P SS P SS P SS P SS P SS
|
|
- Alvin French
- 5 years ago
- Views:
Transcription
1
2
3 P SS
4 P SS P SS P SS P SS t P SS t t P SS
5 P SS P SS P SS P SS P SS P SS
6 P SS
7
8 i t r i,t E t 1 [r i,t r f,t ] = γ M,t Cov t 1 (r i,t, r M,t ) + γ M 2,tCov t 1 ( ri,t, r 2 M,t ) r f,t t r M,t t γ M,t γ M 2,t t E t 1 [ ] Cov t 1 ( ) t 1 ( Cov t 1 ri,t, rm,t) 2 γ M 2,t
9 Cos i,t ( Cos i,t = Cov t 1 ri,t, rm,t) 2. β M 2,i,t E[r i,t r f,t ] = β M,i,t µ M,t + β M 2,i,tµ M 2,t µ M,t µ M 2,t β HS,i,t i [ ] E t 1 ϵi,t ϵ 2 M,t β HS,i,t = [ ] [ ] E t 1 ϵ 2i,t Et 1 ϵ 2M,t ϵ i,t = r i,t r f,t α i β M,i (r M,t r f,t ) i ϵ M,t = r M,t r f,t µ M
10 Cos i,t β M 2,i,t β HS,i,t r rm 2 r M rm 2 β HS,i,t β HS,i,t t t 60 t 1 t 12 t 1 β HS,i,t β M 2,i,t % 30% 0.78% 2.77% 0.23 β M β M
11 t 60 t 1 t r i,t rm,t 2 r i,t r 2 M,t α α MKT MKT SMB HML MOM MKT SMB HML RMW CMA α
12 t F (Cos i,k 12 k 1 ) = κ + F (Y i,k 24 k 13 ) θ + F (X i,k 13 ) ϕ + ε i,k 12 k 1, k = 25, 26,..., t i = 1,..., N k N k k Cos i,k 12 k 1 Cos i k 12 k 1 K Y Y i,k 24 k 13 β M i k 24 k 13 K X X i,k 13 i k 13 ε i,k 12 k 1 F (x i,t ) = Rank(x i,t) N t+1 x i,t x t Rank(x i,t ) N t x i,t x t N t K Y θ K X ϕ κ 25 t Cos Y X 12 t ˆκ ˆθ ˆϕ F (Cos i,t t+11 ) = ˆκ + F (Y i,t 12 t 1 ) ˆθ + F (X i,t 1 ) ˆϕ.
13 30% 30% P SS β M Cos Y F (CSt t+11,i )
14 P SS P SS 5.37% β HS Cos β M 2 P SS α 4.02% 0.36% 5.91% P SS P SS P SS MKT P SS P SS β M P SS P SS MKT rm 2 α
15 P SS α α θ ϕ ˆθ ˆϕ 5 th 95 th
16 t 12 t 2 t 12 t 7 t 1 P SS θ P SS P SS
17 P SS R 2 t R 2 N = 25 P SS MKT P SS t R 2 p R 2 R 2 P SS t R 2 R 2 R 2 t
18 P SS 5.55 t t 2.91 P SS β M P SS MKT r M,t r M,t MKT + r M,t r M,t MKT MKT + P SS MKT MKT + P SS P SS MKT SMB HML MOM MKT SMB HML RMW CMA HML MOM SMB R 2 R 2 P SS P SS P SS SMB R 2 SMB P SS 0.73 α P SS P SS SMB P SS
19 SMB MKT P SS HML MOM R 2 R 2 SMB P SS P SS SMB R 2 P SS R 2 p P SS SMB SMB P SS p R 2 SMB P SS MKT P SS HML RMW CMA R 2 MOM MKT P SS
20 p p P SS P SS
21 i 5 th 95 th
22 t 12 t 7 t 6 t 1 β M P SS t t t % 30%
23 30% 40% 30% 30% 30% MKT P SS MKT P SS HML MOM MKT P SS HML RMW CMA MKT P SS MKT P SS P SS MOM RMW HML α α α α α P SS α
24 P SS α P SS α α α MKT P SS
25 QSK(x t ) = (q 0.95(x t ) q 0.50 (x t )) (q 0.50 (x t ) q 0.05 (x t )) q 0.95 (x t ) q 0.05 (x t ) x t q 0.05 (x t ) q 0.50 (x t ) q 0.95 (x t ) 5 th 50 th 95 th x t QSK QSK QSK P SS MOM RMW α P SS α
26 α
27 i t d β M β M,i,t t+11 r i,td r f,td = α i,t t+11 + β M,i,t t+11 (r M,td r f,td ) + ϵ i,td. ϵ i,td Cos r i,td rm,t 2 d β M 2 β M 2,i,t t+11 r i,td r f,td = α i,t t+11 + β M,i,t t+11 (r M,td r f,td ) + β M 2,i,t t+11 (r M,td r f,td ) 2 + υ i,td. β HS ϵ i,td ϵ 2 M,t d ϵ M,td = r M,td 1 Td,t t+11 T d,t t+11 t d =1 r M,td T d,t t+11 t t + 11 ϵ 2 i,t d ϵ 2 M,t d r i,td r i,td υ i,td υ i,td
28 t 12 t 2 t 12 t 7 t 1
29
30
31
32
33 βm βhs α α α Cos β M βhs Cos β M βhs P SS P SS βm βhs α 30% 30% Cos β M 2 βhs P SS P SS 10, 000 T % 1%
34 Cumulative Log-return P SS MKT 1$ P SS MKT
35 Regression parameters for coskewness rank prediction ˆθ ˆϕ
36 5 th 95 th β M Cos ˆθ ˆϕ th 95 th
37 P SS MKT P SS MKT MKT + R 2 R (5.27) ( 2.62) (5.27) ( 2.65) (6.08) ( 3.64) (2.62) (0.04) (0.01) (6.08) ( 3.77) (2.65) P SS β M β+ M (6.00) (2.47) ( 1.41) ( 0.56) (0.66) (0.76) (6.00) (2.70) ( 1.29) ( 0.33) (4.14) ( 1.26) (4.14) ( 1.27) (4.45) ( 2.29) (3.06) (0.00) (0.00) (4.45) ( 2.55) (2.91) P SS β M β+ M (2.87) (2.64) ( 0.72) ( 0.08) (1.00) (0.65) (2.87) (2.02) ( 0.75) (0.06) MKT P SS MKT MKT MKT + t R 2 p R 2 R 2 p R
