The Higgs Particle Mass, Width and Couplings Seminar Particle Physics at the LHC
|
|
- Mervin Howard
- 5 years ago
- Views:
Transcription
1 The Higgs Particle, and Seminar Particle Physics at the LHC Freiburg, Albert-Ludwigs-Universität Freiburg Michael Schubert
2 Contents 2/54
3 introduction We found a Higgs boson! So... What now? 3/54
4 introduction what we can measure mass spin CP width couplings 4/54
5 introduction a short reminder about Higgs boson interactios production decay 5/54
6 introduction How do we separate? 6/54
7 significant channels Which Channels are suitable for the mass measurement? H Z 4l 7/54
8 But why? very good knowlegde of detector (e & γ) energy calibration (global & cell specific) behaviour of different layers material in front of the calorimeter controlled with > 7 million events (Z e + e, Z l + l γ, J/ψ e + e ) similar for µ, controlled with 15million events (Z µ + µ, J/ψ µ + µ ) separate for inner detector & muon spectrometer 8/54
9 this channel is good because very good mass resolution (2γ final state) smooth background can be determined from data 9/54
10 many many categories We separate into 10 categories: converted γ vs. unconverted γ different η regions different p Tt regions note p Tt = (p γ 1 T + pγ T 2 ) pγ T 1 pγ 2 T p γ 1 T pγ 2 T = projection orthogonal to thrust axis 10/54
11 the categories 11/54
12 results for illustration each channel is weighted with its signal to background ratio 12/54
13 systematics of course we have systematics to account for relative uncertainties in % 13/54
14 relults, again the ATLAS collaboration measures m H = ± 0.42(stat) ± 0.28(syst)GeV and a signal strength = cross section normalized to SM expectation µ = 1.29 ± /54
15 this channel is good because good signal to background ratio clean final state good mass resolution 15/54
16 the categories here the analysis is split into different final states 4µ 2e2µ 2µ2e 4e 16/54
17 results BDT for better signal/background separation BDT input variables: p T, η, D ZZ ( ) = log M sig 2 M ZZ 2 17/54
18 more results number of events, theory and measurement 18/54
19 likelihood ratios 19/54
20 result the ATLAS collaboration measures m H = ± 0.52(stat) ± 0.06(syst)GeV and a signal strength µ = /54
21 combination Can these be combined? Yes they can! m H = 1.47 ± 0.67(stat) ± 0.28(syst)GeV m H = ± 0.37(stat) ± 0.18(syst)GeV 21/54
22 combination plots 22/54
23 systematics 23/54
24 width Theoretical prediction for the Higgs boson width: 4MeV Experimental energy resolution: 2GeV but CMS did a thing 24/54
25 CMS and the of the Higgs Boson 25/54
26 What now? measure relative contributions to the width measurement of signal strengths and coupling strengths 26/54
27 the basics We have to make some basic assumptions: everything comes from the same single particle this particle is assumed to have zero decay width the particle is a CP-even scalar 27/54
28 reminder modified couplings are introduced 28/54
29 one example matrix element modified by κ 2 t but: W ± contribution interference κ 2 γ(κ F κ V ) = 1.59κ 2 V 0.66κ V κ F κ 2 F 29/54
30 SM only SM contributions only κ V = κ W = κ Z κ F = κ t = κ b = κ τ = κ g σ(gg H) BR(H γγ) κ2 F κ2 γ (κ F κ V ) 0.75κ 2 F +0.25κ2 V σ(qq qq H) BR(H γγ) κ2 V κ2 γ (κ F κ V ) 0.75κ 2 F +0.25κ2 V σ(gg H) BR(H ZZ ( ),H WW ( ) κ ) 2 F κ2 V 0.75κ 2 F +0.25κ2 V σ(qq qq H) BR(H ZZ ( ),H WW ( ) κ ) 2 V κ2 V 0.75κ 2 F +0.25κ2 V σ(qq qq κ H,VH) BR(H ττ,h b b) 2 V κ2 F 0.75κ 2 F +0.25κ2 V 30/54
