https://web.stevens.edu/hfslwiki/api.php?action=feedcontributions&user=Shiwei&feedformat=atomHanlon Financial Systems Lab Web Encyclopedia - User contributions [en]2024-03-29T13:02:53ZUser contributionsMediaWiki 1.34.2https://web.stevens.edu/hfslwiki/index.php?title=FE541_Applied_Statistics_with_Applications_in_Finance&diff=5597FE541 Applied Statistics with Applications in Finance2018-10-26T23:04:49Z<p>Shiwei: </p>
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<div>{{Template:Test_Temp<br />
|Name =<br />
<br />
[[David Starer]]<br />
<br />
|Email =<br />
<br />
dstarer@stevens.edu<br />
<br />
|Phone =<br />
<br />
|Office =<br />
<br />
|Photo_file =<br />
<br />
https://web.stevens.edu/facultyprofile/images/avatar-150x150.png<br />
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|Campus1 =<br />
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|Opening_Term_Fall_On_Camp =<br />
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X<br />
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|Opening_Term_Spring_On_Camp =<br />
<br />
X<br />
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|Opening_Term_Summer_On_Camp =<br />
<br />
X<br />
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|Opening_Term_Summer_Web_Camp =<br />
<br />
X<br />
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|Opening_Term_Spring_Web_Camp =<br />
<br />
X<br />
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|Opening_Term_Fall_Web_Camp =<br />
<br />
X<br />
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|Course_Intro =<br />
<br />
This course prepares students to employ essential ideas and reasoning of applied statistics. It teaches theoretical statistical concepts and tests the student’s understanding of them. The course provides students with a solid foundation for solving empirical problems with the ability to summarize observed uni- and multivariate data, and to calibrate statistical models.<br />
<br />
While financial applications are emphasized, the course may also serve areas of science and engineering where statistical concepts are needed. The course is designed to familiarize students with the use of R for statistical data analysis (familiarity with programming in R is assumed. See below).<br />
<br />
|Outcomes =<br />
<br />
This course will allow the students to:<br />
<br />
# Understand and summarize complex data sets through graphs and numerical measures.<br />
# Calculate estimates of parameters using fundamental statistical methods.<br />
# Measure the “goodness” of an estimator by computing confidence intervals.<br />
# Apply statistical tests to experimental observations.<br />
# Estimate and calibrate parameters of mathematical models using real data.<br />
# Study relationships between two or more random variables.<br />
# Be prepared for more advanced applied statistical courses.<br />
<br />
|Description =<br />
<br />
''' Prerequisites '''<br />
<br />
Students require sound understanding of probability gathered through an undergraduate class such as MA222 or equivalent. Also students must have the ability to program in R. Please consider taking FE515 if you are not familiar with R.<br />
<br />
''' Attendance '''<br />
<br />
Attendance is mandatory, and there may be short pop quizzes every week, starting from the second week.<br />
<br />
|Textbook =<br />
<br />
The only required textbook is Moore, McCabe, and Craig (2017).<br />
<br />
* Moore et al. (2017) will be our main textbook. Earlier editions (say back to the sixth edition) should be OK.<br />
* Greene (2012) (or other editions) will be useful for classes on Inference.<br />
* Dalgaard (2004) is a useful basic reference concentrating on the use of R in statistics.<br />
* Florescu and Tudor (2013) is useful for Probability and Estimation Methods.<br />
* James et al. (2013) will be useful for its chapter on Variable Selection. It is available free online from its authors: http://www-bcf.usc.edu/~gareth/ISL/.<br />
<br />
|Reading =<br />
<br />
Peter Dalgaard. Introductory Statistics with R. Springer, 2004.<br />
<br />
Ionut Florescu and Ciprian Tudor. Handbook of Probability. Wiley, 2013.<br />
<br />