38 P SS MKT P SS SMB HML MOM R 2 R (2.50) ( 1.12) (1.72) (2.64) (1.47) (2.50) ( 0.25) (2.01) (2.09) (1.38) P SS (2.67) ( 1.34) (2.03) (1.73) (2.65) (1.50) (0.92) (0.34) (2.67) ( 0.49) (0.96) ( 0.03) (1.98) (1.17) MKT P SS HML UMD (2.69) ( 1.34) (2.39) (2.65) (1.51) (0.93) (0.65) (2.69) ( 0.49) (1.95) (1.98) (1.18) (1.75) ( 0.76) (2.25) (0.04) (3.80) (1.75) ( 0.56) (2.11) (0.15) (1.66) P SS (1.65) ( 0.68) (0.82) (2.25) ( 0.12) (3.72) (0.78) (0.68) (1.65) ( 0.51) ( 0.41) (1.07) (0.03) (1.83) MKT P SS HML UMD (1.64) ( 0.64) (2.61) (0.50) (3.89) (0.99) (0.51) (1.64) ( 0.39) (1.77) (0.54) (1.33) MKT SMB HML MOM P SS t R 2 P SS p R 2 R 2 p R
39 P SS MKT P SS SMB HML RMW CMA R 2 R (4.18) ( 2.31) (2.04) (2.57) (1.03) (0.79) (4.18) ( 1.66) (3.34) (0.56) (1.10) ( 0.09) P SS (3.50) ( 1.60) (3.09) (2.11) (2.60) (1.84) (1.25) (0.00) (0.00) (3.50) ( 0.35) (2.87) ( 1.70) (0.41) (2.48) (0.79) MKT P SS HML RMW CMA (4.35) ( 2.29) (3.01) (2.57) (1.86) (1.00) (0.12) (0.00) (4.35) ( 1.34) (4.39) (0.62) (2.72) (0.33) (2.04) ( 0.90) (3.27) ( 1.66) (0.69) (1.27) (2.04) (0.00) (2.70) ( 2.48) (1.39) (2.21) P SS (1.85) ( 0.69) (1.14) (3.02) ( 0.71) (0.41) (1.45) (0.28) (0.44) (1.85) (0.32) (0.76) (0.65) ( 2.14) (1.32) (2.26) MKT P SS HML RMW CMA (1.72) ( 0.52) (2.33) ( 0.34) (0.06) (2.06) (0.75) (0.82) (1.72) (0.68) (2.40) ( 1.95) (1.04) (2.37) MKT SMB HML RMW CMA P SS t R 2 P SS p R 2 R 2 p R
40 p MKT P SS (0.80) (1.40) (2.37) (1.33) (0.64) (2.37) (2.34) ( 0.99) (2.65) (2.26) (1.73) (1.89) (2.52) (1.38) (2.00) (1.69) (1.56) (1.99) MKT P SS MKT P SS t
41 Regression parameters for idiosyncratic skewness rank prediction ˆθ ˆϕ
42 5 th 95 th β M Cos ˆθ ˆϕ th 95 th
43 Forward daily idiosyncratic skewness Predicted idiosyncratic skewness Lagged idiosyncratic skewness Lagged idiosyncratic volatility % 30%
44 α β MKT β P SS β HML β MOM β RMW β CMA R 2 MKT P SS (2.43) (45.06) (1.09) (2.17) (36.15) (3.59) (0.04) (24.83) (6.88) (1.11) (5.21) (9.46) MKT P SS HML MOM (0.99) (73.09) (1.51) (3.97) (1.57) (2.78) (48.68) (8.40) (6.03) (8.34) (1.78) (23.60) (13.61) (1.47) (7.53) (1.50) (3.13) (15.70) (0.59) (6.78) MKT P SS HML RMW CMA (1.31) (78.13) (1.10) (3.32) (6.25) (4.09) (0.46) (52.87) (5.84) (5.64) (0.40) (1.51) (0.23) (24.66) (4.65) (2.90) (2.98) (0.25) (0.46) (4.40) (4.28) (1.89) (3.96) (0.00) MKT P SS MKT P SS HML MOM MKT P SS RMW CMA α R 2 t T
45 α β MKT β P SS β HML β MOM β RMW β CMA R 2 MKT P SS (2.78) (78.43) (12.32) (0.43) (30.63) (0.75) (1.44) (24.22) (6.11) (1.85) (6.36) (7.63) MKT P SS HML MOM (0.04) (148.90) (16.97) (2.36) (8.80) (1.08) (52.78) (6.13) (7.96) (12.14) (0.36) (31.05) (14.01) (4.13) (9.65) (0.33) (6.70) (15.95) (2.79) (10.16) MKT P SS HML RMW CMA (0.50) (118.45) (8.47) (1.77) (6.66) (3.97) (0.71) (44.58) (0.92) (6.03) (3.12) (0.81) (0.93) (27.57) (4.60) (4.66) (3.67) (0.68) (0.76) (7.14) (5.21) (4.38) (4.21) (1.18) MKT P SS MKT P SS HML MOM MKT P SS RMW CMA α R 2 t T
46 α β MKT β P SS β HML β MOM β RMW β CMA R 2 MKT P SS (2.63) (42.80) (0.94) (2.15) (35.77) (3.80) (0.12) (24.44) (6.59) (1.25) (4.96) (9.03) MKT P SS HML MOM (0.83) (73.57) (1.52) (4.41) (2.37) (2.83) (48.49) (8.74) (5.91) (8.67) (1.76) (23.04) (13.21) (1.37) (7.70) (1.54) (2.79) (15.45) (0.81) (7.16) MKT P SS HML RMW CMA (1.34) (78.43) (1.51) (3.86) (6.70) (3.97) (0.50) (51.79) (5.97) (5.40) (0.37) (1.53) (0.20) (24.13) (4.44) (2.80) (3.05) (0.25) (0.43) (4.15) (3.98) (1.67) (4.08) (0.97) MKT P SS MKT P SS HML MOM MKT P SS RMW CMA α R 2 t T
