Hoek-Brown model for description of short and long term be. rocks

Size: px
Start display at page:

Download "Hoek-Brown model for description of short and long term be. rocks"

Transcription

1 Hoek-Brown model for description of short and long term behavior of rocks Andrzej Truty CUT & ZACE Services

2 Hoek s web page Here we can find lot of useful information on basic H-B model and its calibration. Hoek's web page: ww.rockscience.com

3 General remarks Strain decomposition: dε = dε e + }{{} dε p short term + }{{} dε vp long term Short term behavior of rocks is described by an elasto-plastic H-B model that may include <hardening><softening><variable dilatancy> dε p Long term behavior is described by an additional Lemaitre type of the creep law dε vp Actual version (till ZSoil 2012) of H-B model approximates the true H-B (ZSoil 2012) The new version (ZSoil 2013) is based on the 2002 edition of H-B model and can be calibrated using given GSI index Notation: all stresses are effective and positive in compression

4 Experimental evidence: triaxial test r res R LINEAR PRE PEA AK POST PEA AK P RESIDUA AL 1 Strain softening and strong dilatancy are well visible for low confining pressures σ 3 For high confining pressures both phenomena dissappear under triaxial test conditions

5 Handling strain softening Strain softening requires certain kind of regularization to avoid mesh dependency Nonlocal approach will be used (same as for M-C model) It will be possible to use relatively large elements and still cancel parasitic mesh dependency

6 GSI index GSI - Geological strength index it considers shape of intact rock layers and joints Rough classification 1 Massive Brittle Rocks (70 < GSI < 90) 1 2 Jointed Strong Rocks (50 < GSI < 65) 3 Jointed Intermediate Rocks (40 < GSI < 50) 4 Very Weak Rocks GSI < 30 1 J.J.Crowder and W.F. Bowden. Review of post-peak parameters...

7 Hoek-Brown (2002) edition Yield surface: f (σ 1, σ 3 ) = σ 1 σ 3 σ ci ( m b σ 3 σ ci + s σ ci - intact rock compressive strength other parameters ( are usually ) related to the GSI index GSI 100 m b = m i exp D ) a a = (exp ( GSI/15) exp ( 20/3)) ( 6 ) GSI 100 s = exp 9 3 D m b is reduced value of m i one (for intact rock) D = factor that depends on degree of disturbance (1.0 means highly disturbed and 0.0 undisturbed)

8 How parameters a and s depend on GSI and D D=0.0 D=0.2 D=0.4 D=0.6 D=0.8 D=1.0 a GSI s GSI f t = s σ ci m b f c = σ ci s a

9 Dilatancy: ψ = ψ (σ 3, γ p ) In the basic setup we may assume that ψ=const. (bad choice) Triaxial tests indicate that for σ 3 = σ ψ dilatancy is neglible 90 o This value can be strain dependent f t 0 3 NB. σ ψ is a material parameter

10 Dilatancy: ψ = ψ (σ 3, γ p ) ψ (σ 3 = 0) = ψ o f γ ψ (γp ) 1 f r res 0 p

11 Hardening/Softening: how to include it? Following the disscussion in the recent publicity on H-B, parameters selected for the softening are (same thing for the pre-peak hardening) 1 m b (γ p ) 2 s(γ p ) 3 a(γ p ) To include hardening and softening two values of the accumulated plastic deviatoric strain are defined 1 γ r - deviatoric strain at peak on q ε triax curve 2 γ res - deviatoric strain at residual state on q ε triax curve NB. Both hardening and softening are optional (can easily be excluded); standard H-B parameters m b, s, a correspond to the peak quantities

12 Hardening/Softening: how to include it? Parameter Linear Pre-peak Post-peak m b mb o mb r mb res s s o s r s res a a o a r a res In the linear region the initial H-B surface is defined by m o b, so, a o In the pre-peak region linear interpolation is used for pairs of m o b mr b, so s r, a o a r In the post-peak region 3-rd polynomial is used to interpolate pairs of mb r mres b, sr s res, a r a res

13 User interface

14 Customizing H-B model NB. Yellow cells should be defined by the user

15 Lemaitre creep law dε vp = ( A o exp B ) exp (b γ ) R T ( g(σ) = exp σ σ ) o σ ref ( q qo g(σ) σ ref ) n ( ) ε vp m q eq σ m = m(γ ) = 2 (m o m 1 ) γ 3 3 (m o m 1 ) γ 2 + m o γ p γ if γ = p γ r γ r 1 if γ p > γ r This law does not produce any dilatancy

16 Example: creep test 1 kn/m2 X LTF (t) ɛ xx ɛ yy kn/m2 ɛ[ ] t[h] Initial stresses σ o = { 1000, 1000, 0, 1000}

17 Example: relaxation test Uy=1.0 * LTF (t) 1000 σ xx kn/m2 σ[kpa] t[h] Initial stresses σ o = { 1000, 1000, 0, 1000}

18 Example: triaxial compression (with pre-peak hardening) Triaxial compression with 3 confining stresses σ 3 = 0, 3, 6 MPa σ 3 =0 MPa σ 3 =3 MPa σ 3 =6 MPa 40 σ 1 σ 3 [kpa] ɛ[ ]