31 SM only results κ F =1.15±0.08 κ V = /54
32 free total width variable no assumption on total width hide total width in ratios as free parameter κ VV = κ V κ V/κ H λ FV = κ F/κ V only ratios measurable 32/54
33 free total width functionalities σ(gg H) BR(H γγ) λ 2 FV κ2 VV κ2 γ(λ FV,1) σ(qq qq H) BR(H γγ) κ 2 VV κ2 γ(λ FV,1) σ(gg H) BR(H ZZ ( ),H WW ( ) ) λ 2 FV κ2 VV σ(qq qq H) BR(H ZZ ( ),H WW ( ) ) κ 2 VV σ(qq qq H,VH) BR(H ττ,h b b) κ 2 VV λ2 FV 33/54
34 free total width relults λ FV = κ VV = /54
35 custodial symmetry theory predicts same coupling scale factors for W & Z we test it (again no assumption on total width): κ ZZ =κ Z κ Z/κ H λ WZ =κ W/κ Z λ FZ =κ F/κ Z σ(gg H) BR(H γγ) λ 2 FZ κ2 ZZ κ2 γ (λ FZ,1) σ(qq qq H) BR(H γγ) κ 2 VBF (λ WZ,1)κ2 ZZ κ2 γ (λ FZ,1) σ(gg H) BR(H ZZ ( ) ) λ 2 FZ κ2 ZZ σ(qq qq H) BR(H ZZ ( ) ) κ 2 VBF (λ WZ,1)κ2 ZZ σ(gg H) BR(H WW ( ) ) λ 2 FZ κ2 ZZ λ2 WZ σ(qq qq H) BR(H WW ( ) ) κ 2 VBF (λ WZ,1)κ2 ZZ λ2 WZ σ(qq qq H,VH) BR(H ττ,h b b) κ 2 VBF (λ WZ,1)κ2 ZZ λ2 FZ 35/54
36 custodial symmetry results λ WZ = λ FZ [ 0.91, 0.63] [0.65,1.00] κ ZZ = /54
37 SM loop contents set everything to SM values effective couplings at loops (for γ & g): κ 2 g σ(gg H) BR(H γγ) κ2 γ 0.085κ 2 g κ2 γ σ(qq qq κ 2 γ H) BR(H γγ) 0.085κ 2 g κ2 γ σ(gg H) BR(H ZZ ( ),H WW ( ) κ 2 g ) 0.085κ 2 g κ2 γ σ(qq qq H) BR(H ZZ ( ),H WW ( ) 1 ) 0.085κ 2 g κ2 γ σ(qq qq 1 H,VH) BR(H ττ,h b b) 0.085κ 2 g κ2 γ /54
38 SM loop contents results κ g = κ γ = /54
39 BSM loop contents H invis possible BSM decays Γ H = κ 2 H (κ i) Γ SM H 1 BR inv,undet κ 2 g σ(gg H) BR(H γγ) κ2 γ 0.085κ 2 (1 BR inv,undet) g κ2 γ σ(qq qq κ 2 γ H) BR(H γγ) 0.085κ 2 (1 BR inv,undet) g κ2 γ σ(gg H) BR(H ZZ ( ),H WW ( ) κ 2 g ) 0.085κ 2 (1 BR inv,undet) g κ2 γ σ(qq qq H) BR(H ZZ ( ),H WW ( ) 1 ) 0.085κ 2 (1 BR inv,undet) g κ2 γ σ(qq qq 1 H,VH) BR(H ττ,h b b) 0.085κ 2 (1 BR inv,undet) g κ2 γ /54
40 BSM loop contents results κ g= κ γ= BR inv,undet = /54
41 In the end... 41/54
42 summary mass: m H = ± 0.41GeV signal strength: µ = 1.30 ± 0.20 all those couplings SM validated within 2σ 42/54
43 The End The End? 43/54
44 no end jet But wait there s more! CMS has results, too m H = (stat) (syst) 44/54
45 CMS coupling 45/54
46 references I [1] ATLAS Collaboration, Measurement of the Higgs boson mass from the and H ZZ ( ) 4l channels with the ATLAS detector using 25 fb 1 of pp collision data, arxiv: v1, 15. Jun 2014 [2] ATLAS Collaboration, Combined coupling measurement of the Higgs-like boson with the ATLAS detector using up to 25 fb 1 of proton-proton collision data, ATLAS-CONF , 13. Mar 2013 [3] ATLAS Collaboration, Updated coupling measurement of the Higgs-like boson with the ATLAS detector using up to 25 fb 1 of proton-proton collision data, ATLAS-CONF , 20. Mar /54
47 references II [4] ATLAS Collaboration, Measurement of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC, Physics Letters B 726 (2013) , Aug 2013 [5] CMS Collaboration, Measurement of the properties of the new boson with a mass near 125 GeV, CMS PAS HIG , 17. Apr 2013 [6] CMS Collaboration, Constraints on the Higgs boson width from off-shell production and decay of Z-boson pairs, arxiv: v1, 14. May /54