William H. Greene. Econometric Analysis. Prentice Hall, Seventh edition, 2012.<br />
<br />
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer Verlag, 2013.<br />
<br />
David Moore, George P. McCabe, and Bruce A. Craig. Introduction to the Practice of Statistics. W. H. Freeman and Co., Ninth edition, 2017.<br />
<br />
|GradingPolicy =<br />
<br />
''' Homework '''<br />
<br />
You will be required to submit four homework assignments.<br />
<br />
All homework assignments must be submitted in R markdown (.Rmd) format, with all answers written as functions. For your information, the main markdown page is here: https://rmarkdown.rstudio.com/. A nice summary of the use of R markdown appears here: http://www.stat.cmu.edu/~cshalizi/rmarkdown/. You may wish to include mathematical expressions in your markdown code. If so, it is useful to use L A TEX, which is taught in FE505. If you wish, you may optionally submit a .pdf version of your assignment, but no other formats will be accepted.<br />
<br />
To emphasize: submission in R markdown format is mandatory. When I grade your homework, I will automatically parse your markdown code to extract your functions. I will run your functions with test data to confirm that they work and provide the correct results.<br />
<br />
Late assignments will not be accepted unless you inform me of your circumstances before the assignment is due, and I grant you an extension. I will only grant extensions for serious medical or compassionate reasons. You will not receive an extension just because your computer fails or the network goes down at an inconvenient time.<br />
<br />
''' Examination and Project '''<br />
<br />
There will be an in-class, closed-book, hand-written, mid-term examination. This will test your understanding of the basic concepts. There will also be a take-home final project that will test your ability to put theory into practice.<br />
<br />
For the project, you will work in groups of three to propose, design, and analyze a research topic that contains a significant data component and is applicable to your primary field of study. The project must use statistical methods that are taught in this course. Before you spend more than a few hours of work on your project, you must get my formal approval of your topic.<br />
<br />
''' Grade distribution '''<br />
<br />
Your final grade will be determined by your performance in the homework, mid-term examination, project, and spot quizzes, as weighted below. However, I reserve the right to “curve” the grades; i.e., to adjust the grades such that they follow the usual distribution at Stevens.<br />
<br />
<pre>Assignments: 40% <br />
<br />
Midterm: 10% <br />
<br />
Final Presentation: 10% <br />
<br />
Final Project: 30%<br />
<br />
Quizzes, Class Participation: 10% </pre><br />
|LectureOutline =<br />
<br />
|Week1 =<br />
<br />
Week 1<br />
<br />
|Topic1 =<br />
<br />
Descriptive Graphical Measures.<br />
Numerical Measures.<br />
<br />
|Reading1 =<br />
<br />
Moore et al. (2017): Ch. 1<br />
Moore et al. (2017): Ch. 2<br />
<br />
|Week2 =<br />
<br />
Week 2<br />
<br />
|Topic2 =<br />
<br />
Sampling Distributions.<br />
<br />
|Reading2 =<br />
<br />
Moore et al. (2017): Ch. 5<br />
<br />
|Week3 =<br />
<br />
Week 3<br />
<br />
|Topic3 =<br />
<br />
Introduction to Inference.<br />
<br />
|Reading3 =<br />
<br />
Moore et al. (2017): Ch. 6<br />
<br />
|Week4 =<br />
<br />
Week 4<br />
<br />
|Topic4 =<br />
<br />
Inference for Distributions.<br />
<br />
|Reading4 =<br />
<br />
Moore et al. (2017): Ch. 7<br />
<br />
|Week5 =<br />
<br />
Week 5<br />
<br />
|Topic5 =<br />
<br />
Inference for Proportions.<br />
<br />
|Reading5 =<br />
<br />
Moore et al. (2017): Ch. 8<br />
<br />
|Week6 =<br />
<br />
Week 6<br />
<br />
|Topic6 =<br />
<br />
Estimation. Methods in General.<br />
<br />
# Method of Moments,<br />
# Maximum (and Conditional) Likelihood,<br />
# Bayesian estimators.<br />
<br />
|Reading6 =<br />
<br />
Greene (2012): Ch. 12<br />
Greene (2012): Ch. 13<br />
Greene (2012): Ch. 14<br />
Greene (2012): Ch. 16<br />
<br />
|Week7 =<br />
<br />
Week 7<br />
<br />
|Topic7 =<br />
<br />
Midterm Examination<br />
<br />
|Reading7 =<br />
