Ken Danger, Aditya (Adi) Aladangady, Ilya Rahkovsky, ROBERT MCCLELLAND, Phillip Li, John Schindler, KENETT YOSSEF, M. Vojtech, Kim, Abbigale Y. Kim, Bahar, Dr.ElenaSchwartz, Jonathanhelfgot
Advanced Academic Programs
Behavioral Economics and Finance
AS.440.645.51
W 6:00 - 8:30 PM
Instructor: Abbigale Y. Kim
[email protected]
This Version: January 2, 2018
1
Preliminaries
1.1
Course Objectiv
Behavioral Economics and Finance, Spring 2018
Problem Set 1, Due February 21, 2018
Name(s):
This problem set has 4 questions, For numerical problems and proofs show all work clearly and
in order, typi
Macroeconomic Forecasting
Unit #1
Chapter 2 A Brief Review
You are responsible for the material in this
chapter
You will not be explicitly tested on this material,
but I assume that you know it
If you
Macroeconomic Forecasting
Spring 2018
Syllabus
Course Number:
Instructor:
Email:
Office Hours:
440.615.81
John Schindler
[email protected]
By appointment. If you make an appointment, I can meet you in
Macroeconomic Forecasting
Unit #3
Chapter 7 Characterizing Cycles
Material in this chapter is a bit more
mathematical and theoretical than most of
the other chapters
Chapter 8 provides the practical a
Macroeconomic Forecasting
Unit #2
Chapter 5 Modeling and Forecasting
Trend
Knowledge of the material in this chapter is a
building block for more complicated models
This chapter is all about determini
Macroeconometrics 2:
Dierence Equations and Filtering of Time Series Data
(Graded Homework Problems)
Brendan Epstein, Ph.D.
Johns Hopkins University
1. Worth 25 points. Find the homogenous solution to
Macroeconometrics 3:
Autocorrelated Disturbances
Brendan Epstein, Ph.D.
Johns Hopkins University
Contents
1 Dealing with Time Series Data
2
2 Time Series Operators
3
3 First Order Autocorrelation (Ser
Macroeconometrics 3:
Autocorrelated Disturbances (Practice Problems with
Solutions)
Brendan Epstein, Ph.D.
Johns Hopkins University
Contents
1. A property of lag operators noted in the lecture notes i
HW3
1.
7.52468e+12
2.
27720
3. 7.021881 percent
4.
a. 2 percent
b. 1.2 percent
c. 0.4 percent
d. 0.1 percent
4.2 54.05 percent
5. a. 4.39 percent
b. 0
6.
a. 1.8
b. 0.375
c. 3
R for Data
Science
IMPORT, TIDY, TRANSFORM, VISUALIZE, AND MODEL DATA
Hadley Wickham &
Garrett Grolemund
R for Data Science
Import, Tidy, Transform, Visualize,
and Model Data
Hadley Wickham and Garret
May 2017
Big Data and AI Strategies
Machine Learning and Alternative Data Approach to Investing
Quantitative and Derivatives Strategy
Marko Kolanovic, PhDAC
[email protected]
Rajesh T. Kris
Hw4 Thanawit Oonseangjan (Tito)
1) Suppose that there is only one teacher per class
a. 705 2.62*(25) = 639.5
b. 705 2.62*(20) = 652.6
c. 705 2.62*(19.6) = 653.648
d. I do not think that prediction of
Network Analysis and Visualization with R and igraph
Katherine Ognyanova, www.kateto.net
NetSciX 2016 School of Code Workshop, Wroclaw, Poland
Contents
1. A quick reminder of R basics
3
1.1 Assignment
Financial Econometrics
Spring 2018: Lecture 2
Dror Y. Kenett
Overview
Correlation
Rank correlation
Auto-correlation
Partial correlation
Examples of correlation analysis
Correlation: The degree o
July 05, 2017
Economics Group
Special Commentary
John E. Silvia, Chief Economist
john.[email protected] (704) 410-3275
Azhar Iqbal, Econometrician
[email protected] (704) 410-3270
Michael
April 06, 2017
Economics Group
Special Commentary
John E. Silvia, Chief Economist
[email protected] (704) 410-3275
Azhar Iqbal, Econometrician
[email protected] (704) 410-3270
Michae
Financial Econometrics
Spring 2018: Lecture 1
Dror Y. Kenett
Goals of the course
Access and process financial data from online sources
Provide basic knowledge of financial time series and their
char
Homework assignment 1
Due: January 29, 2018
Instruction: Review the class materials, and then go over the detailed step by step example
provided on the course website, titled Lesson1_Rcodes, using the
Chapter 7: Optimal Risky Portfolios
2.
(a) and (c). After real estate is added to the portfolio, there are four asset classes in the portfolio: stocks,
bonds, cash, and real estate. Portfolio variance
Econ of Invest. & Fin. Mgmt Final
Fall 2015 Vojtech
1
Short Answer
1. (4 pts) How is the current situation of raising rates different for the Federal Reserve
than prior rate increases? And what is one
Econ of Invest. & Fin. Mgmt Lecture Notes
3
Vojtech
1
Chapter Three: How Securities Are Traded
Big Themes
How firms issue securities
Primary vs. secondary market
Privately held vs. publicly traded
Econ of Invest. & Fin. Mgmt Midterm 1
Fall 2015 Vojtech
1
Definition Match (21 pts, 1 pt each)
Match each term to a definition to the right. Notice that there is one extra definition.
So just number y
Chapter 5: Risk, Return, and the Historical Record
1. The Fisher equation predicts that the nominal rate will equal the equilibrium real rate plus the expected inflation
rate. Hence, if the inflation
Midterm 2
57
POINTS
Short Answer
CAPM
1
2
a
2
b
3
c
r_f
r_m
beta
0.047
0.132
1.25
E(r)
0.15325
0.08
0.17
0.9
E(r)
0.16100
market beta=1
r_f
r_m
beta
sell short the stock because it is overpriced
(a) T
Chapter 9: The Capital Asset Pricing Model
1.
E (rP ) rf P [ E (rM ) rf ]
.18 .06 P [.14 .06] P
3.
.12
1.5
.08
a.
False. = 0 implies E(r) = rf , not zero.
b.
False. Investors require a risk premium
Valuing Interest Rate and Currency Swaps
Two Valuation Methods can be used for
Interest Rate Swaps
An interest rate swap is worth zero or close to zero when it is
first initiated.
After it has been