48 Thanks for your attention! Questions? Remarks? 48/54
49 backup 49/54
50 differences 50/54
51 free loop content allow BSM loop contents loose the sign information and get λ FV = κ F/κ V λ γv = κ γ/κ V κ VV = κ V κ V/κ H σ(gg H) BR(H γγ) λ 2 FV κ2 VV λ2 γv σ(qq qq H) BR(H γγ) κ 2 VV λ2 γv σ(gg H) BR(H ZZ ( ),H WW ( ) ) λ 2 FV κ2 VV σ(qq qq H) BR(H ZZ ( ),H WW ( ) ) κ 2 VV σ(qq qq H,VH) BR(H ττ,h b b) κ 2 VV λ2 FV 51/54
52 free loop contents result λ FV = λ γv = κ VV =1.15± /54
53 custodial symmetry BSM testing custodial symmetry again, with free loop content: κ ZZ =κ Z κ Z/κ H λ WZ =κ W/κ Z λ γz =κγ/κ Z λ FZ =κ F/κ Z σ(gg H) BR(H γγ) λ 2 FZ κ2 ZZ λ2 γz σ(qq qq H) BR(H γγ) κ 2 VBF (λ WZ,1)κ2 ZZ λ2 γz σ(gg H) BR(H ZZ ( ) ) λ 2 FZ κ2 ZZ σ(qq qq H) BR(H ZZ ( ) ) κ 2 VBF (λ WZ,1)κ2 ZZ σ(gg H) BR(H WW ( ) ) λ 2 FZ κ2 ZZ λ2 WZ σ(qq qq H) BR(H WW ( ) ) κ 2 VBF (λ WZ,1)κ2 ZZ λ2 WZ σ(qq qq H,VH) BR(H ττ,h b b) κ 2 VBF (λ WZ,1)κ2 ZZ λ2 FZ 53/54
54 custodial symmetry BSM results λ WZ = 0.80 ± 0.15 λ FZ = λ γz = 1.10 ± 0.18 κ ZZ = /54
Extracting Higgs Couplings from LHC and ILC Data
Extracting Higgs Couplings from LHC and ILC Data Sven Heinemeyer (CSIC, Santander) Madrid, 09/2014 1. Introduction 2. Our tool: HiggsSignals 3. Higgs bosons couplings from LHC data 4. Higgs boson couplings
More informationElectron Identification Based on Boosted Decision Trees
Electron Identification Based on Boosted Decision Trees Hai-Jun Yang University of Michigan, Ann Arbor (with T. Dai, X. Li, A. Wilson, B. Zhou) ATLAS Egamma Meeting October 2, 2008 Motivation Lepton (e,
More informationSearch for pairs of Higgs bosons in the bb τ + τ decay channel with the ATLAS detector
Search for pairs of Higgs bosons in the bb τ + τ decay channel with the ATLAS detector Petar Bokan Uppsala University, University of Göttingen supervisors: Arnaud Ferrari, Stan Lai Half-time PhD seminar
More informationStudy of the rare decays of B 0 and B 0 to muon pairs with the ATLAS detector at LHC Run- 1
Study of the rare decays of B 0 and B 0 to muon pairs s with the ATLAS detector at LHC Run- 1 Sandro Pales@ni (CERN) On behalf of the ATLAS Collabora@on Rencontres de Moriond EW interac@on and Unified
More informationBig Data & Machine Learning in HEP
Big Data & Machine Learning in HEP Mike Williams Department of Physics & Laboratory for Nuclear Science Massachusetts Institute of Technology March 17, 216 The Large Hadron Collider Outline { Big Data
More informationHiggs Couplings à la HXSWG
Higgs Couplings à la HXSWG Giampiero PASSARINO Dipartimento di Fisica Teorica, Università di Torino, Italy INFN, Sezione di Torino, Italy HC2012 Workshop, Tokyo, 18 20 November 2012 Outline Higgs couplings
More informationHiggs coupling measurements with ATLAS
igg coupling meaurement with ATLAS Richard Mudd Univerity of Birmingham EP Seminar, Birmingham th November July of 39 igg Mechanim SU() L U() Y decribe electroweak ector in term of male gauge boon In the
More informationProbing Higgs self-couplings at Future Colliders
Probing iggs self-couplings at Future Colliders Ambresh Shivaji IISER Mohali, Punjab, INDIA Mini-Workshop: Theory Physics Opportunities and Advanced Tools IAS (KUST), January -, 29 Ambresh Shivaji (IISER
More informationDesigning Price Contracts for Boundedly Rational Customers: Does the Number of Block Matter?