19 Example: triaxial compression (without pre-peak hardening) Triaxial compression with 3 confining stresses σ 3 = 0, 3, 6 MPa σ 3 =0 MPa σ 3 =3 MPa σ 3 =6 MPa 40 σ 1 σ 3 [kpa] ɛ[ ]

20 Stability analysis using H-B model Here we will use notion of Stress Level SF = SL f (σ 1, σ 3 ) = σ 1 σ 3 1 ( SF σ σ 3 ci m b + s σ ci f (σ 1, σ 3 ) = σ 1 σ 3 [ (SF 1 ) 1 a ) a ] a ( ) a σ 3 σci m b + s σ ci Modified form of H-B with m b and s parameters σ 3 f (σ 1, σ 3 ) = σ 1 σ 3 σ ci (mb + s σ ci 1 mb = m b SF a 1 s = s SF a ) a

21 Stability: moderate slope M C SF = 1.38 Const. =10 o H B SF = 1.58 Const. =10 o

22 Conclusions H-B model should improve predictions for rocks Model can easily be customized to basic/advanced version Model can be used for predictions of short and long term behavior of rocks Finally...it is the true H-B version...

Prof. B V S Viswanadham, Department of Civil Engineering, IIT Bombay

Prof. 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 information

State of Stress in Three Dimensions

State of Stress in Three Dimensions State of Stress in Three Dimensions Theories of failure Introduction: Due to large numbers of examples of compound stresses met with in engineering practice, the cause of failure or permanent set under

More information

Prof. B V S Viswanadham, Department of Civil Engineering, IIT Bombay

Prof. B V S Viswanadham, Department of Civil Engineering, IIT Bombay 46 Module 3: Lecture - 8 on Compressibility and Consolidation Contents Stresses in soil from surface loads; Terzaghi s 1-D consolidation theory; Application in different boundary conditions; Ramp loading;

More information

OHIO DEPARTMENT OF TRANSPORTATION. PROPOSAL for the GEOTECHNICAL EXPLORATION <COUNTY-ROUTE-SECTION> <PID>

OHIO DEPARTMENT OF TRANSPORTATION. PROPOSAL for the GEOTECHNICAL EXPLORATION <COUNTY-ROUTE-SECTION> <PID> OHIO DEPARTMENT OF TRANSPORTATION OFFICE OF GEOTECHNICAL ENGINEERING PROPOSAL for the GEOTECHNICAL EXPLORATION Instructions: Enter data in the shaded cells only. (Enter state route number, project description,county,

More information

Geology 3120 Powerpoint notes available online at:

Geology 3120 Powerpoint notes available online at: Geology 3120 Powerpoint notes available online at: http://www.colorado/edu/geolsci/courses/geol3120 Geology 3120 - The Mohr Stress Diagram σ s 0 2Θ σ n Stress Space Outline Setting up the Problem The Mohr

More information

Chapter (9) Sheet Pile Walls

Chapter (9) Sheet Pile Walls Chapter (9) Introduction Sheet piles are a temporary structures used to retain a soil or water for a specific period of time, to build a structure in the other side of this wall. For example; if we want

More information

Previously, when making inferences about the population mean, μ, we were assuming the following simple conditions:

Previously, when making inferences about the population mean, μ, we were assuming the following simple conditions: Chapter 17 Inference about a Population Mean Conditions for inference Previously, when making inferences about the population mean, μ, we were assuming the following simple conditions: (1) Our data (observations)

More information

ESG Yield Curve Calibration. User Guide

ESG Yield Curve Calibration. User Guide ESG Yield Curve Calibration User Guide CONTENT 1 Introduction... 3 2 Installation... 3 3 Demo version and Activation... 5 4 Using the application... 6 4.1 Main Menu bar... 6 4.2 Inputs... 7 4.3 Outputs...

More information

Life Quality Index for Assessing Risk Acceptance in Geotechnical Engineering

Life Quality Index for Assessing Risk Acceptance in Geotechnical Engineering ISGSR 2011 - Vogt, Schuppener, Straub & Bräu (eds) - 2011 Bundesanstalt für Wasserbau ISBN 978-3-939230-01-4 Life Quality Index for Assessing Risk Acceptance in Geotechnical Engineering D. Straub Engineering

More information

Spline Methods for Extracting Interest Rate Curves from Coupon Bond Prices

Spline Methods for Extracting Interest Rate Curves from Coupon Bond Prices Spline Methods for Extracting Interest Rate Curves from Coupon Bond Prices Daniel F. Waggoner Federal Reserve Bank of Atlanta Working Paper 97-0 November 997 Abstract: Cubic splines have long been used

More information

The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay. Solutions to Final Exam

The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay. Solutions to Final Exam The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (40 points) Answer briefly the following questions. 1. Consider

More information

Chapter 7 A Multi-Market Approach to Multi-User Allocation

Chapter 7 A Multi-Market Approach to Multi-User Allocation 9 Chapter 7 A Multi-Market Approach to Multi-User Allocation A primary limitation of the spot market approach (described in chapter 6) for multi-user allocation is the inability to provide resource guarantees.

