Principal Curvatures
|
|
- Melanie Robbins
- 5 years ago
- Views:
Transcription
1 Principal Curvatures Com S 477/577 Notes Yan-Bin Jia Oct 26, Definition To furtheranalyze thenormal curvatureκ n, we make useof the firstandsecond fundamental forms: Edu 2 +2Fdudv +Gdv 2 and Ldu 2 +2Mdudv +Ndv 2. For convenience, we introduce two symmetric matrices E F F 1 = and F F G 2 = L M M N The tangent vector of the unit-speed curve γt = σut,vt is γ = uσ u + vσ v. Introducing T = u, v t, where t denotes the transpose operator, the normal curvature equation can be rewritten as κ n = L u 2 +2M u v +N v 2 κ n = T t F 2 T. 1 Since σ u and σ v span the tangent plane, consider two tangent vectors: We obtain their inner product: ˆt 1 = ξ 1 σ u +η 1 σ v and ˆt 2 = ξ 2 σ u +η 2 σ v. ˆt 1 ˆt 2 = ξ 1 σ u +η 1 σ v ξ 2 σ u +η 2 σ v = Eξ 1 ξ 2 +Fξ 1 η 2 +ξ 2 η 1 +Gη 1 η 2 = T1 t E F T F G 2, 2 where T 1 = ξ1 η 1 and T 2 = ξ2 η The material is adapted from the book Elementary Differential Geometry by Andrew Pressley, Springer-Verlag, 1
2 The principal curvatures of the surface patch σ are the roots of the equation detf 2 κf 1 = L κe M κf M κf N κg = 0. 3 From the linear independence of σ u and σ v, it is easy to show that matrix F 1 is always invertible. Equation 3 essentially states that the principal curvatures are the eigenvalues of F1 1 F 2. Let κ be a principal curvature of F1 1 F 2, and T = ξ,η T the corresponding eigenvector. That F1 1 F 2T = κt implies F 2 κf 1 T = 0. 4 The unit tangent vector ˆt in the direction of ξσ u +ησ v is called the principal vector corresponding to the principal curvature κ. Theorem 1 Let κ 1 and κ 2 be the principal curvatures at a point p of a surface patch σ. Then i κ 1,κ 2 R; ii if κ 1 = κ 2 = κ, then F 2 = κf 1 and every tangent vector at p is a principal vector. iii if κ 1 κ 2, then the two corresponding principal vectors are perpendicular to each other For a proof of the theorem, we refer the reader to [2, ]. Intuitively, the principal vectors give the directions of maximum and minimum bending of the surface at the point, and the principal curvatures measure the bending rates. In case ii, the point is umbilic. In this case, the surface bends the same amount in all directions at p thus all directions are principal. Example 2. A sphere bends the same amount in every direction. Take the unit sphere in Example 9 in the notes Surfaces, for instance, with the parametrization We found previously that σθ,φ = cosθcosφ,cosθsinφ,sinθ, E = 1, F = 0, G = cos 2 θ. Since a sphere is a surface of revolution, we can plug in the result from Example 1, with fθ = cosθ and gu = sinθ: L = f g fġ = sinθ sinθ cosθcosθ = 1, M = 0, N = fġ = cos 2 θ. Hence the principal curvatures are the roots of detf 2 kf 1 = 1 κ 0 0 cos 2 θ κcos 2 θ = 0. Hence κ = 1. And every tangent direction is a principal vector. 2
3 Example 3. Consider a cylinder with the z-axis as its axis and circular cross sections of unit radius. The parametrization is given as ˆn ˆt 1 ˆt 2 p σu,v = cosv,sinv,u. The coefficients of the first and second fundamental forms can be computed as E = 1, F = 0, G = 1, L = 0, M = 0, N = 1. The principal curvatures are roots of 0 κ κ So we obtain κ 1 = 0 and κ 2 = 1. The eigenvectors T i = ξ i,η i, i = 1,2 of F 1 1 F 2 are found from solving the equation F 2 κ i F 1 T i = 0. The results are T 1 = λ 1 1,0 t and T 2 = λ 2 0,1 t for any non-zero λ 1,λ 2 R. Hence the principal vector ˆt 1 is along the direction of 1σ u +0σ v, i.e., ˆt 1 = 0,0,1. The principal vector ˆt 2 is along the direction of 0σ u +1σ v = sinv,cosv,0, i.e., ˆt 2 = sinv,cosv,0. A curve γ on the surface σ is a principal curve if its velocity γ always points in a principal direction, that is, the direction of a principal vector. At every point on a principal curve, the normal curvature is a maximum or minimum. The next figure shows some principal curves on the ellipsoid 2 Euler s Formula x y2 5 +z2 = 1. Supposethetwoprincipalcurvaturesκ 1 κ 2 atponthesurfaceσ. Then by Theorem 1iii, the two corresponding