Designing Price Contracts for Boundedly ational Customers: Does the Number of Block Matter? Teck H. Ho University of California, Berkeley Forthcoming, Marketing Science Coauthor: Noah Lim, University of
More informationCurrent Status of e-id based on BDT Algorithm
Current Status of e-id based on BDT Algorithm Hai-Jun Yang University of Michigan (with X. Li, T. Dai, A. Wilson, B. Zhou) BNL Analysis Jamboree March 19, 2009 Goals ATLAS default electron-id (IsEM) has
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 informationAppendix to Wage bargaining and monopsony by T. Falch and B. Strøm:
Appendix to age bargaining and monopsony by T. Falch and B. Strøm: The solution of the model e assume a right to manage model where the employment is determined by the firm after the wage bargaining. In
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 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 informationB-tagging based on Boosted Decision Trees
B-tagging based on Boosted Decision Trees Haijun Yang University of Michigan (with Xuefei Li and Bing Zhou) ATLAS B-tagging Meeting CERN, July 7, 2009 1 Introduction Outline Boosted Decision Trees B-tagging
More informationThe latest CALICE activities
The latest CALICE activities Fabrizio Salvatore Royal Holloway University of London HEP seminar, RHUL, 22 nd January 2008 Rationale for a Linear Collider The LHC start up next year is expected to mark
More informationLook Elsewhere Effect
Eilam Gross and Ofer Vitells Weizmann Institute of Science 1 Eilam Gross & Ofer Vitells, Banff A Plausible Thumb Rule for a Trial # Eilam Gross and Ofer Vitells Weizmann Institute of Science 2 Eilam Gross
More informationLHC DATA! Current status of LHC and ATLAS and prospects for
LHC DATA! Current status of LHC and ATLAS and prospects for 2010-2011 Andy Haas SLAC West Coast LHC Theory Meeting SCIPP, UC Santa Cruz May 21, -2010 Andy Haas 5/21/2010 Slide 1 Overview LHC commissioning
More informationThe CAPM Strikes Back? An Investment Model with Disasters
The CAPM Strikes Back? An Investment Model with Disasters Hang Bai 1 Kewei Hou 1 Howard Kung 2 Lu Zhang 3 1 The Ohio State University 2 London Business School 3 The Ohio State University and NBER Federal
More informationDAMA Slides/Graphics
DAMA Slides/Graphics 000608 Rick Gaitskell Center for Particle Astrophysics UC Berkeley source at http://cdms.berkeley.edu/gaitskell/ Gaitskell Best fit to Ann Mod data alone Best Fit Minimum DAMA NaI/1-4
More informationValuing volatility and variance swaps for a non-gaussian Ornstein-Uhlenbeck stochastic volatility model
Valuing volatility and variance swaps for a non-gaussian Ornstein-Uhlenbeck stochastic volatility model 1(23) Valuing volatility and variance swaps for a non-gaussian Ornstein-Uhlenbeck stochastic volatility
More informationDuangporn Jearkpaporn, Connie M. Borror Douglas C. Montgomery and George C. Runger Arizona State University Tempe, AZ
Process Monitoring for Correlated Gamma Distributed Data Using Generalized Linear Model Based Control Charts Duangporn Jearkpaporn, Connie M. Borror Douglas C. Montgomery and George C. Runger Arizona State
More informationTowards the LHC Start
Science @ KIP Heidelberg, June 5, 2008 Towards the LHC Start What, why, how and a bit of daily life. Martin Wessels KIP, University of Heidelberg LH C est pas sorcier The Guardian "Particle physics is
More informationProf. B V S Viswanadham, Department of Civil Engineering, IIT Bombay
57 Module 4: Lecture 8 on Stress-strain relationship and Shear strength of soils Contents Stress state, Mohr s circle analysis and Pole, Principal stressspace, Stress pathsin p-q space; Mohr-Coulomb failure
More informationMeasuring Financial Risk using Extreme Value Theory: evidence from Pakistan
Measuring Financial Risk using Extreme Value Theory: evidence from Pakistan Dr. Abdul Qayyum and Faisal Nawaz Abstract The purpose of the paper is to show some methods of extreme value theory through analysis
More informationSimulation of Extreme Events in the Presence of Spatial Dependence
Simulation of Extreme Events in the Presence of Spatial Dependence Nicholas Beck Bouchra Nasri Fateh Chebana Marie-Pier Côté Juliana Schulz Jean-François Plante Martin Durocher Marie-Hélène Toupin Jean-François
More informationASTIN Colloquium Understanding Split Credibility. Ira Robbin, PhD AVP and Senior Pricing Actuary Endurance US Insurance Operations
ASTIN Colloquium Understanding Split Credibility Ira Robbin, PhD AVP and Senior Pricing Actuary Endurance US Insurance Operations Ground Rules Follow US Anti-trust Laws, s il vous plait! Violators will
More informationThe Aggregate Implications of Regional Business Cycles
The Aggregate Implications of Regional Business Cycles Martin Beraja Erik Hurst Juan Ospina University of Chicago University of Chicago University of Chicago Fall 2017 This Paper Can we use cross-sectional