More information

Subsoil Exploration. Foundation Engineering. Solution Givens:

Subsoil Exploration. Foundation Engineering. Solution Givens: Problems: 1. Site investigation is to be made for a structure of 100m length and 70m width. The soil profile is shown below, if the structure is subjected to 200 KN/m 2 what is the approximate depth of

More information

A SUMMARY OF OUR APPROACHES TO THE SABR MODEL

A SUMMARY OF OUR APPROACHES TO THE SABR MODEL Contents 1 The need for a stochastic volatility model 1 2 Building the model 2 3 Calibrating the model 2 4 SABR in the risk process 5 A SUMMARY OF OUR APPROACHES TO THE SABR MODEL Financial Modelling Agency

More information

Quadrant marked mesh patterns in 123-avoiding permutations

Quadrant marked mesh patterns in 123-avoiding permutations Quadrant marked mesh patterns in 23-avoiding permutations Dun Qiu Department of Mathematics University of California, San Diego La Jolla, CA 92093-02. USA duqiu@math.ucsd.edu Jeffrey Remmel Department

More information

Modelling the Zero Coupon Yield Curve:

Modelling the Zero Coupon Yield Curve: Modelling the Zero Coupon Yield Curve: A regression based approach February,2010 12 th Global Conference of Actuaries Srijan Sengupta Section 1: Introduction What is the zero coupon yield curve? Its importance

More information

UNIVERSITY OF CALIFORNIA College of Engineering Departments of Mechanical Engineering and Material Science & Engineering

UNIVERSITY OF CALIFORNIA College of Engineering Departments of Mechanical Engineering and Material Science & Engineering Fall 006 UNIVERSITY OF CALIFORNIA College of Engineering Departments of Mechanical Engineering and Material Science & Engineering MSEc113/MEc14 Mechanical Behavior of Materials Midterm #1 September 19

More information

Social Networks and the Decision to Insure: Evidence from Randomized Experiments in China. University of Michigan

Social Networks and the Decision to Insure: Evidence from Randomized Experiments in China. University of Michigan Social Networks and the Decision to Insure: Evidence from Randomized Experiments in China Jing Cai University of Michigan October 5, 2012 Social Networks & Insurance Demand 1 / 32 Overview Introducing

More information

Objective Bayesian Analysis for Heteroscedastic Regression

Objective 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 information

Dynamic Response of Jackup Units Re-evaluation of SNAME 5-5A Four Methods

Dynamic Response of Jackup Units Re-evaluation of SNAME 5-5A Four Methods ISOPE 2010 Conference Beijing, China 24 June 2010 Dynamic Response of Jackup Units Re-evaluation of SNAME 5-5A Four Methods Xi Ying Zhang, Zhi Ping Cheng, Jer-Fang Wu and Chee Chow Kei ABS 1 Main Contents

More information

Computational Methods forglobal Change Research. Economics & Computable General Equilibrium models

Computational Methods forglobal Change Research. Economics & Computable General Equilibrium models Computational Methods forglobal Change Research Economics & Computable General Equilibrium models Overview Economic modelling CGE models concepts maths example GAMS CGE modelling software Hands on with

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

Asset pricing in the frequency domain: theory and empirics

Asset pricing in the frequency domain: theory and empirics Asset pricing in the frequency domain: theory and empirics Ian Dew-Becker and Stefano Giglio Duke Fuqua and Chicago Booth 11/27/13 Dew-Becker and Giglio (Duke and Chicago) Frequency-domain asset pricing

More information

Likelihood Outline for today

Likelihood Outline for today Likelihood Outline for today What is probability What is likelihood Maximum likelihood estimation Example: estimate a proportion Likelihood- based confidence intervals Example: estimating speciation and

More information

ONERA Fatigue Model. Z-set group. March 14, Mines ParisTech, CNRS UMR 7633 Centre des Matériaux BP 87, Evry cedex, France

ONERA Fatigue Model. Z-set group. March 14, Mines ParisTech, CNRS UMR 7633 Centre des Matériaux BP 87, Evry cedex, France ONERA Fatigue Model Z-set group Mines ParisTech, CNRS UMR 7633 Centre des Matériaux BP 87, 91003 Evry cedex, France March 14, 2013 Plan 1 ONERA Fatigue Model 2 Basic Tools Multiaxial stress amplitude (SEH)

More information

Algorithmic Differentiation of a GPU Accelerated Application

Algorithmic Differentiation of a GPU Accelerated Application of a GPU Accelerated Application Numerical Algorithms Group 1/31 Disclaimer This is not a speedup talk There won t be any speed or hardware comparisons here This is about what is possible and how to do