principal vectors ˆt 1 = ξ 1 σ u + η 1 σ v and ˆt 2 = ξ 2 σ u + η 2 σ v must be orthogonal to each other. Denote by T 1 = ξ 1,η 1 t and T 2 = ξ 2,η 2 t. Replace the T in 4 with T j, j = 1,2, multiply both sides of the equation by Ti t to the left, and move the second resulting term to the right hand side of the equation. This yields T t i F 2T j = κ j T t i F 1T j, i,j = 1,2. Meanwhile, the orthogonality of the two principal vectors implies that To summarize, we have ˆt 1 ˆt 2 = T t 1 F 1T 2 = 0, from 2. { TiF t 2 T j = κ i TiF t κi, if i = j, 1 T j = 0, otherwise With the principal curvatures and vectors at p, we can evaluate the normal curvature in any direction. 3 5
4 Theorem 2 Let κ 1,κ 2 be the principal curvatures, and ˆt 1,ˆt 2 the two corresponding principal vectors of a patch σ at p. The normal curvature of σ in the direction û = cosθˆt 1 +sinθˆt 2 is κ n = κ 1 cos 2 θ+κ 2 sin 2 θ. Proof Let û = ξσ u + ησ v and T = ξ,η t. We first look at the special case κ 1 = κ 2 = κ. By Theorem 1ii, û = ξσ u +ησ v is a principal vector. The normal curvature in the direction û is κ n = T t F 2 T by 1 = κt t F 1 T by 4 = κû û = κ. 6 Meanwhile, we have κ 1 cos 2 θ+κ 2 sin 2 θ = κcos 2 θ+sin 2 θ = κ. So the theorem holds when the point is umbilic. Assume κ 1 κ 2. Therefore by Theorem 1iii, ˆt 1 and ˆt 2 are perpendicular to each other. Let ˆt i = ξ i σ u +η i σ v, and T i = ξ i,η i t. Thus, So we have û = ξσ u +ησ v, where û = cosθξ 1 σ u +η 1 σ v +sinθξ 2 σ u +η 2 σ v = ξ 1 cosθ +ξ 2 sinθσ u +η 1 cosθ+η 2 sinθσ v. ξ = ξ 1 cosθ+ξ 2 sinθ, η = η 1 cosθ+η 2 sinθ, The above is written succinctly as T = cosθt 1 +sinθt 2. By equation 1 the normal curvature in the û direction is κ n = cosθt t 1 +sinθtt 2 F 2cosθT 1 +sinθt 2 = cos 2 θt t 1 F 2T 1 +cosθsinθt t 1 F 2T 2 +T t 2 F 2T 1 +sin 2 θt t 2 F 2T 2 = κ 1 cos 2 θ+κ 2 sin 2 θ. The last step above followed from the equation 5. Theorem 2 implies that κ 1 and κ 2 are the maximum and minimum of any normal curvatures at the point. Equivalently, among all tangent directions at the point, the geometry varies the most in one principal direction while the least in the other. 4
5 3 Geometric Interpretation of Principal Curvatures In this section, we look at how the local shape at a surface point can be approximated using its principal curvatures and direction. The values of the principal curvatures and vectors at a point p on a surface patch σ tell us about the shape near p. To see this, we apply a rigid motion followed by a reparametrization. 1 More specifically, we move the origin to p and let the tangent plane to σ at p be the xy-plane with the x-axis and y-axis along the directions of the two principal vectors, which correspond to principal curvatures κ 1 and κ 2, respectively. Furthermore, we let the values of both parameters at the origin be zero, that is, σ0,0 = 0. 7 Without any ambiguity, we still denote the new parametrization by σ. Let us determine the function z = zx,y that describes the local shape. The unit principal vectors can be expressed in terms of the partial derivatives: So can any point x,y,0 in the tangent plane: where 1,0,0 = ξ 1 σ u +η 1 σ v, 0,1,0 = ξ 2 σ u +η 2 σ v. x,y,0 = x1,0,0 +y0,1,0 = xξ 1 σ u +η 1 σ v +yξ 2 σ u +η 2 σ v = sσ u +tσ v, 8 s = xξ 1 +yξ 2 and t = xη 1 +yη 2. 9 Let us evaluate σs,t at the parameter values s and t, applying Taylor s theorem with higher order terms in s and t neglected: σs,t = σ0,0+sσ u +tσ v s2 σ uu +2stσ uv +t 2 σ vv = x,y, s2 σ uu +2stσ uv +t 2 σ vv, by 7 and 8 All derivatives are evaluated at the origin p. Neglecting the second order terms added to x and y, the coordinates of σs,t is x,y,z, where z = σs,t ˆn = 1 2 Ls2 +2Mst+Nt 2 = 1 2 s t L M M N s. t 1 The shape does not change under any rigid motion or reparametrization. 5