More informationModeling dynamic diurnal patterns in high frequency financial data
Modeling dynamic diurnal patterns in high frequency financial data Ryoko Ito 1 Faculty of Economics, Cambridge University Email: ri239@cam.ac.uk Website: www.itoryoko.com This paper: Cambridge Working
More informationSANE Analysis Update
SANE Analysis Update Artificial Neural Networks in Analyzing BETA Whitney Armstrong Temple University Physics Department January 23, 2010 Introduction Introduction 1 Spin Asymmetries of the Nucleon Experiment
More informationThe Role of Education Signaling in Explaining the Growth of College Wage Premium
The Role of Education Signaling in Explaining the Growth of College Wage Premium Yu Zheng City University of Hong Kong European University Institute ERF Workshop on Macroeconomics, Istanbul September,
More informationDiscussion of Debt Constraints and Employment by Kehoe, Midrigan, and Pastorino
Discussion of Debt Constraints and Employment by Kehoe, Midrigan, and Pastorino Robert E. Hall Hoover Institution and Department of Economics Stanford University National Bureau of Economic Research EF&G
More informationDynamic Portfolio Choice with Frictions
Dynamic Portfolio Choice with Frictions Nicolae Gârleanu UC Berkeley, CEPR, and NBER Lasse H. Pedersen NYU, Copenhagen Business School, AQR, CEPR, and NBER December 2014 Gârleanu and Pedersen Dynamic Portfolio
More informationGeneralized Additive Modelling for Sample Extremes: An Environmental Example
Generalized Additive Modelling for Sample Extremes: An Environmental Example V. Chavez-Demoulin Department of Mathematics Swiss Federal Institute of Technology Tokyo, March 2007 Changes in extremes? Likely
More informationGeometry Factors pt. II, Error Analysis, and Optimizations
Geometry Factors pt. II, Error Analysis, and Optimizations Christopher Coppola November 11, 2014 Simulation Method Error Analysis Optimizations Simulation Goals -Calculate geometry factors -Optimize pressure
More informationModelling Environmental Extremes
19th TIES Conference, Kelowna, British Columbia 8th June 2008 Topics for the day 1. Classical models and threshold models 2. Dependence and non stationarity 3. R session: weather extremes 4. Multivariate
More informationModelling Environmental Extremes
19th TIES Conference, Kelowna, British Columbia 8th June 2008 Topics for the day 1. Classical models and threshold models 2. Dependence and non stationarity 3. R session: weather extremes 4. Multivariate
More informationA Policy Model for Analyzing Macroprudential and Monetary Policies
A Policy Model for Analyzing Macroprudential and Monetary Policies Sami Alpanda Gino Cateau Cesaire Meh Bank of Canada November 2013 Alpanda, Cateau, Meh (Bank of Canada) ()Macroprudential - Monetary Policy
More informationN a.. n o s.. c a l e.. S.. y.. s t e.. m.. s.. M.. M.. T.. A bullet.. I SSN : hyphen 3290 \ centerline
N S M M SSN : 99 39 S N D O : 4 7 8 S 8 N M N SSN : S 99 39 M M SSN : 99-39 V 4 S D O : 4 7 8 / 8 N M M V M S 4 D O : 4 7 8 / M 8 N M M V W - 4 F X - * C D J 3 S S M - W F X À V C D J 3 S - H 8 D 93 B
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 informationME3620. Theory of Engineering Experimentation. Spring Chapter III. Random Variables and Probability Distributions.
ME3620 Theory of Engineering Experimentation Chapter III. Random Variables and Probability Distributions Chapter III 1 3.2 Random Variables In an experiment, a measurement is usually denoted by a variable
More informationHydrology 4410 Class 29. In Class Notes & Exercises Mar 27, 2013
Hydrology 4410 Class 29 In Class Notes & Exercises Mar 27, 2013 Log Normal Distribution We will not work an example in class. The procedure is exactly the same as in the normal distribution, but first
More information14.05 Lecture Notes. Endogenous Growth
14.05 Lecture Notes Endogenous Growth George-Marios Angeletos MIT Department of Economics April 3, 2013 1 George-Marios Angeletos 1 The Simple AK Model In this section we consider the simplest version
More informationOPTIMAL STOCHASTIC DESIGN FOR MULTI-PARAMETER ESTIMATION PROBLEMS
24 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) OPTIMAL STOCHASTIC DESIGN FOR MULTI-PARAMETER ESTIMATION PROBLEMS HamzaSoganci,,SinanGezici,andOrhanArikan BilkentUniversity,DepartmentofElectricalandElectronicsEngineering,68,Ankara,Turkey
More informationModeling Obesity and S&P500 Using Normal Inverse Gaussian
Modeling Obesity and S&P500 Using Normal Inverse Gaussian Presented by Keith Resendes and Jorge Fernandes University of Massachusetts, Dartmouth August 16, 2012 Diabetes and Obesity Data Data obtained
More informationNoise, sign problems, and statistics arxiv: [hep-lat] Michael Endres, D.K., Jong-Wan Lee, Amy Nicholson...