47 α β MKT β P SS β HML β MOM β RMW β CMA R 2 MKT P SS (2.93) (77.17) (12.02) (0.02) (29.28) (1.41) (1.65) (23.13) (5.74) (2.05) (6.14) (7.29) MKT P SS HML MOM (0.02) (149.20) (16.87) (2.92) (9.07) (1.14) (53.31) (7.95) (6.70) (13.81) (0.36) (30.42) (13.63) (3.60) (9.89) (0.34) (6.40) (15.68) (2.29) (10.66) MKT P SS HML RMW CMA (0.51) (117.89) (8.51) (1.32) (6.91) (4.03) (0.78) (40.99) (1.37) (5.60) (3.58) (0.85) (0.94) (26.57) (4.12) (4.40) (4.05) (0.72) (0.77) (6.85) (4.78) (4.11) (4.57) (1.21) MKT P SS MKT P SS HML MOM MKT P SS RMW CMA α R 2 t T
Equity risk factors and the Intertemporal CAPM
Equity risk factors and the Intertemporal CAPM Ilan Cooper 1 Paulo Maio 2 1 Norwegian Business School (BI) 2 Hanken School of Economics BEROC Conference, Minsk Outline 1 Motivation 2 Cross-sectional tests
More informationSupply Chain Network Structure and Risk Propagation
Supply Chain Network Structure and Risk Propagation John R. Birge 1 1 University of Chicago Booth School of Business (joint work with Jing Wu, Chicago Booth) IESE Business School Birge (Chicago Booth)
More informationUniform Inference for Conditional Factor Models with Instrumental and Idiosyncratic Betas
Uniform Inference for Conditional Factor Models with Instrumental and Idiosyncratic Betas Yuan Xiye Yang Rutgers University Dec 27 Greater NY Econometrics Overview main results Introduction Consider a
More informationPredicting Mutual Fund Performance
Predicting Mutual Fund Performance Oxford, July-August 2013 Allan Timmermann 1 1 UC San Diego, CEPR, CREATES Timmermann (UCSD) Predicting fund performance July 29 - August 2, 2013 1 / 51 1 Basic Performance
More informationRegression Analysis. y t = β 1 x t1 + β 2 x t2 + β k x tk + ϵ t, t = 1,..., T,
Regression Analysis The multiple linear regression model with k explanatory variables assumes that the tth observation of the dependent or endogenous variable y t is described by the linear relationship
More informationMFE Financial Econometrics 2018 Final Exam Model Solutions
MFE Financial Econometrics 2018 Final Exam Model Solutions Tuesday 12 th March, 2019 1. If (X, ε) N (0, I 2 ) what is the distribution of Y = µ + β X + ε? Y N ( µ, β 2 + 1 ) 2. What is the Cramer-Rao lower
More informationGMM - Generalized method of moments
GMM - Generalized method of moments GMM Intuition: Matching moments You want to estimate properties of a data set {x t } T t=1. You assume that x t has a constant mean and variance. x t (µ 0, σ 2 ) Consider
More informationFinite Sample Analysis of Weighted Realized Covariance with Noisy Asynchronous Observations
Finite Sample Analysis of Weighted Realized Covariance with Noisy Asynchronous Observations Taro Kanatani JSPS Fellow, Institute of Economic Research, Kyoto University October 18, 2007, Stanford 1 Introduction
More informationFinancial Times Series. Lecture 12
Financial Times Series Lecture 12 Multivariate Volatility Models Here our aim is to generalize the previously presented univariate volatility models to their multivariate counterparts We assume that returns
More information1 One-way analysis of variance
LIST OF FORMULAS (Version from 21. November 2014) STK2120 1 One-way analysis of variance Assume X ij = µ+α i +ɛ ij ; j = 1, 2,..., J i ; i = 1, 2,..., I ; where ɛ ij -s are independent and N(0, σ 2 ) distributed.
