Stochastic Models, Homework 2, 9/28/16
Ch 2: 43, 44; Ch 3: 37, 38, 39, 40, 41, 44, 50, 74
Problem 2.43
The value of is binary and is 1 if a red ball is picked up before any black is chosen and 0 otherwise.
Given that we seek the total number of red balls
Stochastic Models, Homework 5, 10/11/16
Ch 4: 2,3,5,6,20,24,25,35,45,46
Problem 4.2
There are a total of eight states. Allow the ordered tuple (1 , 2 , 3 ) to define a given a given state
where 1 is the weather two days ago, 2 is the weather yesterday, an
Stochastic Models, Homework 1, 9/23/16
Ch 1: 36, 37, 43, 44; Ch 2: 20, 21, 30, 36, 37
Problem 1.36
This is an example of branching probability and combined probability within the branching possibilities;
basically, we first choose a box and then choose a
Stochastic Models, Homework 3, 10/11/16
Ch 5: 4, 6, 22, 43, 47(a,b)
Problem 5.4a
If the service time is definitely ten minutes, then there is no way that person A will remain in the post
office after the other two have left. This is because people A and B
Stochastic Models, Homework 1, 9/23/16
Ch 1: 36, 37, 43, 44; Ch 2: 20, 21, 30, 36, 37
Problem 1.36
This is an example of branching probability and combined probability within the branching possibilities;
basically, we first choose a box and then choose a
Stochastic Models, Homework 2, 9/28/16
Ch 2: 43, 44; Ch 3: 37, 38, 39, 40, 41, 44, 50, 74
Problem 2.43
X i is binary and is 1 if a red ball is picked up before any black is chosen and 0 otherwise.
The value of
Given that we seek the total number of red ba
Stochastic Models, Homework 5, 10/11/16
Ch 4: 2,3,5,6,20,24,25,35,45,46
Problem 4.2
There are a total of eight states. Allow the ordered tuple
X 1 is the weather two days ago,
where
( X1 , X2 , X3)
to define a given a given state
X 2 is the weather yester
Stochastic Models, Homework 4, 10/11/16
Ch 5: 44, 50, 85, 86
Problem 5.44a
This is the same as asking the probability that there will be no cars for the next T units right now which
in turn analogous to saying that the next vehicle will arrive T or more u
Stochastic Models, Homework 3, 10/11/16
Ch 5: 4, 6, 22, 43, 47(a,b)
Problem 5.4a
If the service time is definitely ten minutes, then there is no way that person A will remain in the post
office after the other two have left. This is because people A and B
Stochastic Models, Homework 4, 10/11/16
Ch 5: 44, 50, 85, 86
Problem 5.44a
This is the same as asking the probability that there will be no cars for the next T units right now which in
turn analogous to saying that the next vehicle will arrive T or more u
ll Calendars El Alerts 3] Export  4] Settings Economic Calendars
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IEOR E4404
Solution to Assignment 5
2015 Spring
1. Suppose that you wish to estimate = E [X], and that we design a control variate estimator of
the form
n
1X
Z n ( ) =
Zi ( ),
n i=1
where Zi ( ) = Xi + (Yi ), Xi are i.i.d. copies of X, Yi are i.i.d. copie
Simulation
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IEOR E4404
Solution to Assignment 4
2015 Spring
1. Consider a life insurance company that works as follows. Customers arrive according to a Poisson
process with rate , and let the interarrival times be X1 , X2 , . The ith individual stays in the system
f
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IEOR 4403 Quantitative Corporate Finance
Lecture 2: More accounting
Sept 13, 2016
Rodney SunadaWong
Material from Hughes, Ayers, Hoskin 2004
Shoutouts to Cyrus Mohebbi, Sarita Sheth
1
Review concepts from lecture 1
1.
Identify three types of business ac
IEOR 4403 Quantitative Corporate Finance
Lecture 3: Even more accounting
Sept 20, 2016
Rodney SunadaWong
Material from Hughes, Ayers, Hoskin 2004
Shoutouts to Cyrus Mohebbi, Sarita Sheth
1
Hughes, Ayers, Hoskin Ch. 4
Income Measurement and Reporting
2
T
IEOR 4403 Quantitative Corporate Finance
Lecture 1: Sept 6, 2016
Rodney SunadaWong
1
What is ACCOUNTING?
The language of business
The process of identifying, recording, summarizing, and reporting
economic information to decision makers
Public versus P
Lecture 3 Part II
Univariate Time Series Analysis
Wold Theorem and Reducedform Representations
Algorithmic Trading
Instructor: Iraj Kani
Univariate Time Series Analysis
In part II of this lecture we will present a brief introduction to univariate time se
FOMC STATEMENTS: SIDEBYSIDE
Sept. 21, 2016
July 27, 2016
Information received since the Federal Open Market Committee met
in July indicates that the labor market has continued to
strengthen and growth of economic activity has picked up from
the modest p
For release at 2:00 p.m., EDT, September 21, 2016
Economic projections of Federal Reserve Board members and Federal Reserve Bank presidents under
their individual assessments of projected appropriate monetary policy, September 2016
Advance release of tabl
Lecture 2
Market Microstructure Fundamentals
Institutions and Mechanisms of Securities Trading
Algorithmic Trading
Instructor: Ken Gleason
Market Microstructure Fundamentals
In this lecture we will discuss some basic concepts of cash equity market structu
Lecture 3 Part I
Modeling Security Price Dynamics
The Roll Model of Trade Prices
Algorithmic Trading
Instructor: Iraj Kani
Modeling Security Price Dynamics
In part I of this lecture we will discuss microstructure perspectives of price dynamics and
examine
September 21, 2016
Bank of Japan
New Framework for Strengthening Monetary Easing:
"Quantitative and Qualitative Monetary Easing with Yield Curve Control"
1.
At the Monetary Policy Meeting held today, the Policy Board of the Bank of Japan conducted
a compr