Listening to NOIS E TYPE THE THREE WORDS Listening to NOISE Noise, sign problems, and statistics arxiv:1106.0073 [hep-lat] Michael Endres, D.K., Jong-Wan Lee, Amy Nicholson...& work in progress Physics
More information2.1 Mean-variance Analysis: Single-period Model
Chapter Portfolio Selection The theory of option pricing is a theory of deterministic returns: we hedge our option with the underlying to eliminate risk, and our resulting risk-free portfolio then earns
More informationDisaster risk and its implications for asset pricing Online appendix
Disaster risk and its implications for asset pricing Online appendix Jerry Tsai University of Oxford Jessica A. Wachter University of Pennsylvania December 12, 2014 and NBER A The iid model This section
More informationEstimating Macroeconomic Models of Financial Crises: An Endogenous Regime-Switching Approach
Estimating Macroeconomic Models of Financial Crises: An Endogenous Regime-Switching Approach Gianluca Benigno 1 Andrew Foerster 2 Christopher Otrok 3 Alessandro Rebucci 4 1 London School of Economics and
More informationCredit Constraints, Technology Choice and Exports - A Firm Level Study for Latin American Countries
Credit Constraints, Technology Choice and Exports - A Firm Level Study for Latin American Countries December 17, 2013 Research Motivation Trade liberalization benefits are not fully realized by firms in
More informationContinuous Time Bewley Models
1 / 18 Continuous Time Bewley Models DEEQA Quantitative Macro Sang Yoon (Tim) Lee Toulouse School of Economics October 24, 2016 2 / 18 Today Aiyagari with Poisson wage process : Based on http://www.princeton.edu/~moll/hact.pdf,
More informationCEO Attributes, Compensation, and Firm Value: Evidence from a Structural Estimation. Internet Appendix
CEO Attributes, Compensation, and Firm Value: Evidence from a Structural Estimation Internet Appendix A. Participation constraint In evaluating when the participation constraint binds, we consider three
More informationA NEW APPROACH TO MERTON MODEL DEFAULT AND PREDICTIVE ANALYTICS WITH APPLICATIONS TO RECESSION ECONOMICS TOMMY LEWIS
A NEW APPROACH TO MERTON MODEL DEFAULT AND PREDICTIVE ANALYTICS WITH APPLICATIONS TO RECESSION ECONOMICS TOMMY LEWIS BACKGROUND/MOTIVATION Default risk is the uncertainty surrounding how likely it is that
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 informationResearch Article On the Classification of Lattices Over Q( 3) Which Are Even Unimodular Z-Lattices of Rank 32
International Mathematics and Mathematical Sciences Volume 013, Article ID 837080, 4 pages http://dx.doi.org/10.1155/013/837080 Research Article On the Classification of Lattices Over Q( 3) Which Are Even
More informationObjective Bayesian Analysis for Heteroscedastic Regression
Analysis for Heteroscedastic Regression & Esther Salazar Universidade Federal do Rio de Janeiro Colóquio Inter-institucional: Modelos Estocásticos e Aplicações 2009 Collaborators: Marco Ferreira and Thais
More informationOn modelling of electricity spot price
, Rüdiger Kiesel and Fred Espen Benth Institute of Energy Trading and Financial Services University of Duisburg-Essen Centre of Mathematics for Applications, University of Oslo 25. August 2010 Introduction
More informationPricing Variance Swaps on Time-Changed Lévy Processes
Pricing Variance Swaps on Time-Changed Lévy Processes ICBI Global Derivatives Volatility and Correlation Summit April 27, 2009 Peter Carr Bloomberg/ NYU Courant pcarr4@bloomberg.com Joint with Roger Lee
More informationASSET PRICING WITH LIMITED RISK SHARING AND HETEROGENOUS AGENTS
ASSET PRICING WITH LIMITED RISK SHARING AND HETEROGENOUS AGENTS Francisco Gomes and Alexander Michaelides Roine Vestman, New York University November 27, 2007 OVERVIEW OF THE PAPER The aim of the paper
More informationCommon risk factors in currency markets
Common risk factors in currency markets by Hanno Lustig, Nick Roussanov and Adrien Verdelhan Discussion by Fabio Fornari Frankfurt am Main, 18 June 2009 External Developments Division Common risk factors
More informationTHE ENERGY EFFICIENCY OF THE ERGODIC FADING RELAY CHANNEL
7th European Signal Processing Conference (EUSIPCO 009) Glasgow, Scotland, August 4-8, 009 THE ENERGY EFFICIENCY OF THE ERGODIC FADING RELAY CHANNEL Jesús Gómez-Vilardebó Centre Tecnològic de Telecomunicacions
More informationFinal 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 informationEconomics 883: The Basic Diffusive Model, Jumps, Variance Measures. George Tauchen. Economics 883FS Spring 2015
Economics 883: The Basic Diffusive Model, Jumps, Variance Measures George Tauchen Economics 883FS Spring 2015 Main Points 1. The Continuous Time Model, Theory and Simulation 2. Observed Data, Plotting
More informationGenetics and/of basket options
