEREST RATES AND FX MODELS INTEREST RATES AND FX MODELS 6. LIBOR Market Model Andrew Lesniewski Courant Institute of Mathematical Sciences New York University New York March 6, 2013 2 Interest Rates & FX Models Contents 1 Introduction

More information

Partitioned Analysis of Coupled Systems

Partitioned Analysis of Coupled Systems Partitioned Analysis of Coupled Systems Hermann G. Matthies, Rainer Niekamp, Jan Steindorf Technische Universität Braunschweig Brunswick, Germany wire@tu-bs.de http://www.wire.tu-bs.de Coupled Problems

More information

Exam M Fall 2005 PRELIMINARY ANSWER KEY

Exam M Fall 2005 PRELIMINARY ANSWER KEY Exam M Fall 005 PRELIMINARY ANSWER KEY Question # Answer Question # Answer 1 C 1 E C B 3 C 3 E 4 D 4 E 5 C 5 C 6 B 6 E 7 A 7 E 8 D 8 D 9 B 9 A 10 A 30 D 11 A 31 A 1 A 3 A 13 D 33 B 14 C 34 C 15 A 35 A

More information

Uniform Refraction in Negative Refractive Index Materials

Uniform Refraction in Negative Refractive Index Materials Haverford College Haverford Scholarship Faculty Publications Mathematics 2015 Uniform Refraction in Negative Refractive Index Materials Eric Stachura Haverford College, estachura@haverford.edu Cristian

More information

Prize offered for the solution of a dynamic blocking problem

Prize offered for the solution of a dynamic blocking problem Prize offered for the solution of a dynamic blocking problem Posted by A. Bressan on January 19, 2011 Statement of the problem Fire is initially burning on the unit disc in the plane IR 2, and propagateswith

More information

Continuous Distributions

Continuous Distributions Quantitative Methods 2013 Continuous Distributions 1 The most important probability distribution in statistics is the normal distribution. Carl Friedrich Gauss (1777 1855) Normal curve A normal distribution

More information

Log-Robust Portfolio Management

Log-Robust Portfolio Management Log-Robust Portfolio Management Dr. Aurélie Thiele Lehigh University Joint work with Elcin Cetinkaya and Ban Kawas Research partially supported by the National Science Foundation Grant CMMI-0757983 Dr.

More information

Posterior Inference. , where should we start? Consider the following computational procedure: 1. draw samples. 2. convert. 3. compute properties

Posterior Inference. , where should we start? Consider the following computational procedure: 1. draw samples. 2. convert. 3. compute properties Posterior Inference Example. Consider a binomial model where we have a posterior distribution for the probability term, θ. Suppose we want to make inferences about the log-odds γ = log ( θ 1 θ), where

More information

Extended Libor Models and Their Calibration

Extended Libor Models and Their Calibration Extended Libor Models and Their Calibration Denis Belomestny Weierstraß Institute Berlin Haindorf, 7 Februar 2008 Denis Belomestny (WIAS) Extended Libor Models and Their Calibration Haindorf, 7 Februar

More information

International Trade Elasticity Puzzle & Dynamic market penetrat

International Trade Elasticity Puzzle & Dynamic market penetrat International Trade Elasticity Puzzle & Dynamic market penetration April 2012 Question Motivation How can we explain the low short run trade elasticity and large long run elasticity. Question Motivation

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

Supplementary Appendix to The Risk Premia Embedded in Index Options

Supplementary Appendix to The Risk Premia Embedded in Index Options Supplementary Appendix to The Risk Premia Embedded in Index Options Torben G. Andersen Nicola Fusari Viktor Todorov December 214 Contents A The Non-Linear Factor Structure of Option Surfaces 2 B Additional

More information

Continuous random variables

Continuous random variables Continuous random variables probability density function (f(x)) the probability distribution function of a continuous random variable (analogous to the probability mass function for a discrete random variable),

More information

Using derivatives to find the shape of a graph

Using derivatives to find the shape of a graph Using derivatives to find the shape of a graph Example 1 The graph of y = x 2 is decreasing for x < 0 and increasing for x > 0. Notice that where the graph is decreasing the slope of the tangent line,

More information

Calibration of Interest Rates

Calibration of Interest Rates WDS'12 Proceedings of Contributed Papers, Part I, 25 30, 2012. ISBN 978-80-7378-224-5 MATFYZPRESS Calibration of Interest Rates J. Černý Charles University, Faculty of Mathematics and Physics, Prague,