More informationLecture Notes in Empirical Finance (PhD): Linear Factor Models
Contents Lecture Notes in Empirical Finance (PhD): Linear Factor Models Paul Söderlind 8 June 26 University of St Gallen Address: s/bf-hsg, Rosenbergstrasse 52, CH-9 St Gallen, Switzerland E-mail: PaulSoderlind@unisgch
More informationThe Conditional Pricing of Systematic and Idiosyncratic Risk in the UK Equity Market. Niall O Sullivan b Francesco Rossi c. This version: July 2014
The Conditional Pricing of Systematic and Idiosyncratic Risk in the UK Equity Market John Cotter a Niall O Sullivan b Francesco Rossi c This version: July 2014 Abstract We test whether firm idiosyncratic
More informationLeast Squares Data Fitting
Least Squares Data Fitting Stephen Boyd EE103 Stanford University October 31, 2017 Outline Least squares model fitting Validation Feature engineering Least squares model fitting 2 Setup we believe a scalar
More information3. For a given dataset and linear model, what do you think is true about least squares estimates? Is Ŷ always unique? Yes. Is ˆβ always unique? No.
7. LEAST SQUARES ESTIMATION 1 EXERCISE: Least-Squares Estimation and Uniqueness of Estimates 1. For n real numbers a 1,...,a n, what value of a minimizes the sum of squared distances from a to each of
More informationMaster s Written Examination
Master s Written Examination Option: Statistics and Probability Spring 05 Full points may be obtained for correct answers to eight questions Each numbered question (which may have several parts) is worth
More informationProblem 1 (20) Log-normal. f(x) Cauchy
ORF 245. Rigollet Date: 11/21/2008 Problem 1 (20) f(x) f(x) 0.0 0.1 0.2 0.3 0.4 0.0 0.2 0.4 0.6 0.8 4 2 0 2 4 Normal (with mean -1) 4 2 0 2 4 Negative-exponential x x f(x) f(x) 0.0 0.1 0.2 0.3 0.4 0.5
More informationVolatility. Gerald P. Dwyer. February Clemson University
Volatility Gerald P. Dwyer Clemson University February 2016 Outline 1 Volatility Characteristics of Time Series Heteroskedasticity Simpler Estimation Strategies Exponentially Weighted Moving Average Use
More informationDirectional Statistics by K. V. Mardia & P. E. Jupp Wiley, Chichester, Errata to 1st printing
Directional Statistics K V Mardia & P E Jupp Wiley, Chichester, 2000 Errata to 1st printing 8 4 Insert of after development 17 3 Replace θ 1 α,, θ 1 α θ 1 α,, θ n α 19 1 = (2314) Replace θ θ i 20 7 Replace
More informationComparing Asset Pricing Models: Distance-based Metrics and Bayesian Interpretations. Zhongzhi (Lawrence) He * This version: March 2018.
Comparing Asset Pricing Models: Distance-based Metrics and Bayesian Interpretations Zhongzhi (Lawrence) He * This version: March 2018 Abstract In light of the power problems of statistical tests and undisciplined
More informationGARCH Models Estimation and Inference
GARCH Models Estimation and Inference Eduardo Rossi University of Pavia December 013 Rossi GARCH Financial Econometrics - 013 1 / 1 Likelihood function The procedure most often used in estimating θ 0 in
More informationFinancial Econometrics Short Course Lecture 3 Multifactor Pricing Model
Financial Econometrics Short Course Lecture 3 Multifactor Pricing Model Oliver Linton obl20@cam.ac.uk Renmin University Financial Econometrics Short Course Lecture 3 MultifactorRenmin Pricing University
More informationSystem Identification
System Identification Arun K. Tangirala Department of Chemical Engineering IIT Madras July 26, 2013 Module 6 Lecture 1 Arun K. Tangirala System Identification July 26, 2013 1 Objectives of this Module
More informationEconometric Methods for Panel Data
Based on the books by Baltagi: Econometric Analysis of Panel Data and by Hsiao: Analysis of Panel Data Robert M. Kunst robert.kunst@univie.ac.at University of Vienna and Institute for Advanced Studies
More informationDeep Learning in Asset Pricing
Deep Learning in Asset Pricing Luyang Chen 1 Markus Pelger 1 Jason Zhu 1 1 Stanford University November 17th 2018 Western Mathematical Finance Conference 2018 Motivation Hype: Machine Learning in Investment
More informationAnalysis of Cross-Sectional Data
Analysis of Cross-Sectional Data Kevin Sheppard http://www.kevinsheppard.com Oxford MFE This version: October 30, 2017 November 6, 2017 Outline Econometric models Specification that can be analyzed with
More informationTime-varying parameters: New test tailored to applications in finance and macroeconomics. Russell Davidson and Niels S. Grønborg
Time-varying parameters: New test tailored to applications in finance and macroeconomics Russell Davidson and Niels S. Grønborg CREATES Research Paper 2018-22 Department of Economics and Business Economics
More informationModule 9: Stationary Processes
Module 9: Stationary Processes Lecture 1 Stationary Processes 1 Introduction A stationary process is a stochastic process whose joint probability distribution does not change when shifted in time or space.