<br />
|Week8 =<br />
<br />
Week 8<br />
<br />
|Topic8 =<br />
<br />
Analysis of Two-Way (and One-Way) Tables.<br />
Goodness of Fit Test.<br />
Independence Test.<br />
<br />
|Reading8 =<br />
<br />
Moore et al. (2017): Ch. 9<br />
<br />
|Week9 =<br />
<br />
Week 9<br />
<br />
|Topic9 =<br />
<br />
Simple Linear Regression.<br />
Least Squares Method.<br />
Analysis and Testing.<br />
Prediction.<br />
<br />
|Reading9 =<br />
<br />
Moore et al. (2017): Ch. 10<br />
<br />
|Week10 =<br />
<br />
Week 10<br />
<br />
|Topic10 =<br />
<br />
Multiple Regression.<br />
Confidence intervals.<br />
ANOVA Table, Multiple R 2 , Residuals.<br />
<br />
|Reading10 =<br />
<br />
Moore et al. (2017): Ch. 11<br />
<br />
|Week11 =<br />
<br />
Week 11<br />
<br />
|Topic11 =<br />
<br />
Selection of Variables.<br />
Correlation Analysis<br />
Variance Inflation Factors.<br />
Nonlinear Regression.<br />
Generalized Additive Models.<br />
<br />
|Reading11 =<br />
<br />
James et al. (2013): Ch. 6<br />
<br />
|Week12 =<br />
<br />
Week 12<br />
<br />
|Topic12 =<br />
<br />
Analysis of Variance (ANOVA) Models.<br />
Two-Way Analysis of Variance.<br />
Expansion to Mixture Models.<br />
Analysis of Covariance.<br />
<br />
|Reading12 =<br />
<br />
Moore et al. (2017): Ch. 12<br />
Moore et al. (2017): Ch. 13<br />
<br />
|Week13 =<br />
<br />
Week 13<br />
<br />
|Topic13 =<br />
<br />
Logistic regression.<br />
<br />
|Reading13 =<br />
<br />
Moore et al. (2017): Ch. 14<br />
<br />
|Week14 =<br />
<br />
Week 14<br />
<br />
|Topic14 =<br />
<br />
Bootstrap Method and Permutation Tests.<br />
Cross-Validation Methods.<br />
<br />
|Reading14 =<br />
<br />
Moore et al. (2017): Ch. 16<br />
<br />
}}</div>Shiweihttps://web.stevens.edu/hfslwiki/index.php?title=FE540_Probability_theory_for_Financial_Engineering&diff=5594FE540 Probability theory for Financial Engineering2018-10-25T15:04:58Z<p>Shiwei: </p>
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<div>{{Template:Test_Temp<br />
<br />
|Name =<br />
[[Ionut Florescu]]<br />
|Email =<br />
ifloresc@stevens.edu<br />
|Phone =<br />
<ol start="201" style="list-style-type: decimal;"><br />
<li>216-5452</li></ol><br />
|Office =<br />
Babbio 544<br />
|Photo_file =<br />
https://kolmogorov.fsc.stevens.edu/cms/images/stories/faculty_photo/Ionut_Florescu.jpg<br />
|Office_Hour =<br />
By appointment<br />
|Classroom =<br />
|Classtime =<br />
|Opening_Term1 =<br />
Fall<br />
|Opening_Term2 =<br />
Spring<br />
|Campus1 =<br />
On Campus, Web Campus<br />
|Campus2 =<br />
On Campus, Web Campus<br />
|Opening_Term_Fall_On_Camp =<br />
X<br />
|Opening_Term_Spring_On_Camp =<br />
X<br />
|Opening_Term_Summer_On_Camp =<br />
|Opening_Term_Summer_Web_Camp =<br />
|Opening_Term_Spring_Web_Camp =<br />
X<br />
|Opening_Term_Fall_Web_Camp =<br />
X<br />
|Course_Intro =<br />
The goals of this course is to provide FE and FA students with the necessary probability theory background to ensure a better performance in the rest of the FE/FA programs. In particular the concepts of sigma fields or algebras are not covered in undergraduate probability courses these are fundamental for stochastic processes and for properly define random variables. The students will learn to perform probability reasoning and fundamental probability calculations to help them with the derivations in statistics, time series, and stochastic calculus.<br />
|Outcomes =<br />
|Description =<br />
|Textbook =<br />
The main textbook will be:<br />
* [FT] Florescu, Ionut and Tudor, Ciprian A. Handbook of Probability, Wiley, 2014, ISBN 1118593146, 9781118593141.<br />
I chose to use this book as the primary textbook for two reasons. Each chapter is supposed to be more or less self contained and it contains many details that I believe are useful. Second, each chapter has a section with exercises split into two, First set of problems have solutions while the second set does not. I will assign exercises that do not have solutions but you should be working through the ones that have solutions for practice.<br />
We will be using two other textbooks.<br />