More information

AN ANALYTICALLY TRACTABLE UNCERTAIN VOLATILITY MODEL

AN ANALYTICALLY TRACTABLE UNCERTAIN VOLATILITY MODEL AN ANALYTICALLY TRACTABLE UNCERTAIN VOLATILITY MODEL FABIO MERCURIO BANCA IMI, MILAN http://www.fabiomercurio.it 1 Stylized facts Traders use the Black-Scholes formula to price plain-vanilla options. An

More information

Research Statement. Dapeng Zhan

Research Statement. Dapeng Zhan Research Statement Dapeng Zhan The Schramm-Loewner evolution (SLE), first introduced by Oded Schramm ([12]), is a oneparameter (κ (0, )) family of random non-self-crossing curves, which has received a

More information

Homework # 8 - [Due on Wednesday November 1st, 2017]

Homework # 8 - [Due on Wednesday November 1st, 2017] Homework # 8 - [Due on Wednesday November 1st, 2017] 1. A tax is to be levied on a commodity bought and sold in a competitive market. Two possible forms of tax may be used: In one case, a per unit tax

More information

1 Residual life for gamma and Weibull distributions

1 Residual life for gamma and Weibull distributions Supplement to Tail Estimation for Window Censored Processes Residual life for gamma and Weibull distributions. Gamma distribution Let Γ(k, x = x yk e y dy be the upper incomplete gamma function, and let

More information

Inflation-indexed Swaps and Swaptions

Inflation-indexed Swaps and Swaptions Inflation-indexed Swaps and Swaptions Mia Hinnerich Aarhus University, Denmark Vienna University of Technology, April 2009 M. Hinnerich (Aarhus University) Inflation-indexed Swaps and Swaptions April 2009

More information

Stock Loan Valuation Under Brownian-Motion Based and Markov Chain Stock Models

Stock Loan Valuation Under Brownian-Motion Based and Markov Chain Stock Models Stock Loan Valuation Under Brownian-Motion Based and Markov Chain Stock Models David Prager 1 1 Associate Professor of Mathematics Anderson University (SC) Based on joint work with Professor Qing Zhang,

More information

Statistical Tables Compiled by Alan J. Terry

Statistical Tables Compiled by Alan J. Terry Statistical Tables Compiled by Alan J. Terry School of Science and Sport University of the West of Scotland Paisley, Scotland Contents Table 1: Cumulative binomial probabilities Page 1 Table 2: Cumulative

More information

FX Smile Modelling. 9 September September 9, 2008

FX Smile Modelling. 9 September September 9, 2008 FX Smile Modelling 9 September 008 September 9, 008 Contents 1 FX Implied Volatility 1 Interpolation.1 Parametrisation............................. Pure Interpolation.......................... Abstract

More information

Arrow-Debreu Equilibrium

Arrow-Debreu Equilibrium Arrow-Debreu Equilibrium Econ 2100 Fall 2017 Lecture 23, November 21 Outline 1 Arrow-Debreu Equilibrium Recap 2 Arrow-Debreu Equilibrium With Only One Good 1 Pareto Effi ciency and Equilibrium 2 Properties

More information

Method of Characteristics

Method of Characteristics The Ryan C. Trinity University Partial Differential Equations January 22, 2015 Linear and Quasi-Linear (first order) PDEs A PDE of the form A(x,y) u x +B(x,y) u y +C 1(x,y)u = C 0 (x,y) is called a (first

More information

Chapter 8: CAPM. 1. Single Index Model. 2. Adding a Riskless Asset. 3. The Capital Market Line 4. CAPM. 5. The One-Fund Theorem

Chapter 8: CAPM. 1. Single Index Model. 2. Adding a Riskless Asset. 3. The Capital Market Line 4. CAPM. 5. The One-Fund Theorem Chapter 8: CAPM 1. Single Index Model 2. Adding a Riskless Asset 3. The Capital Market Line 4. CAPM 5. The One-Fund Theorem 6. The Characteristic Line 7. The Pricing Model Single Index Model 1 1. Covariance

More information

6.2 Normal Distribution. Normal Distributions

6.2 Normal Distribution. Normal Distributions 6.2 Normal Distribution Normal Distributions 1 Homework Read Sec 6-1, and 6-2. Make sure you have a good feel for the normal curve. Do discussion question p302 2 3 Objective Identify Complete normal model