6 z Writing T 1 = ξ1 η 1 and T 2 = ξ2 η 2, x σ p u 1 u 2 y we have from 9: s = xt 1 +yt 2. t Thus, z = 1 2 xt 1 +yt 2 t F 2 xt 1 +yt 2 = 1 x 2 T 1 F 2 T 1 +xyt t 2 1F 2 T 2 +T2F t 2 T 1 +y 2 T2F t 2 T 2 = 1 2 κ 1x 2 +κ 2 y 2, since T t i F 2T j = κ i if i = j or 0 otherwise. Hence the shape of a surface near the point p has a quadratic approximation determined by its principal curvature κ 1 and κ 2. It is described by the equation z = 1 2 κ 1x 2 +κ 2 y 2. 4 Covariant Derivative Next, welook at how to characterizes therate of change of a vector definedon asurfacewith respect to a tangent vector. Let us slightly abuse the notation ˆn to represent a function that assigns to every point p on the surface S the normal ˆnp at the point. Since ˆn is continuous, it is a vector field on S, and referred to as the normal vector field. Similarly, ˆt 1 and ˆt 2 are also vector fields on S that continuously assign to every point two orthogonal principal vectors. At the point p, a vector field Z typically changes differently in different tangential directions. The rate of change along a tangent w is characterized by its covariant derivative along w. More specifically, we let αt be a curve on S that has initial velocity α 0 = w. Consider restriction of Z to α. Then, the covariant derivative of Z with respect to w is defined to be w Z = dzαt dt. t=0 In particular, consider the u-curve αu = σu,v 0 passing through p = σu 0,v 0 at velocity w = σ u u 0,v 0. We have w Z = dzαu du u=u0 = dzσu,v 0 du u=u0 = Z u u 0,v 0. Reparametrize αu as a unit-speed curve βs, where s is arc length. Clearly, ds du 0 = α u 0 = σ u u 0,v 0. 6
7 At p, let ˆx = β 0 = σ u u 0,v 0 / σ u u 0,v 0 be the unit velocity of the u-curve. The covariant derivative with respect to ˆx is ˆx Z = dzβs ds s=0 = dzαus/du ds/du = Z uu 0,v 0 σ u u 0,v 0. u=u0 In the Darboux frame T-V-U at p of a unit-speed surface curve, where T is the curve tangent, U the unit surface normal ˆn, and V = U T, it holds that U = κ n T τ g V, where κ n and τ g are the surface s normal curvature and curve s geodesic torsion at p. Meanwhile, U is the covariant derivative along T, i.e., U = T U. The normal curvature at the point in the direction T is equivalently defined to be k n T [1, p. 196], for we have κ n T = U T = T U T. The principal curvatures are the normal curvatures in the two principal directions, that is, the covariant derivatives of the normal with respect to the principal vectors: κ 1 = κ n ˆt 1 = ˆt 1 U ˆt 1 = ˆt 1ˆn ˆt 1, κ 2 = κ n ˆt 2 = ˆt 2 U ˆt 2 = ˆt 2ˆn ˆt 2. It can be shown that ˆt iˆn ˆt j = 0 if i j. References [1] B. O Neill. Elementary Differential Geometry. Academic Press, Inc., [2] A. Pressley. Elementary Differential Geometry. Springer-Verlag London,
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 informationCurves 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 informationRiemannian Geometry, Key to Homework #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
More information5.1 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 informationSmarandache 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 information1.17 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 informationHomework 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 informationA 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 informationarxiv: 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 informationk-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 informationDENSITY 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 informationDifferential 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 informationCURVATURE 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 informationMethod 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 informationEuler 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 informationMath 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 informationThe 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"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 informationIMPA 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 informationTangent Lévy Models. Sergey Nadtochiy (joint work with René Carmona) Oxford-Man Institute of Quantitative Finance University of Oxford.