* [G] Ghahramani, Saeed Fundamentals of Probability: with Stochastic Processes, Third Edition, Chapman and Hill/CRC, Nov. 2015, ISBN 9781498755016<br />
This is an undergraduate textbook and it is very useful for those of you who did not do a serious probability class in undergraduate. The book explains very well the basic probability distributions and concepts. I will be using exercises from the book and you should use it as a source of material and problems.<br />
Finally,<br />
* [F] Florescu, Ionut Probability and Stochastic Processes, Wiley, Oct. 2014, ISBN-13: 978-0470624555, ISBN-10: 047062455<br />
This book has more material than the main textbook but it isn’t as detailed which is why I am using the handbook as the main text. However, this book has a second part about stochastic processes which is I believe very useful for future. I am referring in particular to Markov chains and Markov processes, Poisson process and the Brownian motion.<br />
|Reading =<br />
|GradingPolicy =<br />
The final grade will be determined upon the student’s performance in the course. We will have multiple assignments and possibly quizzes throughout the course. Most of the grade will be coming from the in class midterm as well as from the final.<br />
Only use the .pdf format when submitting files online. If specified in class you can turn in handwritten assignment in the traditional way. You should be able to transform any document into a pdf file. You can use Adobe Acrobat - should be free to Stevens students as far as I know (please call the students help desk), or a simple alternative: use a pdf printer driver. I write all my documents in LATEX and that typesetting program produces pdf files. A simple alternative (using any typesetting program): search on google for a driver that would print to a pdf file. Such drivers are generally free.<br />
Late assignments will not be accepted under any circumstances without prior notice and permission of the instructor. If outside circumstances are affecting your ability to perform in the course, you must contact your instructor before you fall behind.<br />
Generally the grade distribution follows the following percentages.<br />
=== Grade distribution ===<br />
<pre>Assignments: 30%<br />
Midterm: 25%<br />
Final: 40%<br />
Quizzes, class participation: 5%</pre><br />
|LectureOutline =<br />
|Week1 =<br />
Week 1<br />
|Topic1 =<br />
Axioms of Probability, Sample Spaces, Examples Combinatorial Analysis, Counting Permutations, Combinations, Binomial Coefficient<br />
|Reading1 =<br />
F-1, FT-1, G-1,2<br />
|Week2 =<br />
Week 2<br />
|Topic2 =<br />
Conditional Probability and Independence, Law of Total Probability, Bayes Theorem, Applications<br />
|Reading2 =<br />
FT-2, F-2, G-3<br />
|Week3 =<br />
Week 3<br />
|Topic3 =<br />
Random Variables: Generalities<br />
|Reading3 =<br />
FT-3<br />
|Week4 =<br />
Week 4<br />
|Topic4 =<br />
Discrete Random variables, examples<br />
|Reading4 =<br />
FT-4, G-4,5<br />
|Week5 =<br />
Week 5<br />
|Topic5 =<br />
Continuous Random variables, Examples<br />
|Reading5 =<br />
FT-5, G-6,7<br />
|Week6 =<br />
Week 6<br />
|Topic6 =<br />
Generating Random variables. Catching up.<br />
|Reading6 =<br />
FT-6, F-3<br />
|Week7 =<br />
Week 7<br />
|Topic7 =<br />
MIDTERM<br />
|Reading7 =<br />
|Week8 =<br />
Week 8<br />
|Topic8 =<br />
Random vectors, Joint distribution<br />
|Reading8 =<br />
FT-7, F-4<br />
|Week9 =<br />
Week 9<br />
|Topic9 =<br />
Conditional distribution, Conditional expectation<br />
|Reading9 =<br />
G-8,9<br />
|Week10 =<br />
Week 10<br />
|Topic10 =<br />
Moment Generating Function, Characteristic Function<br />
|Reading10 =<br />
FT-8,9,F-6<br />
|Week11 =<br />
Week 11<br />
|Topic11 =<br />
Gaussian Random Vectors, Catch up<br />
|Reading11 =<br />
FT-10<br />
|Week12 =<br />
Week 12<br />
|Topic12 =<br />
Statistical Inference, Limit Theorems<br />
|Reading12 =<br />
FT-11,12, F-7,8<br />
|Week13 =<br />
Week 13<br />
|Topic13 =<br />
Poisson Process and Markov Chain<br />
|Reading13 =<br />
F-10,12 G-12.2,12.4<br />
|Week14 =<br />
Week 14<br />
|Topic14 =<br />
Brownian motion<br />
|Reading14 =<br />
F-15, G-12.5<br />
<br />
}}</div>Shiweihttps://web.stevens.edu/hfslwiki/index.php?title=BIA660_Web_Analytics&diff=5593BIA660 Web Analytics2018-10-25T14:15:27Z<p>Shiwei: </p>