Genetics and/of basket options Wolfgang Karl Härdle Elena Silyakova Ladislaus von Bortkiewicz Chair of Statistics Humboldt-Universität zu Berlin http://lvb.wiwi.hu-berlin.de Motivation 1-1 Basket derivatives
More informationA Markov-Switching Multi-Fractal Inter-Trade Duration Model, with Application to U.S. Equities
A Markov-Switching Multi-Fractal Inter-Trade Duration Model, with Application to U.S. Equities Fei Chen (HUST) Francis X. Diebold (UPenn) Frank Schorfheide (UPenn) December 14, 2012 1 / 39 Big Data Are
More informationPrice Impact and Optimal Execution Strategy
OXFORD MAN INSTITUE, UNIVERSITY OF OXFORD SUMMER RESEARCH PROJECT Price Impact and Optimal Execution Strategy Bingqing Liu Supervised by Stephen Roberts and Dieter Hendricks Abstract Price impact refers
More informationDynamic Wrong-Way Risk in CVA Pricing
Dynamic Wrong-Way Risk in CVA Pricing Yeying Gu Current revision: Jan 15, 2017. Abstract Wrong-way risk is a fundamental component of derivative valuation that was largely neglected prior to the 2008 financial
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 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 informationRISK-NEUTRAL VALUATION AND STATE SPACE FRAMEWORK. JEL Codes: C51, C61, C63, and G13
RISK-NEUTRAL VALUATION AND STATE SPACE FRAMEWORK JEL Codes: C51, C61, C63, and G13 Dr. Ramaprasad Bhar School of Banking and Finance The University of New South Wales Sydney 2052, AUSTRALIA Fax. +61 2
More informationRate region boundary of the Z-interference. channel with improper signaling
Rate region boundary of the Z-interference channel with improper signaling Christian Lameiro, Member, IEEE, Ignacio Santamaría, Senior Member, IEEE, and Peter J. Schreier, Senior Member, IEEE arxiv:605.040v
More information(RP13) Efficient numerical methods on high-performance computing platforms for the underlying financial models: Series Solution and Option Pricing
(RP13) Efficient numerical methods on high-performance computing platforms for the underlying financial models: Series Solution and Option Pricing Jun Hu Tampere University of Technology Final conference
More informationThe Lost Generation of the Great Recession
The Lost Generation of the Great Recession Sewon Hur University of Pittsburgh January 21, 2016 Introduction What are the distributional consequences of the Great Recession? Introduction What are the distributional
More informationGeostatistical Inference under Preferential Sampling
Geostatistical Inference under Preferential Sampling Marie Ozanne and Justin Strait Diggle, Menezes, and Su, 2010 October 12, 2015 Marie Ozanne and Justin Strait Preferential Sampling October 12, 2015
More informationSticky Wages and Financial Frictions
Sticky Wages and Financial Frictions Alex Clymo 1 1 University of Essex EEA-ESEM, August 2017 1 / 18 Introduction Recent work highlights that new wages more flexible than old: Pissarides (2009), Haefke,
More informationarxiv:cond-mat/ v2 [cond-mat.str-el] 5 Nov 2002
arxiv:cond-mat/0211050v2 [cond-mat.str-el] 5 Nov 2002 Comparison between the probability distribution of returns in the Heston model and empirical data for stock indices A. Christian Silva, Victor M. Yakovenko
More informationSparse Wavelet Methods for Option Pricing under Lévy Stochastic Volatility models
Sparse Wavelet Methods for Option Pricing under Lévy Stochastic Volatility models Norbert Hilber Seminar of Applied Mathematics ETH Zürich Workshop on Financial Modeling with Jump Processes p. 1/18 Outline
More information12 GeV CEBAF Upgrade. Risk Management Plan
12 GeV CEBAF Upgrade Risk Management Plan May 29, 2007 12 GeV CEBAF Upgrade Risk Management Plan 1 Apr 05 ISSUE DATE PAGES AFFECTED DESCRIPTION Original CD-2 4/01/05 5/29/07 All All General update to maintain
More informationSession 5. Predictive Modeling in Life Insurance
SOA Predictive Analytics Seminar Hong Kong 29 Aug. 2018 Hong Kong Session 5 Predictive Modeling in Life Insurance Jingyi Zhang, Ph.D Predictive Modeling in Life Insurance JINGYI ZHANG PhD Scientist Global
More informationPrincipal Component Analysis of the Volatility Smiles and Skews. Motivation
Principal Component Analysis of the Volatility Smiles and Skews Professor Carol Alexander Chair of Risk Management ISMA Centre University of Reading www.ismacentre.rdg.ac.uk 1 Motivation Implied volatilities
More informationModern Methods of Data Analysis - SS 2009
Modern Methods of Data Analysis Lecture II (7.04.09) Contents: Characterize data samples Characterize distributions Correlations, covariance Reminder: Average of a Sample arithmetic mean of data set: weighted
More informationInternational Trade and Income Differences
International Trade and Income Differences By Michael E. Waugh AER (Dec. 2010) Content 1. Motivation 2. The theoretical model 3. Estimation strategy and data 4. Results 5. Counterfactual simulations 6.