Tangent Lévy Models Sergey Nadtochiy (joint work with René Carmona) Oxford-Man Institute of Quantitative Finance University of Oxford June 24, 2010 6th World Congress of the Bachelier Finance Society Sergey
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 informationSaddlepoint 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 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 informationInvariant 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 informationResearch 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 information1-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 informationPart 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 informationDynamic 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 informationLinearity-Generating Processes, Unspanned Stochastic Volatility, and Interest-Rate Option Pricing
Linearity-Generating Processes, Unspanned Stochastic Volatility, and Interest-Rate Option Pricing Liuren Wu, Baruch College Joint work with Peter Carr and Xavier Gabaix at New York University Board of
More informationAdvanced Stochastic Processes.
Advanced Stochastic Processes. David Gamarnik LECTURE 16 Applications of Ito calculus to finance Lecture outline Trading strategies Black Scholes option pricing formula 16.1. Security price processes,
More informationPortfolio Choice. := δi j, the basis is orthonormal. Expressed in terms of the natural basis, x = j. x j x j,
Portfolio Choice Let us model portfolio choice formally in Euclidean space. There are n assets, and the portfolio space X = R n. A vector x X is a portfolio. Even though we like to see a vector as coordinate-free,
More informationINTEREST 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 informationsymmys.com 3.2 Projection of the invariants to the investment horizon
122 3 Modeling the market In the swaption world the underlying rate (3.57) has a bounded range and thus it does not display the explosive pattern typical of a stock price. Therefore the swaption prices
More informationUniform 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 informationM5MF6. Advanced Methods in Derivatives Pricing
Course: Setter: M5MF6 Dr Antoine Jacquier MSc EXAMINATIONS IN MATHEMATICS AND FINANCE DEPARTMENT OF MATHEMATICS April 2016 M5MF6 Advanced Methods in Derivatives Pricing Setter s signature...........................................