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<div>{{Template:Test_Temp<br />
|Name =<br />
<br />
[[Theodoros Lappas]]<br />
<br />
|Email =<br />
<br />
tlappas@stevens.edu<br />
<br />
|Phone =<br />
<br />
|Office =<br />
<br />
Babbio 639<br />
<br />
|Photo_file =<br />
<br />
https://kolmogorov.fsc.stevens.edu/cms/images/stories/crop/1897_Lappas.jpeg<br />
<br />
|Office_Hour =<br />
<br />
|Classroom =<br />
<br />
|Classtime =<br />
<br />
|Opening_Term =<br />
<br />
Fall On Campus, Spring On Campus<br />
<br />
|Opening_Term1 =<br />
<br />
Fall<br />
<br />
|Opening_Term2 =<br />
<br />
Spring<br />
<br />
|Campus1 =<br />
<br />
On Campus<br />
<br />
|Campus2 =<br />
<br />
On Campus<br />
<br />
|Opening_Term_Fall_On_Camp =<br />
<br />
X<br />
<br />
|Opening_Term_Spring_On_Camp =<br />
<br />
X<br />
<br />
|Opening_Term_Summer_On_Camp =<br />
<br />
|Opening_Term_Summer_Web_Camp =<br />
<br />
|Opening_Term_Spring_Web_Camp =<br />
<br />
|Opening_Term_Fall_Web_Camp =<br />
<br />
|Course_Intro =<br />
<br />
The course covers:<br />
<br />
* Introduction to Python<br />
* Data collection from the Web<br />
* Parsing and cleaning of structured and unstructured text<br />
* Text mining<br />
* Introduction to Natural Language Processing (NLP)<br />
* Topic Modeling<br />
* Supervised and unsupervised learning algorithms<br />
<br />
Students will be organized to teams of 4-5 people. Student teams will work on a large project that will determine the largest percentage of the class grade. A teammate evaluation survey will be conducted twice during the semester and will contribute to the class grade.<br />
<br />
=== Lecture structure: ===<br />
<br />
For the first 30-40 minutes of the lecture the instructor presents a new concept to the students, typically via the presentation and discussion of a python script that solves a practical problem. The students are then given a relevant assignment that they must complete in-class. During this time, the instructor assists the students and provides hints toward the solution of the assignment. These assignments contribute to the class grade.<br />
<br />
|Outcomes =<br />
<br />
|Description =<br />
<br />
|Textbook =<br />
<br />
Readings will be assigned each week. Links will be provided on the course website.<br />
<br />
|Reading =<br />
<br />
|GradingPolicy =<br />
<br />
<pre>In-class Assignments 60 points<br />
Final Team Project 35 points<br />
Peer Evaluations 25 points<br />
TOTAL 120 points</pre><br />
(A student needs 93 points for an A) Team Evaluations are mapped to a multipler in [0,1] which is then applied to the team's project grade to compute individual student grades.<br />
<br />
|LectureOutline =<br />
<br />
|Week1 =<br />
<br />
Week 1<br />
<br />
|Topic1 =<br />
<br />
Orientation Week<br />
<br />
|Reading1 =<br />
<br />
|Week2 =<br />
<br />
Week 2<br />
<br />
|Topic2 =<br />
<br />
Introduction to Python I<br />
<br />
|Reading2 =<br />
<br />
|Week3 =<br />
<br />
Week 3<br />
<br />
|Topic3 =<br />
<br />
Introduction to Python II<br />
<br />
|Reading3 =<br />
<br />
|Week4 =<br />
<br />
Week 4<br />
<br />
|Topic4 =<br />
<br />
Using Python for web scraping I<br />
<br />
|Reading4 =<br />
<br />
|Week5 =<br />
<br />
Week 5<br />
<br />
|Topic5 =<br />
<br />
Using Python for web scraping II<br />
<br />
|Reading5 =<br />
<br />
|Week6 =<br />
<br />
Week 6<br />
<br />
|Topic6 =<br />
<br />
Tet Mining I (Regex)<br />
<br />
|Reading6 =<br />
<br />
|Week7 =<br />
<br />
Week 7<br />
<br />
|Topic7 =<br />
<br />
Text Mining II (NLTK)<br />
<br />
|Reading7 =<br />
<br />
|Week8 =<br />
<br />
Week 8<br />
<br />
|Topic8 =<br />
<br />
Text Mining III (Opinion Mining)<br />
<br />
|Reading8 =<br />
<br />
|Week9 =<br />
<br />
Week 9<br />
<br />
|Topic9 =<br />
<br />
Supervised Learning I<br />
<br />
|Reading9 =<br />
<br />
|Week10 =<br />
<br />
Week 10<br />
<br />
|Topic10 =<br />
<br />
Supervised Learning II<br />
<br />
|Reading10 =<br />
<br />
|Week11 =<br />
<br />
Week 11<br />
<br />
|Topic11 =<br />
<br />
Supervised Learning III<br />
<br />
|Reading11 =<br />