More informationSlides 12: Output Analysis for a Single Model
Slides 12: Output Analysis for a Single Model Objective: Estimate system performance via simulation. If θ is the system performance, the precision of the estimator ˆθ can be measured by: The standard error
More informationRiemann Manifold Methods in Bayesian Statistics
Ricardo Ehlers ehlers@icmc.usp.br Applied Maths and Stats University of São Paulo, Brazil Working Group in Statistical Learning University College Dublin September 2015 Bayesian inference is based on Bayes
More informationPanel Threshold Regression Models with Endogenous Threshold Variables
Panel Threshold Regression Models with Endogenous Threshold Variables Chien-Ho Wang National Taipei University Eric S. Lin National Tsing Hua University This Version: June 29, 2010 Abstract This paper
More informationTrade and Inequality: From Theory to Estimation
Trade and Inequality: From Theory to Estimation Elhanan Helpman Oleg Itskhoki Marc Muendler Stephen Redding Harvard Princeton UC San Diego Princeton MEF Italia Dipartimento del Tesoro September 2014 1
More informationSymmetric btw positive & negative prior returns. where c is referred to as risk premium, which is expected to be positive.
Advantages of GARCH model Simplicity Generates volatility clustering Heavy tails (high kurtosis) Weaknesses of GARCH model Symmetric btw positive & negative prior returns Restrictive Provides no explanation
More informationInference on Risk Premia in the Presence of Omitted Factors
Inference on Risk Premia in the Presence of Omitted Factors Stefano Giglio Dacheng Xiu Booth School of Business, University of Chicago Center for Financial and Risk Analytics Stanford University May 19,
More informationSIO 221B, Rudnick adapted from Davis 1. 1 x lim. N x 2 n = 1 N. { x} 1 N. N x = 1 N. N x = 1 ( N N x ) x = 0 (3) = 1 x N 2
SIO B, Rudnick adapted from Davis VII. Sampling errors We do not have access to the true statistics, so we must compute sample statistics. By this we mean that the number of realizations we average over
More informationEmpirical Asset Pricing
Department of Mathematics and Statistics, University of Vaasa, Finland Texas A&M University, May June, 2013 As of May 24, 2013 Part III Stata Regression 1 Stata regression Regression Factor variables Postestimation:
More informationData Science and Service Research Discussion Paper
Discussion Paper No. 96 Estimation of Weak Factor Models Yoshimasa Uematsu Takashi Yamagata April 11, 019 Data Science and Service Research Discussion Paper Center for Data Science and Service Research
More informationClass 11 Maths Chapter 15. Statistics
1 P a g e Class 11 Maths Chapter 15. Statistics Statistics is the Science of collection, organization, presentation, analysis and interpretation of the numerical data. Useful Terms 1. Limit of the Class
More informationEstimating Global Bank Network Connectedness
Estimating Global Bank Network Connectedness Mert Demirer (MIT) Francis X. Diebold (Penn) Laura Liu (Penn) Kamil Yılmaz (Koç) September 22, 2016 1 / 27 Financial and Macroeconomic Connectedness Market
More informationWhich Factors are Risk Factors in Asset Pricing? A Model Scan Framework
Which Factors are Risk Factors in Asset Pricing? A Model Scan Framework Siddhartha Chib Xiaming Zeng November 2017, May 2018, December 2018 Abstract A key question for understanding the cross-section of
More informationApproximation of BSDEs using least-squares regression and Malliavin weights
Approximation of BSDEs using least-squares regression and Malliavin weights Plamen Turkedjiev (turkedji@math.hu-berlin.de) 3rd July, 2012 Joint work with Prof. Emmanuel Gobet (E cole Polytechnique) Plamen
More informationEFFICIENCY BOUNDS FOR SEMIPARAMETRIC MODELS WITH SINGULAR SCORE FUNCTIONS. (Nov. 2016)
EFFICIENCY BOUNDS FOR SEMIPARAMETRIC MODELS WITH SINGULAR SCORE FUNCTIONS PROSPER DOVONON AND YVES F. ATCHADÉ Nov. 2016 Abstract. This paper is concerned with asymptotic efficiency bounds for the estimation
More informationGeneralized Linear Models. Kurt Hornik
Generalized Linear Models Kurt Hornik Motivation Assuming normality, the linear model y = Xβ + e has y = β + ε, ε N(0, σ 2 ) such that y N(μ, σ 2 ), E(y ) = μ = β. Various generalizations, including general
More informationRegression: Ordinary Least Squares
Regression: Ordinary Least Squares Mark Hendricks Autumn 2017 FINM Intro: Regression Outline Regression OLS Mathematics Linear Projection Hendricks, Autumn 2017 FINM Intro: Regression: Lecture 2/32 Regression
More informationASSET PRICING MODELS
ASSE PRICING MODELS [1] CAPM (1) Some notation: R it = (gross) return on asset i at time t. R mt = (gross) return on the market portfolio at time t. R ft = return on risk-free asset at time t. X it = R
More informationResearch Methodology Statistics Comprehensive Exam Study Guide
Research Methodology Statistics Comprehensive Exam Study Guide References Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology (3rd ed.). Boston: Allyn and Bacon. Gravetter,
More informationLecture 6: Recursive Preferences