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 19 11/20/2013. Applications of Ito calculus to finance
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.7J Fall 213 Lecture 19 11/2/213 Applications of Ito calculus to finance Content. 1. Trading strategies 2. Black-Scholes option pricing formula 1 Security
More informationMath 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 informationChapter 2 Portfolio Management and the Capital Asset Pricing Model
Chapter 2 Portfolio Management and the Capital Asset Pricing Model In this chapter, we explore the issue of risk management in a portfolio of assets. The main issue is how to balance a portfolio, that
More informationON 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 informationApplication 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 informationDiscounting a mean reverting cash flow
Discounting a mean reverting cash flow Marius Holtan Onward Inc. 6/26/2002 1 Introduction Cash flows such as those derived from the ongoing sales of particular products are often fluctuating in a random
More informationExam Quantitative Finance (35V5A1)
Exam Quantitative Finance (35V5A1) Part I: Discrete-time finance Exercise 1 (20 points) a. Provide the definition of the pricing kernel k q. Relate this pricing kernel to the set of discount factors D
More informationChapter 10: Mixed strategies Nash equilibria, reaction curves and the equality of payoffs theorem
Chapter 10: Mixed strategies Nash equilibria reaction curves and the equality of payoffs theorem Nash equilibrium: The concept of Nash equilibrium can be extended in a natural manner to the mixed strategies
More informationParametric 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 informationONE FACTOR GAUSSIAN SHORT RATE MODEL IMPLEMENTATION. 1. model
ONE FACTOR GAUSSIAN SHORT RATE MODEL IMPLEMENTATION P. CASPERS First Version March 1, 2013 - This Version March 1, 2013 Abstract. We collect some results in Piterbarg, Interest Rate Modelling, needed for
More informationOn 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 informationA No-Arbitrage Theorem for Uncertain Stock Model
Fuzzy Optim Decis Making manuscript No (will be inserted by the editor) A No-Arbitrage Theorem for Uncertain Stock Model Kai Yao Received: date / Accepted: date Abstract Stock model is used to describe
More informationForward Dynamic Utility
Forward Dynamic Utility El Karoui Nicole & M RAD Mohamed UnivParis VI / École Polytechnique,CMAP elkaroui@cmapx.polytechnique.fr with the financial support of the "Fondation du Risque" and the Fédération
More informationGeneralized Affine Transform Formulae and Exact Simulation of the WMSV Model
On of Affine Processes on S + d Generalized Affine and Exact Simulation of the WMSV Model Department of Mathematical Science, KAIST, Republic of Korea 2012 SIAM Financial Math and Engineering joint work
More informationShape 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 informationMulti-dimensional Term Structure Models
Multi-dimensional Term Structure Models We will focus on the affine class. But first some motivation. A generic one-dimensional model for zero-coupon yields, y(t; τ), looks like this dy(t; τ) =... dt +
More informationMSc Financial Engineering CHRISTMAS ASSIGNMENT: MERTON S JUMP-DIFFUSION MODEL. To be handed in by monday January 28, 2013
MSc Financial Engineering 2012-13 CHRISTMAS ASSIGNMENT: MERTON S JUMP-DIFFUSION MODEL To be handed in by monday January 28, 2013 Department EMS, Birkbeck Introduction The assignment consists of Reading
More informationWeb-based Supplementary Materials for. A space-time conditional intensity model. for invasive meningococcal disease occurence
Web-based Supplementary Materials for A space-time conditional intensity model for invasive meningococcal disease occurence by Sebastian Meyer 1,2, Johannes Elias 3, and Michael Höhle 4,2 1 Department
More informationThe 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 informationOnline Appendices to Financing Asset Sales and Business Cycles
Online Appendices to Financing Asset Sales usiness Cycles Marc Arnold Dirk Hackbarth Tatjana Xenia Puhan August 22, 2017 University of St. allen, Unterer raben 21, 9000 St. allen, Switzerl. Telephone:
More informationSNELL S LAW AND UNIFORM REFRACTION. Contents
