Homework 5:
In this homework we will investigate characteristics of the multivariate microstructure
models of Richardson and Roomans (MRR) in terms of their vector moving average
(VMA) and vector mixed autoregressive moving average (VARMA) forms. We will
Lecture 7 Part I
Univariate Random-Walk Decomposition
Identification of Efficient Price and Spread
Components of Transaction Prices
Algorithmic Trading
Instructor: Iraj Kani
Univariate Random-Walk Decomposition
In this lecture we will examine the problem
Homework 2:
In this homework we will examine certain extensions of the Roll model of transaction
prices in which its assumptions of absence of serial correlation (or independence) of trade
directions, and the absence of correlation between trade direction
Chapter 3
LP Models: Asset/Liability
Cash Flow Matching
3.1
Short Term Financing
Corporations routinely face the problem of financing short term cash commitments. Linear programming can help in figuring out an optimal combination of financial instruments
Homework 4:
In this homework we will examine certain variants of the strategic trade model in which
noise traders are partially informed, informed traders information is partial, and brokers
may mimic profitable strategies of the informed traders. We will
Homework 3:
In this homework we will examine MA and AR representations of the trade prices based
on Wold representation theorem from reported transaction data. We will also examine
certain variants of the sequential trade model in which informed traders d
IEOR E4706 Foundations of Financial Engineering
Martin Haugh
Due: Friday 16 September 2016
Solutions to Assignment 1
1. (Q12.1, Luenberger 2nd ed.)
The current price of gold is $412 per ounce. The storage cost is $2 per ounce per year, payable
quarterly i
IEOR E4706 Foundations of Financial Engineering
Martin Haugh
Due: 5pm Wednesday 12th October 2016
Assignment 4 (Mandatory)
1. (A Surprising Result?) Consider an N -period binomial model for a non-dividend paying
stock where the true probability of an up-m
IEOR E4706 Foundations of Financial Engineering
Martin Haugh
Due: 5pm Tuesday 4th October 2016
Assignment 3 (Mandatory)
The Examples discussed in the questions below refer to the examples in the Martingale Pricing
Theory in Discrete-Time and Discrete-Spac
IEOR E4706 Foundations of Financial Engineering
Martin Haugh
Due: 5pm Friday 23rd September 2016
Assignment 2 (Mandatory)
Examples 1, 2 and 7 in the questions below refer to the examples in the Martingale Pricing Theory
in Discrete-Time and Discrete-Space
95] News v 9."? Settings World Equity Indices
Standard Y '|:='-. '-': ' F3.;-: 3. t. _-.-. L ~'-'-'~'-_ Em: ' m ' En '
1) Americas 2033* Net Chg %Chg 1. 111.51 Time %Ytd
lLE-IJ . - - Ei ' " .- +25.?5-o' " - " cfw_'6
11- : v " ' '-_- +18.34% ' -
132 - ' '
Homework 1:
In this homework we will conduct various empirical and statistical analysis of the
reported transaction data (for Apple Inc.) with different frequencies (daily, hourly, 5minute, etc.). The required data for this analysis can be found in the fi
Lecture 1 Part II
Market Microstructure Fundamentals
Institutions and Mechanisms of Securities Trading
Topics in Quantitative Finance: Algorithmic Trading
Instructor: Iraj Kani
Market Microstructure Fundamentals
In part II of this lecture we will survey o
Lecture 1 Part I
Market Microstructure Fundamentals
Prices, Markets and Securities Trading
Topics in Quantitative Finance: Algorithmic Trading
Instructor: Iraj Kani
Prices, Markets and Securities Trading
In part I of this lecture we will review concepts o
Research Papers
I.
Trading Mechanisms
Macey, Jonathan R., and Maureen OHara, 1997, The law and economics of best execution,
Journal of Financial Intermediation 6, 188 223.
Reiss, Peter C., and Ingrid M. Werner, 1998, Does risk-sharing motivate interdealer
Question 1
i.
Used Excel functions to perform calculations for mean, median, standard deviation, skewness, and kurtosis.
Calculated autocorrelations by shifting the input parameters by 1, 5, and 10 days respectively. The result is as
below:
PX_LAST
366.74
Machine Learning Assignment#3
Name: Xiao Guo, Uni: xg2185
Quesiton1:
Step 1. Read the data into R as a data frame.
Step 2. Remove the last column in the data frame
Step 3. Randomly select 25% of the observations to be in the test set. Investigate the R fu