More information

The Two Sample T-test with One Variance Unknown

The Two Sample T-test with One Variance Unknown The Two Sample T-test with One Variance Unknown Arnab Maity Department of Statistics, Texas A&M University, College Station TX 77843-343, U.S.A. amaity@stat.tamu.edu Michael Sherman Department of Statistics,

More information

Risks For The Long Run And The Real Exchange Rate

Risks For The Long Run And The Real Exchange Rate Riccardo Colacito, Mariano M. Croce Overview International Equity Premium Puzzle Model with long-run risks Calibration Exercises Estimation Attempts & Proposed Extensions Discussion International Equity

More information

Monetary policy regime formalization: instrumental rules

Monetary policy regime formalization: instrumental rules Monetary policy regime formalization: instrumental rules PhD program in economics 2009/10 University of Rome La Sapienza Course in monetary policy (with G. Ciccarone) University of Teramo The monetary

More information

Ct value interpretation

Ct value interpretation Method 1 Appendix of Real-time quantitative PCR training course. Division of Genomics Research Life Science Research Center, Gifu University This document explains how you can interpret the Ct values from

More information

4: Single Cash Flows and Equivalence

4: Single Cash Flows and Equivalence 4.1 Single Cash Flows and Equivalence Basic Concepts 28 4: Single Cash Flows and Equivalence This chapter explains basic concepts of project economics by examining single cash flows. This means that each

More information

Preferences - A Reminder

Preferences - A Reminder Chapter 4 Utility Preferences - A Reminder x y: x is preferred strictly to y. p x ~ y: x and y are equally preferred. f ~ x y: x is preferred at least as much as is y. Preferences - A Reminder Completeness:

More information

Tests for the Difference Between Two Linear Regression Intercepts

Tests for the Difference Between Two Linear Regression Intercepts Chapter 853 Tests for the Difference Between Two Linear Regression Intercepts Introduction Linear regression is a commonly used procedure in statistical analysis. One of the main objectives in linear regression

More information

Modelling the Sharpe ratio for investment strategies

Modelling the Sharpe ratio for investment strategies Modelling the Sharpe ratio for investment strategies Group 6 Sako Arts 0776148 Rik Coenders 0777004 Stefan Luijten 0783116 Ivo van Heck 0775551 Rik Hagelaars 0789883 Stephan van Driel 0858182 Ellen Cardinaels

More information

Econometrics and Economic Data

Econometrics and Economic Data Econometrics and Economic Data Chapter 1 What is a regression? By using the regression model, we can evaluate the magnitude of change in one variable due to a certain change in another variable. For example,

More information

Mathematical Economics dr Wioletta Nowak. Lecture 1

Mathematical Economics dr Wioletta Nowak. Lecture 1 Mathematical Economics dr Wioletta Nowak Lecture 1 Syllabus Mathematical Theory of Demand Utility Maximization Problem Expenditure Minimization Problem Mathematical Theory of Production Profit Maximization

More information

Consumer preferences and utility. Modelling consumer preferences

Consumer preferences and utility. Modelling consumer preferences Consumer preferences and utility Modelling consumer preferences Consumer preferences and utility How can we possibly model the decision of consumers? What will they consume? How much of each good? Actually,

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

Quantile Regression as a Tool for Investigating Local and Global Ice Pressures Paul Spencer and Tom Morrison, Ausenco, Calgary, Alberta, CANADA

Quantile Regression as a Tool for Investigating Local and Global Ice Pressures Paul Spencer and Tom Morrison, Ausenco, Calgary, Alberta, CANADA 24550 Quantile Regression as a Tool for Investigating Local and Global Ice Pressures Paul Spencer and Tom Morrison, Ausenco, Calgary, Alberta, CANADA Copyright 2014, Offshore Technology Conference This

More information

Consolidation of Clays

Consolidation of Clays Consolidation of Clays N. Sivakugan Duration: 17 minutes 1 What is Consolidation? When a saturated clay is loaded externally, GL saturated clay the water is squeezed out of the clay over a long time (due

More information

Final Projects Introduction to Numerical Analysis atzberg/fall2006/index.html Professor: Paul J.

Final Projects Introduction to Numerical Analysis  atzberg/fall2006/index.html Professor: Paul J. Final Projects Introduction to Numerical Analysis http://www.math.ucsb.edu/ atzberg/fall2006/index.html Professor: Paul J. Atzberger Instructions: In the final project you will apply the numerical methods

More information

Expected Inflation Regime in Japan

Expected Inflation Regime in Japan Expected Inflation Regime in Japan Tatsuyoshi Okimoto (Okki) Crawford School of Public Policy Australian National University June 26, 2017 IAAE 2017 Expected Inflation Regime in Japan Expected Inflation

More information

Asset Pricing in Production Economies

Asset Pricing in Production Economies Urban J. Jermann 1998 Presented By: Farhang Farazmand October 16, 2007 Motivation Can we try to explain the asset pricing puzzles and the macroeconomic business cycles, in one framework. Motivation: Equity