Lecture 6: Recursive Preferences Simon Gilchrist Boston Univerity and NBER EC 745 Fall, 2013 Basics Epstein and Zin (1989 JPE, 1991 Ecta) following work by Kreps and Porteus introduced a class of preferences
More informationUnderstanding Regressions with Observations Collected at High Frequency over Long Span
Understanding Regressions with Observations Collected at High Frequency over Long Span Yoosoon Chang Department of Economics, Indiana University Joon Y. Park Department of Economics, Indiana University
More informationGraduate Econometrics I: Unbiased Estimation
Graduate Econometrics I: Unbiased Estimation Yves Dominicy Université libre de Bruxelles Solvay Brussels School of Economics and Management ECARES Yves Dominicy Graduate Econometrics I: Unbiased Estimation
More informationPanels A and B report the summary statistics for all variables used in the comovement analysis based on baseline (Model 1) and extended Fama-
Online Appendix A. Summary Statistics Table A.1. Summary Statistics Panels A and B report the summary statistics for all variables used in the comovement analysis based on baseline (Model 1) and extended
More informationCUSUM TEST FOR PARAMETER CHANGE IN TIME SERIES MODELS. Sangyeol Lee
CUSUM TEST FOR PARAMETER CHANGE IN TIME SERIES MODELS Sangyeol Lee 1 Contents 1. Introduction of the CUSUM test 2. Test for variance change in AR(p) model 3. Test for Parameter Change in Regression Models
More informationGranular Comparative Advantage
Granular Comparative Advantage Cecile Gaubert cecile.gaubert@berkeley.edu Oleg Itskhoki itskhoki@princeton.edu Stanford University March 2018 1 / 26 Exports are Granular Freund and Pierola (2015): Export
More informationKostas Triantafyllopoulos University of Sheffield. Motivation / algorithmic pairs trading. Model set-up Detection of local mean-reversion
Detecting Mean Reverted Patterns in Statistical Arbitrage Outline Kostas Triantafyllopoulos University of Sheffield Motivation / algorithmic pairs trading Model set-up Detection of local mean-reversion
More informationModel Comparison with Sharpe Ratios
Model Comparison with Sharpe Ratios Francisco Barillas, Raymond Kan, Cesare Robotti, and Jay Shanken Barillas is from Emory University. Kan is from the University of Toronto. Robotti is from the University
More informationStatistics & Data Sciences: First Year Prelim Exam May 2018
Statistics & Data Sciences: First Year Prelim Exam May 2018 Instructions: 1. Do not turn this page until instructed to do so. 2. Start each new question on a new sheet of paper. 3. This is a closed book
More information10.7 Fama and French Mutual Funds notes
1.7 Fama and French Mutual Funds notes Why the Fama-French simulation works to detect skill, even without knowing the characteristics of skill. The genius of the Fama-French simulation is that it lets
More informationThe Effects of Monetary Policy on Stock Market Bubbles: Some Evidence
The Effects of Monetary Policy on Stock Market Bubbles: Some Evidence Jordi Gali Luca Gambetti ONLINE APPENDIX The appendix describes the estimation of the time-varying coefficients VAR model. The model
More informationSIMPLE ROBUST TESTS FOR THE SPECIFICATION OF HIGH-FREQUENCY PREDICTORS OF A LOW-FREQUENCY SERIES
SIMPLE ROBUST TESTS FOR THE SPECIFICATION OF HIGH-FREQUENCY PREDICTORS OF A LOW-FREQUENCY SERIES J. Isaac Miller University of Missouri International Symposium on Forecasting Riverside, California June
More informationPhysics 7B Final Exam: Monday December 14th, 2015 Instructors: Prof. R.J. Birgeneau/Dr. A. Frano
Physics 7B Final Exam: Monday December 14th, 15 Instructors: Prof. R.J. Birgeneau/Dr. A. Frano Total points: 1 (7 problems) Show all your work and take particular care to explain what you are doing. Partial
More informationForecasting and Estimation
February 3, 2009 Forecasting I Very frequently the goal of estimating time series is to provide forecasts of future values. This typically means you treat the data di erently than if you were simply tting
More informationModel Validation in Non-Linear Continuous-Discrete Grey-Box Models p.1/30
Model Validation in Non-Linear Continuous-Discrete Grey-Box Models Jan Holst, Erik Lindström, Henrik Madsen and Henrik Aalborg Niels Division of Mathematical Statistics, Centre for Mathematical Sciences
More informationThe Slow Convergence of OLS Estimators of α, β and Portfolio. β and Portfolio Weights under Long Memory Stochastic Volatility
The Slow Convergence of OLS Estimators of α, β and Portfolio Weights under Long Memory Stochastic Volatility New York University Stern School of Business June 21, 2018 Introduction Bivariate long memory
More informationMultivariate modelling of long memory processes with common components
Multivariate modelling of long memory processes with common components Claudio Morana University of Piemonte Orientale, International Centre for Economic Research (ICER), and Michigan State University
More informationVariance. Standard deviation VAR = = value. Unbiased SD = SD = 10/23/2011. Functional Connectivity Correlation and Regression.