More information

Shape of the Yield Curve Under CIR Single Factor Model: A Note

Shape of the Yield Curve Under CIR Single Factor Model: A Note Shape of the Yield Curve Under CIR Single Factor Model: A Note Raymond Kan University of Chicago June, 1992 Abstract This note derives the shapes of the yield curve as a function of the current spot rate

More information

IMPA Commodities Course : Forward Price Models

IMPA Commodities Course : Forward Price Models IMPA Commodities Course : Forward Price Models Sebastian Jaimungal sebastian.jaimungal@utoronto.ca Department of Statistics and Mathematical Finance Program, University of Toronto, Toronto, Canada http://www.utstat.utoronto.ca/sjaimung

More information

Chapter 3: Black-Scholes Equation and Its Numerical Evaluation

Chapter 3: Black-Scholes Equation and Its Numerical Evaluation Chapter 3: Black-Scholes Equation and Its Numerical Evaluation 3.1 Itô Integral 3.1.1 Convergence in the Mean and Stieltjes Integral Definition 3.1 (Convergence in the Mean) A sequence {X n } n ln of random

More information

Numerical solution of conservation laws applied to the Shallow Water Wave Equations

Numerical solution of conservation laws applied to the Shallow Water Wave Equations Numerical solution of conservation laws applie to the Shallow Water Wave Equations Stephen G Roberts Mathematical Sciences Institute, Australian National University Upate January 17, 2013 (base on notes

More information

Mathematics (Project Maths Phase 2)

Mathematics (Project Maths Phase 2) L.17 NAME SCHOOL TEACHER Pre-Leaving Certificate Examination, 2013 Mathematics (Project Maths Phase 2) Paper 1 Higher Level Time: 2 hours, 30 minutes 300 marks For examiner Question 1 Centre stamp 2 3

More information

MODELING INVESTMENT RETURNS WITH A MULTIVARIATE ORNSTEIN-UHLENBECK PROCESS

MODELING INVESTMENT RETURNS WITH A MULTIVARIATE ORNSTEIN-UHLENBECK PROCESS MODELING INVESTMENT RETURNS WITH A MULTIVARIATE ORNSTEIN-UHLENBECK PROCESS by Zhong Wan B.Econ., Nankai University, 27 a Project submitted in partial fulfillment of the requirements for the degree of Master

More information

Sentiments and Aggregate Fluctuations

Sentiments and Aggregate Fluctuations Sentiments and Aggregate Fluctuations Jess Benhabib Pengfei Wang Yi Wen June 15, 2012 Jess Benhabib Pengfei Wang Yi Wen () Sentiments and Aggregate Fluctuations June 15, 2012 1 / 59 Introduction We construct

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

3.1 The Marschak-Machina triangle and risk aversion

3.1 The Marschak-Machina triangle and risk aversion Chapter 3 Risk aversion 3.1 The Marschak-Machina triangle and risk aversion One of the earliest, and most useful, graphical tools used to analyse choice under uncertainty was a triangular graph that was

More information

The Lognormal Interest Rate Model and Eurodollar Futures

The Lognormal Interest Rate Model and Eurodollar Futures GLOBAL RESEARCH ANALYTICS The Lognormal Interest Rate Model and Eurodollar Futures CITICORP SECURITIES,INC. 399 Park Avenue New York, NY 143 Keith Weintraub Director, Analytics 1-559-97 Michael Hogan Ex

More information

A Stochastic Levenberg-Marquardt Method Using Random Models with Application to Data Assimilation

A Stochastic Levenberg-Marquardt Method Using Random Models with Application to Data Assimilation A Stochastic Levenberg-Marquardt Method Using Random Models with Application to Data Assimilation E Bergou Y Diouane V Kungurtsev C W Royer July 5, 08 Abstract Globally convergent variants of the Gauss-Newton

More information

CHAPTER 12. Hedging. hedging strategy = replicating strategy. Question : How to find a hedging strategy? In other words, for an attainable contingent

CHAPTER 12. Hedging. hedging strategy = replicating strategy. Question : How to find a hedging strategy? In other words, for an attainable contingent CHAPTER 12 Hedging hedging dddddddddddddd ddd hedging strategy = replicating strategy hedgingdd) ddd Question : How to find a hedging strategy? In other words, for an attainable contingent claim, find

More information

Problem set Fall 2012.

Problem set Fall 2012. Problem set 1. 14.461 Fall 2012. Ivan Werning September 13, 2012 References: 1. Ljungqvist L., and Thomas J. Sargent (2000), Recursive Macroeconomic Theory, sections 17.2 for Problem 1,2. 2. Werning Ivan

More information