SNELL S LAW AND UNIFORM REFRACTION CRISTIAN E. GUTIÉRREZ Contents 1. Snell s law of refraction 1 1.1. In vector form 1 1.2. κ < 1 2 1.3. κ > 1 3 1.4. κ = 1 4 2. Uniform refraction 4 2.1. Surfaces with
More informationMean Variance Analysis and CAPM
Mean Variance Analysis and CAPM Yan Zeng Version 1.0.2, last revised on 2012-05-30. Abstract A summary of mean variance analysis in portfolio management and capital asset pricing model. 1. Mean-Variance
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 informationSimulating more interesting stochastic processes
Chapter 7 Simulating more interesting stochastic processes 7. Generating correlated random variables The lectures contained a lot of motivation and pictures. We'll boil everything down to pure algebra
More informationThe 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 informationStock 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 informationHints 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 informationLeader or Follower? A Payoff Analysis in Quadratic Utility Harsanyi Economy
Leader or Follower? A Payoff Analysis in Quadratic Utility Harsanyi Economy Sai Ma New York University Oct. 0, 015 Model Agents and Belief There are two players, called agent i {1, }. Each agent i chooses
More informationSTOCHASTIC INTEGRALS
Stat 391/FinMath 346 Lecture 8 STOCHASTIC INTEGRALS X t = CONTINUOUS PROCESS θ t = PORTFOLIO: #X t HELD AT t { St : STOCK PRICE M t : MG W t : BROWNIAN MOTION DISCRETE TIME: = t < t 1
More informationA 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 informationKing s College London
King s College London University Of London This paper is part of an examination of the College counting towards the award of a degree. Examinations are governed by the College Regulations under the authority
More informationProject 1: Double Pendulum
Final Projects Introduction to Numerical Analysis II http://www.math.ucsb.edu/ atzberg/winter2009numericalanalysis/index.html Professor: Paul J. Atzberger Due: Friday, March 20th Turn in to TA s Mailbox:
More informationFinancial 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 informationThe Binomial Lattice Model for Stocks: Introduction to Option Pricing
1/27 The Binomial Lattice Model for Stocks: Introduction to Option Pricing Professor Karl Sigman Columbia University Dept. IEOR New York City USA 2/27 Outline The Binomial Lattice Model (BLM) as a Model
More informationSupplementary 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 informationIEOR E4703: Monte-Carlo Simulation
IEOR E4703: Monte-Carlo Simulation Simulating Stochastic Differential Equations Martin Haugh Department of Industrial Engineering and Operations Research Columbia University Email: martin.b.haugh@gmail.com
More informationMultiname and Multiscale Default Modeling
Multiname and Multiscale Default Modeling Jean-Pierre Fouque University of California Santa Barbara Joint work with R. Sircar (Princeton) and K. Sølna (UC Irvine) Special Semester on Stochastics with Emphasis
More informationGirsanov s Theorem. Bernardo D Auria web: July 5, 2017 ICMAT / UC3M
Girsanov s Theorem Bernardo D Auria email: bernardo.dauria@uc3m.es web: www.est.uc3m.es/bdauria July 5, 2017 ICMAT / UC3M Girsanov s Theorem Decomposition of P-Martingales as Q-semi-martingales Theorem
More informationOnline Appendix to Financing Asset Sales and Business Cycles
Online Appendix to Financing Asset Sales usiness Cycles Marc Arnold Dirk Hackbarth Tatjana Xenia Puhan August 31, 2015 University of St. allen, Rosenbergstrasse 52, 9000 St. allen, Switzerl. Telephone:
More informationModelling strategies for bivariate circular data
Modelling strategies for bivariate circular data John T. Kent*, Kanti V. Mardia, & Charles C. Taylor Department of Statistics, University of Leeds 1 Introduction On the torus there are two common approaches
More informationSystems 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 informationChapter 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 informationShape 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 Toronto June, 199 Abstract This note derives the shapes of the yield curve as a function of the current spot rate
More informationwarwick.ac.uk/lib-publications
Original citation: Gogala, Jaka and Kennedy, Joanne E.. (217) Classification of two-and three-factor timehomogeneous separable LMMs. International Journal of Theoretical and Applied Finance, 2 (2). 17521.