<br />
|Week12 =<br />
<br />
Week 12<br />
<br />
|Topic12 =<br />
<br />
Special Topic I (Unsupervised Learning, Clustering, Visualization, Topic Modeling)<br />
<br />
|Reading12 =<br />
<br />
|Week13 =<br />
<br />
Week 13<br />
<br />
|Topic13 =<br />
<br />
Special Topic II<br />
<br />
|Reading13 =<br />
<br />
|Week14 =<br />
<br />
Week 14<br />
<br />
|Topic14 =<br />
<br />
Project Presentations<br />
<br />
|Reading14 =<br />
<br />
}}</div>Shiweihttps://web.stevens.edu/hfslwiki/index.php?title=FE641_Multivariate_Statistics_and_Advanced_Time_Series_in_Finance&diff=5592FE641 Multivariate Statistics and Advanced Time Series in Finance2018-10-25T00:55:01Z<p>Shiwei: </p>
<hr />
<div>{{Template:Test_Temp<br />
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|Email =<br />
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|Phone =<br />
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|Office =<br />
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|Photo_file =<br />
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|Classroom =<br />
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|Classtime =<br />
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|Opening_Term =<br />
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|Opening_Term1 =<br />
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|Opening_Term2 =<br />
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|Campus1 =<br />
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|Campus2 =<br />
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|Opening_Term_Fall_On_Camp =<br />
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|Opening_Term_Spring_On_Camp =<br />
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|Opening_Term_Summer_On_Camp =<br />
<br />
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<br />
|Opening_Term_Fall_Web_Camp =<br />
<br />
|Course_Intro =<br />
<br />
The main objective is to equip students with advanced statistical analytical techniques to have better success in the financial engineering domain. This course is preparing the students to use tools whenever statistical analysis of data is involved and to provide students with a solid foundation of statistical problem solving empirical methods with the ability to summarize, and calibrate observed multivariate data.<br />
<br />
|Outcomes =<br />
<br />
A student graduating this course:<br />
<br />
# Will understand and summarize complex data sets through graphs and numerical measures<br />
# Will know how good estimators are by providing confidence intervals or approximate confidence intervals<br />
# Will know how to estimate and calibrate parameters of mathematical models using real data<br />
# Assess relationships between multiple random variables and stochastic processes<br />
# Use the techniques learned to study multivariate data from most domains.<br />
# Will gain experience in analyzing multivariate time series data<br />
# Will gain knowledge of multivariate time series models, including vector AR and ARMA models with exogenous variables<br />
# Will have a good understanding of co-integration and error-correction models<br />
# Will have a good understanding of factor models and their applications<br />
# Will have a good understanding of structural specification of a linear vector process<br />
# Will be able to model multivariate volatility<br />
<br />
|Description =<br />
<br />
The course is an advanced statistics course designed to incorporate the newest areas of statistics research and applications in the Stevens Institute curriculum. Topics include multivariate statistics methods such as principal components, independent components, factor analysis, discriminant analysis, mixture models, and lasso regression. Advanced topics in time series such as Granger causality, vector auto regressive models, co-integration, and error corrected models, VARMA models and multivariate volatility models will be presented.<br />
<br />
|Textbook =<br />
<br />
[1] Multivariate Time Series Analysis with R and Financial Applications by Ruey S. Tsay (2014), Wiley: ISBN: 978-1118617908.<br />
<br />