More information

Agricultural and Applied Economics 637 Applied Econometrics II

Agricultural and Applied Economics 637 Applied Econometrics II Agricultural and Applied Economics 637 Applied Econometrics II Assignment I Using Search Algorithms to Determine Optimal Parameter Values in Nonlinear Regression Models (Due: February 3, 2015) (Note: Make

More information

Sovereign Default and the Choice of Maturity

Sovereign Default and the Choice of Maturity Sovereign Default and the Choice of Maturity Juan M. Sanchez Horacio Sapriza Emircan Yurdagul FRB of St. Louis Federal Reserve Board Washington U. St. Louis February 4, 204 Abstract This paper studies

More information

Risks for the Long Run and the Real Exchange Rate

Risks for the Long Run and the Real Exchange Rate Risks for the Long Run and the Real Exchange Rate Riccardo Colacito - NYU and UNC Kenan-Flagler Mariano M. Croce - NYU Risks for the Long Run and the Real Exchange Rate, UCLA, 2.22.06 p. 1/29 Set the stage

More information

Credit Spreads and the Macroeconomy

Credit Spreads and the Macroeconomy Credit Spreads and the Macroeconomy Simon Gilchrist Boston University and NBER Joint BIS-ECB Workshop on Monetary Policy & Financial Stability Bank for International Settlements Basel, Switzerland September

More information

A Unified Theory of Bond and Currency Markets

A Unified Theory of Bond and Currency Markets A Unified Theory of Bond and Currency Markets Andrey Ermolov Columbia Business School April 24, 2014 1 / 41 Stylized Facts about Bond Markets US Fact 1: Upward Sloping Real Yield Curve In US, real long

More information

One-Period Valuation Theory

One-Period Valuation Theory One-Period Valuation Theory Part 2: Chris Telmer March, 2013 1 / 44 1. Pricing kernel and financial risk 2. Linking state prices to portfolio choice Euler equation 3. Application: Corporate financial leverage

More information

,,, be any other strategy for selling items. It yields no more revenue than, based on the

,,, be any other strategy for selling items. It yields no more revenue than, based on the ONLINE SUPPLEMENT Appendix 1: Proofs for all Propositions and Corollaries Proof of Proposition 1 Proposition 1: For all 1,2,,, if, is a non-increasing function with respect to (henceforth referred to as

More information

We follow Agarwal, Driscoll, and Laibson (2012; henceforth, ADL) to estimate the optimal, (X2)

We follow Agarwal, Driscoll, and Laibson (2012; henceforth, ADL) to estimate the optimal, (X2) Online appendix: Optimal refinancing rate We follow Agarwal, Driscoll, and Laibson (2012; henceforth, ADL) to estimate the optimal refinance rate or, equivalently, the optimal refi rate differential. In

More information

CHAPTER II LITERATURE STUDY

CHAPTER II LITERATURE STUDY CHAPTER II LITERATURE STUDY 2.1. Risk Management Monetary crisis that strike Indonesia during 1998 and 1999 has caused bad impact to numerous government s and commercial s bank. Most of those banks eventually

More information

Modelling Environmental Extremes

Modelling 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 information

CS227-Scientific Computing. Lecture 6: Nonlinear Equations

CS227-Scientific Computing. Lecture 6: Nonlinear Equations CS227-Scientific Computing Lecture 6: Nonlinear Equations A Financial Problem You invest $100 a month in an interest-bearing account. You make 60 deposits, and one month after the last deposit (5 years

More information

Modelling Environmental Extremes

Modelling 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 information

January Statistics S1 Mark Scheme

January Statistics S1 Mark Scheme January 007 6683 Statistics S1 Mark Scheme Question Scheme Marks number 1. (a) ( ) 17 Just 17 B1 (1) (Accept as totals under each column in qu.) B1, B1 (b) t = 1 and m= 61 S tm S tt 61 1 = 485, = 1191.8

More information

On Stochastic Evaluation of S N Models. Based on Lifetime Distribution

On Stochastic Evaluation of S N Models. Based on Lifetime Distribution Applied Mathematical Sciences, Vol. 8, 2014, no. 27, 1323-1331 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.412 On Stochastic Evaluation of S N Models Based on Lifetime Distribution

More information

arxiv:cond-mat/ v2 [cond-mat.str-el] 5 Nov 2002

arxiv: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 information

Chapter 9 Dynamic Models of Investment

Chapter 9 Dynamic Models of Investment George Alogoskoufis, Dynamic Macroeconomic Theory, 2015 Chapter 9 Dynamic Models of Investment In this chapter we present the main neoclassical model of investment, under convex adjustment costs. This

More information

The Aggregate Implications of Regional Business Cycles

The 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 information

Efficiency Wage. Economics of Information and Contracts Moral Hazard: Applications and Extensions. Financial Contracts. Financial Contracts

Efficiency Wage. Economics of Information and Contracts Moral Hazard: Applications and Extensions. Financial Contracts. Financial Contracts Efficiency Wage Economics of Information and Contracts Moral Hazard: Applications and Extensions Levent Koçkesen Koç University A risk neutral agent working for a firm Assume two effort and output levels

More information

F8. Prototype models for plastic response; integration of plastic flow; structural analys of beam problem.