More informationHousehold Debt, Financial Intermediation, and Monetary Policy
Household Debt, Financial Intermediation, and Monetary Policy Shutao Cao 1 Yahong Zhang 2 1 Bank of Canada 2 Western University October 21, 2014 Motivation The US experience suggests that the collapse
More informationarxiv: v1 [math.pr] 15 Dec 2011
Parameter Estimation of Fiber Lay down in Nonwoven Production An Occupation Time Approach Wolfgang Bock, Thomas Götz, Uditha Prabhath Liyanage arxiv:2.355v [math.pr] 5 Dec 2 Dept. of Mathematics, University
More informationLecture 2: Stochastic Discount Factor
Lecture 2: Stochastic Discount Factor Simon Gilchrist Boston Univerity and NBER EC 745 Fall, 2013 Stochastic Discount Factor (SDF) A stochastic discount factor is a stochastic process {M t,t+s } such that
More informationLecture 4: Model-Free Prediction
Lecture 4: Model-Free Prediction David Silver Outline 1 Introduction 2 Monte-Carlo Learning 3 Temporal-Difference Learning 4 TD(λ) Introduction Model-Free Reinforcement Learning Last lecture: Planning
More informationImproved Inference for Signal Discovery Under Exceptionally Low False Positive Error Rates
Improved Inference for Signal Discovery Under Exceptionally Low False Positive Error Rates (to appear in Journal of Instrumentation) Igor Volobouev & Alex Trindade Dept. of Physics & Astronomy, Texas Tech
More informationEmpirical Test of Affine Stochastic Discount Factor Model of Currency Pricing. Abstract
Empirical Test of Affine Stochastic Discount Factor Model of Currency Pricing Alex Lebedinsky Western Kentucky University Abstract In this note, I conduct an empirical investigation of the affine stochastic
More informationReinforcement Learning
Reinforcement Learning n-step bootstrapping Daniel Hennes 12.06.2017 University Stuttgart - IPVS - Machine Learning & Robotics 1 n-step bootstrapping Unifying Monte Carlo and TD n-step TD n-step Sarsa
More informationUnconventional Monetary Policy
Unconventional Monetary Policy Mark Gertler (based on joint work with Peter Karadi) NYU October 29 Old Macro Analyzes pre versus post 1984:Q4. 1 New Macro Analyzes pre versus post August 27 Post August
More informationExtended 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 informationDepartment Heads Meeting March 10, David B. MacFarlane
Department Heads Meeting March 10, 2011 David B. MacFarlane Topics News LDRD proposals: PPA internal deadline: March 18 with 1-pager Lab-wide: April 15 with full proposal Field Budget Request for FY2012
More information4 Reinforcement Learning Basic Algorithms
Learning in Complex Systems Spring 2011 Lecture Notes Nahum Shimkin 4 Reinforcement Learning Basic Algorithms 4.1 Introduction RL methods essentially deal with the solution of (optimal) control problems
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 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 informationPartitioned 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 informationState Dependency of Monetary Policy: The Refinancing Channel
State Dependency of Monetary Policy: The Refinancing Channel Martin Eichenbaum, Sergio Rebelo, and Arlene Wong May 2018 Motivation In the US, bulk of household borrowing is in fixed rate mortgages with
More informationChapter 6 Forecasting Volatility using Stochastic Volatility Model
Chapter 6 Forecasting Volatility using Stochastic Volatility Model Chapter 6 Forecasting Volatility using SV Model In this chapter, the empirical performance of GARCH(1,1), GARCH-KF and SV models from
More informationDifferences between NPV, Decision Trees, and Real Options. ACTEX 2010 Section I - 29
Differences between NPV, Decision Trees, and Real Options ACTEX 2010 Section I - 29 1. NPV is flawed because it systematically undervalues everything due to simplifying assumptions a. Ignores options to
More informationFrom LEP to the Large Hadron CERN Spring School, LNF, Frascati, 15 May 2006
From LEP to the Large Hadron Collider @ CERN Spring School, LNF, Frascati, 15 May 2006 Luciano Maiani, Universita di Roma La Sapienza (a real photo!!!) A revolutionary tool: Collision Rings beam-beam collisions
More information