More informationAn overview of some financial models using BSDE with enlarged filtrations
An overview of some financial models using BSDE with enlarged filtrations Anne EYRAUD-LOISEL Workshop : Enlargement of Filtrations and Applications to Finance and Insurance May 31st - June 4th, 2010, Jena
More informationA Numerical Approach to the Estimation of Search Effort in a Search for a Moving Object
Proceedings of the 1. Conference on Applied Mathematics and Computation Dubrovnik, Croatia, September 13 18, 1999 pp. 129 136 A Numerical Approach to the Estimation of Search Effort in a Search for a Moving
More informationDynamic Relative Valuation
Dynamic Relative Valuation Liuren Wu, Baruch College Joint work with Peter Carr from Morgan Stanley October 15, 2013 Liuren Wu (Baruch) Dynamic Relative Valuation 10/15/2013 1 / 20 The standard approach
More informationPDE Approach to Credit Derivatives
PDE Approach to Credit Derivatives Marek Rutkowski School of Mathematics and Statistics University of New South Wales Joint work with T. Bielecki, M. Jeanblanc and K. Yousiph Seminar 26 September, 2007
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 informationNumerical 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 informationA new approach for scenario generation in risk management
A new approach for scenario generation in risk management Josef Teichmann TU Wien Vienna, March 2009 Scenario generators Scenarios of risk factors are needed for the daily risk analysis (1D and 10D ahead)
More informationNo-arbitrage theorem for multi-factor uncertain stock model with floating interest rate
Fuzzy Optim Decis Making 217 16:221 234 DOI 117/s17-16-9246-8 No-arbitrage theorem for multi-factor uncertain stock model with floating interest rate Xiaoyu Ji 1 Hua Ke 2 Published online: 17 May 216 Springer
More informationPDE Methods for the Maximum Drawdown
PDE Methods for the Maximum Drawdown Libor Pospisil, Jan Vecer Columbia University, Department of Statistics, New York, NY 127, USA April 1, 28 Abstract Maximum drawdown is a risk measure that plays an
More informationHedging of swaptions in a Lévy driven Heath-Jarrow-Morton framework
Hedging of swaptions in a Lévy driven Heath-Jarrow-Morton framework Kathrin Glau, Nele Vandaele, Michèle Vanmaele Bachelier Finance Society World Congress 2010 June 22-26, 2010 Nele Vandaele Hedging of
More informationImplementing the HJM model by Monte Carlo Simulation
Implementing the HJM model by Monte Carlo Simulation A CQF Project - 2010 June Cohort Bob Flagg Email: bob@calcworks.net January 14, 2011 Abstract We discuss an implementation of the Heath-Jarrow-Morton
More informationWorst-Case Value-at-Risk of Non-Linear Portfolios
Worst-Case Value-at-Risk of Non-Linear Portfolios Steve Zymler Daniel Kuhn Berç Rustem Department of Computing Imperial College London Portfolio Optimization Consider a market consisting of m assets. Optimal
More informationTechniques for Calculating the Efficient Frontier
Techniques for Calculating the Efficient Frontier Weerachart Kilenthong RIPED, UTCC c Kilenthong 2017 Tee (Riped) Introduction 1 / 43 Two Fund Theorem The Two-Fund Theorem states that we can reach any
More informationLibor Market Model Version 1.0
Libor Market Model Version.0 Introduction This plug-in implements the Libor Market Model (also know as BGM Model, from the authors Brace Gatarek Musiela). For a general reference on this model see [, [2
More informationMECHANICS OF MATERIALS
CHAPTER 7 Transformations MECHANICS OF MATERIALS Ferdinand P. Beer E. Russell Johnston, Jr. John T. DeWolf Lecture Notes: J. Walt Oler Texas Tech Universit of Stress and Strain 006 The McGraw-Hill Companies,
More informationCONTINUOUS-TIME MEAN VARIANCE EFFICIENCY: THE 80% RULE
Submitted to the Annals of Applied Probability CONTINUOUS-TIME MEAN VARIANCE EFFICIENCY: THE 8% RULE By Xun Li and Xun Yu Zhou National University of Singapore and The Chinese University of Hong Kong This
More informationHeterogeneous 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 informationELEMENTS 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 informationInformation, Interest Rates and Geometry
Information, Interest Rates and Geometry Dorje C. Brody Department of Mathematics, Imperial College London, London SW7 2AZ www.imperial.ac.uk/people/d.brody (Based on work in collaboration with Lane Hughston
More informationClassifying Solvency Capital Requirement Contribution of Collective Investments under Solvency II
Classifying Solvency Capital Requirement Contribution of Collective Investments under Solvency II Working Paper Series 2016-03 (01) SolvencyAnalytics.com March 2016 Classifying Solvency Capital Requirement
More informationRICCATI EQUATION AND VOLUME ESTIMATES
RICCATI EQUATION AND VOLUME ESTIMATES WERNER BALLMANN Contents 1. Introduction 1 2. Notions and notations 2 3. Distance functions and Riccati equation 4 4. Comparison theory for the Riccati equation 6
More information