F8. Prototype models for plastic response; integration of plastic flow; structural analys of beam problem. F8. Prototype models for plastic response; integration of plastic flow; structural analys of beam problem. Prototype model: Perfect plasticity Consider model beavior: Figure 1 stablis reological model:

More information

CHAPTER TOPICS STATISTIK & PROBABILITAS. Copyright 2017 By. Ir. Arthur Daniel Limantara, MM, MT.

CHAPTER TOPICS STATISTIK & PROBABILITAS. Copyright 2017 By. Ir. Arthur Daniel Limantara, MM, MT. Distribusi Normal CHAPTER TOPICS The Normal Distribution The Standardized Normal Distribution Evaluating the Normality Assumption The Uniform Distribution The Exponential Distribution 2 CONTINUOUS PROBABILITY

More information

AUGUST 2017 STOXX REFERENCE CALCULATIONS GUIDE

AUGUST 2017 STOXX REFERENCE CALCULATIONS GUIDE AUGUST 2017 STOXX REFERENCE CALCULATIONS GUIDE CONTENTS 2/14 4.3. SECURITY AVERAGE DAILY TRADED VALUE (ADTV) 13 1. INTRODUCTION TO THE STOXX INDEX GUIDES 3 4.4. TURNOVER 13 2. CHANGES TO THE GUIDE BOOK

More information

Intergenerational Policy and the Measurement of the Tax Incidence of Unfunded Liabilities

Intergenerational Policy and the Measurement of the Tax Incidence of Unfunded Liabilities Intergenerational Policy and the Measurement of the Tax Incidence of Unfunded Liabilities Juan Carlos Conesa, Universitat Autònoma de Barcelona Carlos Garriga, Federal Reserve Bank of St. Louis May 26th,

More information

Copulas? What copulas? R. Chicheportiche & J.P. Bouchaud, CFM

Copulas? What copulas? R. Chicheportiche & J.P. Bouchaud, CFM Copulas? What copulas? R. Chicheportiche & J.P. Bouchaud, CFM Multivariate linear correlations Standard tool in risk management/portfolio optimisation: the covariance matrix R ij = r i r j Find the portfolio

More information

Discussion of. How the LSAPs Influence MBS Yields and Mortgage Rates? Diana Hancock and Wayne Passmore

Discussion of. How the LSAPs Influence MBS Yields and Mortgage Rates? Diana Hancock and Wayne Passmore Discussion of How the LSAPs Influence MBS Yields and Mortgage Rates? Diana Hancock and Wayne Passmore Adi Sunderam Harvard Business School December 6, 2013 Overview How does quantitative easing (QE) work?

More information

Solutions to questions in Chapter 8 except those in PS4. The minimum-variance portfolio is found by applying the formula:

Solutions to questions in Chapter 8 except those in PS4. The minimum-variance portfolio is found by applying the formula: Solutions to questions in Chapter 8 except those in PS4 1. The parameters of the opportunity set are: E(r S ) = 20%, E(r B ) = 12%, σ S = 30%, σ B = 15%, ρ =.10 From the standard deviations and the correlation

More information

Analyzing Oil Futures with a Dynamic Nelson-Siegel Model

Analyzing Oil Futures with a Dynamic Nelson-Siegel Model Analyzing Oil Futures with a Dynamic Nelson-Siegel Model NIELS STRANGE HANSEN & ASGER LUNDE DEPARTMENT OF ECONOMICS AND BUSINESS, BUSINESS AND SOCIAL SCIENCES, AARHUS UNIVERSITY AND CENTER FOR RESEARCH

More information

Estimating Macroeconomic Models of Financial Crises: An Endogenous Regime-Switching Approach

Estimating 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 information

AP Stats Review. Mrs. Daniel Alonzo & Tracy Mourning Sr. High

AP Stats Review. Mrs. Daniel Alonzo & Tracy Mourning Sr. High AP Stats Review Mrs. Daniel Alonzo & Tracy Mourning Sr. High sdaniel@dadeschools.net Agenda 1. AP Stats Exam Overview 2. AP FRQ Scoring & FRQ: 2016 #1 3. Distributions Review 4. FRQ: 2015 #6 5. Distribution

More information

Modeling Capital Market with Financial Signal Processing

Modeling Capital Market with Financial Signal Processing Modeling Capital Market with Financial Signal Processing Jenher Jeng Ph.D., Statistics, U.C. Berkeley Founder & CTO of Harmonic Financial Engineering, www.harmonicfinance.com Outline Theory and Techniques