[2] Modeling financial time series with S-Plus®, by E. Zivot, and Wang, J. (2007), Springer Science &amp; Business Media.<br />
<br />
[3] Analysis of Financial Time Series, 3rd Ed., Tsay (2010), Wiley.<br />
<br />
|Reading =<br />
<br />
Materials:<br />
<br />
# Time Series Analysis: Forecasting and Control, 4th ed., by Box, Jenkins and Reinsel (2008), Wiley. Chapters 10 and 11.<br />
# A Course in Time Series Analysis by Pena, Tiao and Tsay (2001), John-Wiley. Chapters 14 and 15.<br />
# Time Series Analysis by J. Hamilton (1994), Princeton University Press. Chapters 10, 11, 13, 18, 19 &amp; 20.<br />
# Time Series Analysis by State Space Models by Durbin and Koopman (2001), Oxford University Press.<br />
# Elements of Multivariate Time Series Analysis by G. C. Reinsel (1993), SpringerVerlag.<br />
# Introductory Statistics with R, by Peter Daalgard, Springer; (2004). Corr. 3d printing edition January 9.<br />
# Probability and Stochastic Processes, by Ionut Florescu, (2014) Wiley, ISBN: 978-0-470-62455-5<br />
# An Introduction to Statistical Learning with Applications in R by G. James, D. Witteb, T. Hastie, R. Tibshirani, (2013), Springer, ISBN: 1-4614-7137-0<br />
# New Introduction to Multiple Time Series Analysis by H. Lutkepohl, SpringerVerlag, (2005). ISBN: 3-540-26239-3.<br />
# Applied Multivariate Statistical Analysis by R.A. Johnson and D.W. Wichern, 6th ed., (2007) Prentice Hall. ISBN 0-13-187715-1<br />
# Nonlinear Time Series: Nonparametric and Parametric Methods, J Fan and Q. Yao. New York: Springer-Verlag, 2003. ISBN 0-387-95170-9. <br />
<br />
|GradingPolicy =<br />
<br />
<pre>HW 40% <br />
<br />
Midterm 20% <br />
<br />
Final Exam 40% </pre><br />
|LectureOutline =<br />
<br />
|Week1 =<br />
<br />
Week 1<br />
<br />
|Topic1 =<br />
<br />
Review of Statistical concepts. Estimators, Confidence intervals, Testing, Two way tables, Regression, ANOVA, logistic regression<br />
<br />
|Reading1 =<br />
<br />
Lecture notes<br />
<br />
|Week2 =<br />
<br />
Week 2<br />
<br />
|Topic2 =<br />
<br />
Principal Component Analysis, Independent Component Analysis, Scree plot, Returns and from PCA<br />
<br />
|Reading2 =<br />
<br />
Ch. 9 in [3] and notes<br />
<br />
|Week3 =<br />
<br />
Week 3<br />
<br />
|Topic3 =<br />
<br />
Factor models for asset returns. BARRA type models. Application to portfolio optimization.<br />
<br />
|Reading3 =<br />
<br />
Ch. 15 in [2], Ch. 6 in[1]<br />
<br />
|Week4 =<br />
<br />
Week 4<br />
<br />
|Topic4 =<br />
<br />
Discriminant Analysis and Mixture models. Introduction to classification. Regime switching and Hidden Markov Chains.<br />
<br />
|Reading4 =<br />
<br />
Lecture notes<br />
<br />
|Week5 =<br />
<br />
Week 5<br />
<br />
|Topic5 =<br />
<br />
Lasso Regression and related sparse regression techniques. Applications to finance. Copula methods and applications to risk management.<br />
<br />
|Reading5 =<br />
<br />
Lecture notes and Ch. 19 in [2]<br />
<br />
|Week6 =<br />
<br />
Week 6<br />
<br />
|Topic6 =<br />
<br />
Bayesian Statistics Methods. Hierarchical Bayes. Conjugate prior/posterior techniques. Sequential Bayes. Applications to estimating parameters for financial models.<br />
<br />
|Reading6 =<br />
<br />
Ch. 8 in [7] and Ch. 12 in [3] lecture notes<br />
<br />
|Week7 =<br />
<br />
Week 7<br />
<br />
|Topic7 =<br />
<br />
Midterm Examination<br />
<br />
|Reading7 =<br />
<br />
|Week8 =<br />
<br />
Week 8<br />
<br />
|Topic8 =<br />
<br />
Review of Univariate Time series ARIMA and ARCH type models.<br />
<br />
|Reading8 =<br />
<br />
Notes and Ch. 1 in [1]<br />
<br />
|Week9 =<br />
<br />
Week 9<br />
<br />
|Topic9 =<br />
<br />
Time series with an external component. ARIMAX models. Granger Causality.<br />
<br />
|Reading9 =<br />
<br />
Ch. 2 in [1] and notes<br />
<br />
|Week10 =<br />
<br />
Week 10<br />
<br />
|Topic10 =<br />
<br />
Long Memory time series models. Hurst parameter estimation, R/S analysis. ARFIMA/FARIMA, FIGARCH models.<br />
<br />
|Reading10 =<br />
<br />
Ch. 8 in [2]<br />
<br />
|Week11 =<br />
<br />
Week 11<br />
<br />
|Topic11 =<br />
<br />
Vector autoregressive and moving average VAR, VARMA models<br />
<br />
|Reading11 =<br />
<br />
Ch. 3 in [1], Ch. 11 in [2]<br />