10/3/011 Functional Connectivity Correlation and Regression Variance VAR = Standard deviation Standard deviation SD = Unbiased SD = 1 10/3/011 Standard error Confidence interval SE = CI = = t value for
More informationStatistical Models for Rainfall with Applications to Index Insura
Statistical Models for Rainfall with Applications to April 21, 2008 Overview The idea: Insure farmers against the risk of crop failure, like drought, instead of crop failure itself. It reduces moral hazard
More informationZHAW Zurich University of Applied Sciences. Bachelor s Thesis Estimating Multi-Beta Pricing Models With or Without an Intercept:
ZHAW Zurich University of Applied Sciences School of Management and Law Bachelor s Thesis Estimating Multi-Beta Pricing Models With or Without an Intercept: Further Results from Simulations Submitted by:
More informationGARCH Models Estimation and Inference. Eduardo Rossi University of Pavia
GARCH Models Estimation and Inference Eduardo Rossi University of Pavia Likelihood function The procedure most often used in estimating θ 0 in ARCH models involves the maximization of a likelihood function
More informationSegment-Fixed Priority Scheduling for Self-Suspending Real-Time Tasks
Segment-Fixed Priority Scheduling for Self-Suspending Real-Time Tasks Junsung Kim, Björn Andersson, Dionisio de Niz, and Raj Rajkumar Carnegie Mellon University 2/31 Motion Planning on Self-driving Parallel
More informationAdvanced Statistics I : Gaussian Linear Model (and beyond)
Advanced Statistics I : Gaussian Linear Model (and beyond) Aurélien Garivier CNRS / Telecom ParisTech Centrale Outline One and Two-Sample Statistics Linear Gaussian Model Model Reduction and model Selection
More informationEstimating Econometric Models through Matrix Equations
Estimating Econometric Models through Matrix Equations Federico Poloni 1 Giacomo Sbrana 2 1 U Pisa, Dept of Computer Science 2 Rouen Business School, France No Free Lunch Seminar SNS, Pisa, February 2013
More informationIn modern portfolio theory, which started with the seminal work of Markowitz (1952),
1 Introduction In modern portfolio theory, which started with the seminal work of Markowitz (1952), many academic researchers have examined the relationships between the return and risk, or volatility,
More informationA Modified Fractionally Co-integrated VAR for Predicting Returns
A Modified Fractionally Co-integrated VAR for Predicting Returns Xingzhi Yao Marwan Izzeldin Department of Economics, Lancaster University 13 December 215 Yao & Izzeldin (Lancaster University) CFE (215)
More informationModeling conditional distributions with mixture models: Applications in finance and financial decision-making
Modeling conditional distributions with mixture models: Applications in finance and financial decision-making John Geweke University of Iowa, USA Journal of Applied Econometrics Invited Lecture Università
More informationApplications of Random Matrix Theory to Economics, Finance and Political Science
Outline Applications of Random Matrix Theory to Economics, Finance and Political Science Matthew C. 1 1 Department of Economics, MIT Institute for Quantitative Social Science, Harvard University SEA 06
More informationDynamic analysis of binary longitudinal data
Dynamic analysis of binary longitudinal data Ørnulf Borgan Department of Mathematics University of Oslo Based on joint work with Rosemeire L. Fiaccone, Robin Henderson and Mauricio L. Barreto 1 Outline:
More informationBandit Algorithms. Zhifeng Wang ... Department of Statistics Florida State University
Bandit Algorithms Zhifeng Wang Department of Statistics Florida State University Outline Multi-Armed Bandits (MAB) Exploration-First Epsilon-Greedy Softmax UCB Thompson Sampling Adversarial Bandits Exp3
More informationSchool of Education, Culture and Communication Division of Applied Mathematics
School of Education, Culture and Communication Division of Applied Mathematics MASTER THESIS IN MATHEMATICS / APPLIED MATHEMATICS Estimation and Testing the Quotient of Two Models by Marko Dimitrov Masterarbete
More informationMultivariate GARCH models.
Multivariate GARCH models. Financial market volatility moves together over time across assets and markets. Recognizing this commonality through a multivariate modeling framework leads to obvious gains
More informationScattering. March 20, 2016
Scattering March 0, 06 The scattering of waves of any kind, by a compact object, has applications on all scales, from the scattering of light from the early universe by intervening galaxies, to the scattering
More informationGARCH Models. Eduardo Rossi University of Pavia. December Rossi GARCH Financial Econometrics / 50
GARCH Models Eduardo Rossi University of Pavia December 013 Rossi GARCH Financial Econometrics - 013 1 / 50 Outline 1 Stylized Facts ARCH model: definition 3 GARCH model 4 EGARCH 5 Asymmetric Models 6
More informationWhy Data Transformation? Data Transformation. Homoscedasticity and Normality. Homoscedasticity and Normality
Objectives: Data Transformation Understand why we often need to transform our data The three commonly used data transformation techniques Additive effects and multiplicative effects Application of data
More informationA Regression Model For Recurrent Events With Distribution Free Correlation Structure