More information

Financial Stress and Equilibrium Dynamics in Term Interbank Funding Markets

Financial Stress and Equilibrium Dynamics in Term Interbank Funding Markets Financial Stress and Equilibrium Dynamics in Term Interbank Funding Markets Emre Yoldas a Zeynep Senyuz a a Federal Reserve Board June 17, 2017 North American Summer Meeting of the Econometric Society

More information

Convergence of statistical moments of particle density time series in scrape-off layer plasmas

Convergence of statistical moments of particle density time series in scrape-off layer plasmas Convergence of statistical moments of particle density time series in scrape-off layer plasmas R. Kube and O. E. Garcia Particle density fluctuations in the scrape-off layer of magnetically confined plasmas,

More information

Mathematical Economics Dr Wioletta Nowak, room 205 C

Mathematical Economics Dr Wioletta Nowak, room 205 C Mathematical Economics Dr Wioletta Nowak, room 205 C Monday 11.15 am 1.15 pm wnowak@prawo.uni.wroc.pl http://prawo.uni.wroc.pl/user/12141/students-resources Syllabus Mathematical Theory of Demand Utility

More information

Estimating Effects of Adjustable Mortgage Rate Resets

Estimating Effects of Adjustable Mortgage Rate Resets Estimating Effects of Adjustable Mortgage Rate Resets Sergey P. Trudolyubov Strategic Analytics Inc., Santa Fe, NM 87505, USA strudolyubov@strategicanalytics.com Joseph L. Breeden Strategic Analytics Inc.,

More information

Statistics 101: Section L - Laboratory 6

Statistics 101: Section L - Laboratory 6 Statistics 101: Section L - Laboratory 6 In today s lab, we are going to look more at least squares regression, and interpretations of slopes and intercepts. Activity 1: From lab 1, we collected data on

More information

Option Pricing Under a Stressed-Beta Model

Option Pricing Under a Stressed-Beta Model Option Pricing Under a Stressed-Beta Model Adam Tashman in collaboration with Jean-Pierre Fouque University of California, Santa Barbara Department of Statistics and Applied Probability Center for Research

More information

P2.T5. Tuckman Chapter 9. Bionic Turtle FRM Video Tutorials. By: David Harper CFA, FRM, CIPM

P2.T5. Tuckman Chapter 9. Bionic Turtle FRM Video Tutorials. By: David Harper CFA, FRM, CIPM P2.T5. Tuckman Chapter 9 Bionic Turtle FRM Video Tutorials By: David Harper CFA, FRM, CIPM Note: This tutorial is for paid members only. You know who you are. Anybody else is using an illegal copy and

More information

MODELING THE ELECTRICITY SPOT MARKETS

MODELING THE ELECTRICITY SPOT MARKETS .... MODELING THE ELECTRICITY SPOT MARKETS Özgür İnal Rice University 6.23.2009 Özgür İnal MODELING THE ELECTRICITY SPOT MARKETS 1/27 . Motivation Modeling the game the electricity generating firms play

More information

Chapter 5 Univariate time-series analysis. () Chapter 5 Univariate time-series analysis 1 / 29

Chapter 5 Univariate time-series analysis. () Chapter 5 Univariate time-series analysis 1 / 29 Chapter 5 Univariate time-series analysis () Chapter 5 Univariate time-series analysis 1 / 29 Time-Series Time-series is a sequence fx 1, x 2,..., x T g or fx t g, t = 1,..., T, where t is an index denoting

More information

Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay. Solutions to Final Exam

Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay. Solutions to Final Exam Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (30 pts) Answer briefly the following questions. 1. Suppose that

More information

Volatility and Informativeness

Volatility and Informativeness Volatility and Informativeness Eduardo Dávila Cecilia Parlatore December 017 Abstract We explore the equilibrium relation between price volatility and price informativeness in financial markets, with the

More information

F A S C I C U L I M A T H E M A T I C I

F A S C I C U L I M A T H E M A T I C I F A S C I C U L I M A T H E M A T I C I Nr 38 27 Piotr P luciennik A MODIFIED CORRADO-MILLER IMPLIED VOLATILITY ESTIMATOR Abstract. The implied volatility, i.e. volatility calculated on the basis of option

More information

درس هفتم یادگیري ماشین. (Machine Learning) دانشگاه فردوسی مشهد دانشکده مهندسی رضا منصفی

درس هفتم یادگیري ماشین. (Machine Learning) دانشگاه فردوسی مشهد دانشکده مهندسی رضا منصفی یادگیري ماشین توزیع هاي نمونه و تخمین نقطه اي پارامترها Sampling Distributions and Point Estimation of Parameter (Machine Learning) دانشگاه فردوسی مشهد دانشکده مهندسی رضا منصفی درس هفتم 1 Outline Introduction

More information

Supplementary Material for Combinatorial Partial Monitoring Game with Linear Feedback and Its Application. A. Full proof for Theorems 4.1 and 4.

Supplementary Material for Combinatorial Partial Monitoring Game with Linear Feedback and Its Application. A. Full proof for Theorems 4.1 and 4. Supplementary Material for Combinatorial Partial Monitoring Game with Linear Feedback and Its Application. A. Full proof for Theorems 4.1 and 4. If the reader will recall, we have the following problem-specific

More information

Term Par Swap Rate Term Par Swap Rate 2Y 2.70% 15Y 4.80% 5Y 3.60% 20Y 4.80% 10Y 4.60% 25Y 4.75%

Term Par Swap Rate Term Par Swap Rate 2Y 2.70% 15Y 4.80% 5Y 3.60% 20Y 4.80% 10Y 4.60% 25Y 4.75% Revisiting The Art and Science of Curve Building FINCAD has added curve building features (enhanced linear forward rates and quadratic forward rates) in Version 9 that further enable you to fine tune the

More information

STATE OF OHIO DEPARTMENT OF TRANSPORTATION SUPPLEMENT 1102 ACCEPTANCE OF NON-SPECIFICATION MATERIAL ON CONSTRUCTION PROJECTS.

STATE OF OHIO DEPARTMENT OF TRANSPORTATION SUPPLEMENT 1102 ACCEPTANCE OF NON-SPECIFICATION MATERIAL ON CONSTRUCTION PROJECTS. STATE OF OHIO DEPARTMENT OF TRANSPORTATION SUPPLEMENT 1102 ACCEPTANCE OF NON-SPECIFICATION MATERIAL ON CONSTRUCTION PROJECTS October 21, 2016 1102.01 General 1102.02 Determining if Non Specification Material

More information

Axioma Global Multi-Asset Class Risk Model Fact Sheet. AXGMM Version 2.0. May 2018

Axioma Global Multi-Asset Class Risk Model Fact Sheet. AXGMM Version 2.0. May 2018 Axioma Global Multi-Asset Class Risk Fact Sheet AXGMM Version 2.0 May 2018 Axioma s Global Multi-Asset Class Risk (Global MAC ) is intended to capture the investment risk of a multi-asset class portfolio

More information

The Young, the Old, and the Restless: Demographics and Business Cycle Volatility. Nir Jaimovich and Henry Siu

The Young, the Old, and the Restless: Demographics and Business Cycle Volatility. Nir Jaimovich and Henry Siu The Young, the Old, and the Restless: Demographics and Business Cycle Volatility Nir Jaimovich and Henry Siu What is the role of demographic change in explaining changes in business cycle volatility? Since

More information

Least Squares Monte Carlo (LSMC) life and annuity application Prepared for Institute of Actuaries of Japan

Least Squares Monte Carlo (LSMC) life and annuity application Prepared for Institute of Actuaries of Japan Least Squares Monte Carlo (LSMC) life and annuity application Prepared for Institute of Actuaries of Japan February 3, 2015 Agenda A bit of theory Overview of application Case studies Final remarks 2 Least

More information

Conditional Rewriting

Conditional Rewriting Conditional Rewriting Bernhard Gramlich ISR 2009, Brasilia, Brazil, June 22-26, 2009 Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26, 2009 1 Outline Introduction Basics in Conditional Rewriting

More information

Preferences and Utility

Preferences and Utility Preferences and Utility PowerPoint Slides prepared by: Andreea CHIRITESCU Eastern Illinois University 1 Axioms of Rational Choice Completeness If A and B are any two situations, an individual can always

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

Random Variables and Probability Distributions

Random Variables and Probability Distributions Chapter 3 Random Variables and Probability Distributions Chapter Three Random Variables and Probability Distributions 3. Introduction An event is defined as the possible outcome of an experiment. In engineering

More information

IEOR E4602: Quantitative Risk Management

IEOR E4602: Quantitative Risk Management IEOR E4602: Quantitative Risk Management Basic Concepts and Techniques of Risk Management Martin Haugh Department of Industrial Engineering and Operations Research Columbia University Email: martin.b.haugh@gmail.com

More information

What the hell statistical arbitrage is?

What the hell statistical arbitrage is? What the hell statistical arbitrage is? Statistical arbitrage is the mispricing of any given security according to their expected value, base on the mathematical analysis of its historic valuations. Statistical

More information

Preferences. Rationality in Economics. Indifference Curves

Preferences. Rationality in Economics. Indifference Curves Preferences Rationality in Economics Behavioral Postulate: A decisionmaker always chooses its most preferred alternative from its set of available alternatives. So to model choice we must model decisionmakers

More information

Introductory Microeconomics (ES10001)

Introductory Microeconomics (ES10001) Introductory Microeconomics (ES10001) Exercise 3: Suggested Solutions 1. True/False: a. Indifference curves always slope downwards to the right if the consumer prefers more to less. b. Indifference curves

More information

Evaluating Electricity Generation, Energy Options, and Complex Networks

Evaluating Electricity Generation, Energy Options, and Complex Networks Evaluating Electricity Generation, Energy Options, and Complex Networks John Birge The University of Chicago Graduate School of Business and Quantstar 1 Outline Derivatives Real options and electricity

More information