A Regression Model For Recurrent Events With Distribution Free Correlation Structure J. Pénichoux(1), A. Latouche(2), T. Moreau(1) (1) INSERM U780 (2) Université de Versailles, EA2506 ISCB - 2009 - Prague
More informationRegularized PCA to denoise and visualise data
Regularized PCA to denoise and visualise data Marie Verbanck Julie Josse François Husson Laboratoire de statistique, Agrocampus Ouest, Rennes, France CNAM, Paris, 16 janvier 2013 1 / 30 Outline 1 PCA 2
More informationEE292: Fundamentals of ECE
EE292: Fundamentals of ECE Fall 2012 TTh 10:00-11:15 SEB 1242 Lecture 20 121101 http://www.ee.unlv.edu/~b1morris/ee292/ 2 Outline Chapters 1-3 Circuit Analysis Techniques Chapter 10 Diodes Ideal Model
More informationStaff Working Paper No. 777 The long-run information effect of central bank communication
Staff Working Paper No. 777 The long-run information effect of central bank communication Stephen Hansen, Michael McMahon and Matthew Tong January 2019 Staff Working Papers describe research in progress
More informationSparsity Models. Tong Zhang. Rutgers University. T. Zhang (Rutgers) Sparsity Models 1 / 28
Sparsity Models Tong Zhang Rutgers University T. Zhang (Rutgers) Sparsity Models 1 / 28 Topics Standard sparse regression model algorithms: convex relaxation and greedy algorithm sparse recovery analysis:
More informationEcon 620. Matrix Differentiation. Let a and x are (k 1) vectors and A is an (k k) matrix. ) x. (a x) = a. x = a (x Ax) =(A + A (x Ax) x x =(A + A )
Econ 60 Matrix Differentiation Let a and x are k vectors and A is an k k matrix. a x a x = a = a x Ax =A + A x Ax x =A + A x Ax = xx A We don t want to prove the claim rigorously. But a x = k a i x i i=
More informationFinal Exam November 24, Problem-1: Consider random walk with drift plus a linear time trend: ( t
Problem-1: Consider random walk with drift plus a linear time trend: y t = c + y t 1 + δ t + ϵ t, (1) where {ϵ t } is white noise with E[ϵ 2 t ] = σ 2 >, and y is a non-stochastic initial value. (a) Show
More information7 Day 3: Time Varying Parameter Models
7 Day 3: Time Varying Parameter Models References: 1. Durbin, J. and S.-J. Koopman (2001). Time Series Analysis by State Space Methods. Oxford University Press, Oxford 2. Koopman, S.-J., N. Shephard, and
More informationUniversity of Oxford. Statistical Methods Autocorrelation. Identification and Estimation
University of Oxford Statistical Methods Autocorrelation Identification and Estimation Dr. Órlaith Burke Michaelmas Term, 2011 Department of Statistics, 1 South Parks Road, Oxford OX1 3TG Contents 1 Model
More informationVariation Principle in Mechanics
Section 2 Variation Principle in Mechanics Hamilton s Principle: Every mechanical system is characterized by a Lagrangian, L(q i, q i, t) or L(q, q, t) in brief, and the motion of he system is such that
More informationCHAPTER 1: Decomposition Methods
CHAPTER 1: Decomposition Methods Prof. Alan Wan 1 / 48 Table of contents 1. Data Types and Causal vs.time Series Models 2 / 48 Types of Data Time series data: a sequence of observations measured over time,
More informationA new test on the conditional capital asset pricing model
Appl. Math. J. Chinese Univ. 2015, 30(2): 163-186 A new test on the conditional capital asset pricing model LI Xia-fei 1 CAI Zong-wu 2,1 REN Yu 1, Abstract. Testing the validity of the conditional capital
More informationECON 3150/4150, Spring term Lecture 6
ECON 3150/4150, Spring term 2013. Lecture 6 Review of theoretical statistics for econometric modelling (II) Ragnar Nymoen University of Oslo 31 January 2013 1 / 25 References to Lecture 3 and 6 Lecture
More informationTest for Parameter Change in ARIMA Models
Test for Parameter Change in ARIMA Models Sangyeol Lee 1 Siyun Park 2 Koichi Maekawa 3 and Ken-ichi Kawai 4 Abstract In this paper we consider the problem of testing for parameter changes in ARIMA models
More informationComputer Science, Informatik 4 Communication and Distributed Systems. Simulation. Discrete-Event System Simulation. Dr.
Simulation Discrete-Event System Simulation Chapter 0 Output Analysis for a Single Model Purpose Objective: Estimate system performance via simulation If θ is the system performance, the precision of the
More informationStochastic Calculus February 11, / 33
Martingale Transform M n martingale with respect to F n, n =, 1, 2,... σ n F n (σ M) n = n 1 i= σ i(m i+1 M i ) is a Martingale E[(σ M) n F n 1 ] n 1 = E[ σ i (M i+1 M i ) F n 1 ] i= n 2 = σ i (M i+1 M
More informationORTHOGO ALIZED EQUITY RISK PREMIA SYSTEMATIC RISK DECOMPOSITIO. Rudolf F. Klein a,* and K. Victor Chow b,* Abstract
ORTHOGO ALIZED EQUITY RIS PREMIA A D SYSTEMATIC RIS DECOMPOSITIO Rudolf F. lein a,* and. Victor Chow b,* Abstract To solve the dependency problem between factors, in the context of linear multi-factor
More informationTime series: Cointegration
Time series: Cointegration May 29, 2018 1 Unit Roots and Integration Univariate time series unit roots, trends, and stationarity Have so far glossed over the question of stationarity, except for my stating
More informationInternet Appendix for Digesting Anomalies: An Investment Approach
Internet Appendix for Digesting Anomalies: An Investment Approach Kewei Hou The Ohio State University and CAFR Chen Xue University of Cincinnati August 2014 Lu Zhang The Ohio State University and NBER
More information