<br />
|Week12 =<br />
<br />
Week 12<br />
<br />
|Topic12 =<br />
<br />
Unit root non-stationarity, Co-integration. Co-integrated VAR and VARMA. Error corrected models.<br />
<br />
|Reading12 =<br />
<br />
Ch. 5 in [1] and Ch. 12 in [2]<br />
<br />
|Week13 =<br />
<br />
Week 13<br />
<br />
|Topic13 =<br />
<br />
Multivariate volatility models. Multivariate GARCH. Cholesky decomposition and volatility modeling.<br />
<br />
|Reading13 =<br />
<br />
Ch. 7 in [1]<br />
<br />
|Week14 =<br />
<br />
Week 14<br />
<br />
|Topic14 =<br />
<br />
Multivariate volatility (cont.). Go-GARCH, Dynamic orthogonal components, principal volatility components<br />
<br />
|Reading14 =<br />
<br />
Ch. 13 in [2]<br />
<br />
}}</div>Shiweihttps://web.stevens.edu/hfslwiki/index.php?title=FE641_Multivariate_Statistics_and_Advanced_Time_Series_in_Finance&diff=5591FE641 Multivariate Statistics and Advanced Time Series in Finance2018-10-25T00:44:10Z<p>Shiwei: </p>
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|Opening_Term_Fall_Web_Camp =<br />
<br />
|Course_Intro =<br />
<br />
The main objective is to equip students with advanced statistical analytical techniques to have better success in the financial engineering domain. This course is preparing the students to use tools whenever statistical analysis of data is involved and to provide students with a solid foundation of statistical problem solving empirical methods with the ability to summarize, and calibrate observed multivariate data.<br />
<br />
|Outcomes =<br />
<br />
A student graduating this course:<br />
<br />
# Will understand and summarize complex data sets through graphs and numerical measures<br />
# Will know how good estimators are by providing confidence intervals or approximate confidence intervals<br />
# Will know how to estimate and calibrate parameters of mathematical models using real data<br />
# Assess relationships between multiple random variables and stochastic processes<br />
# Use the techniques learned to study multivariate data from most domains.<br />
# Will gain experience in analyzing multivariate time series data<br />
# Will gain knowledge of multivariate time series models, including vector AR and ARMA models with exogenous variables<br />
# Will have a good understanding of co-integration and error-correction models<br />
# Will have a good understanding of factor models and their applications<br />
# Will have a good understanding of structural specification of a linear vector process<br />
# Will be able to model multivariate volatility<br />
<br />
|Description =<br />
<br />
The course is an advanced statistics course designed to incorporate the newest areas of statistics research and applications in the Stevens Institute curriculum. Topics include multivariate statistics methods such as principal components, independent components, factor analysis, discriminant analysis, mixture models, and lasso regression. Advanced topics in time series such as Granger causality, vector auto regressive models, co-integration, and error corrected models, VARMA models and multivariate volatility models will be presented.<br />
<br />
|Textbook =<br />
<br />
[1] Multivariate Time Series Analysis with R and Financial Applications by Ruey S. Tsay (2014), Wiley: ISBN: 978-1118617908.<br />
<br />
[2] Modeling financial time series with S-Plus®, by E. Zivot, and Wang, J. (2007), Springer Science &amp; Business Media.<br />
<br />
[3] Analysis of Financial Time Series, 3rd Ed., Tsay (2010), Wiley.<br />
<br />
|Reading =<br />
<br />
Materials:<br />
<br />
# Time Series Analysis: Forecasting and Control, 4th ed., by Box, Jenkins and Reinsel (2008), Wiley. Chapters 10 and 11.<br />
# A Course in Time Series Analysis by Pena, Tiao and Tsay (2001), John-Wiley. Chapters 14 and 15.<br />
# Time Series Analysis by J. Hamilton (1994), Princeton University Press. Chapters 10, 11, 13, 18, 19 &amp; 20.<br />
# Time Series Analysis by State Space Models by Durbin and Koopman (2001), Oxford University Press.<br />
# Elements of Multivariate Time Series Analysis by G. C. Reinsel (1993), SpringerVerlag.<br />
# Introductory Statistics with R, by Peter Daalgard, Springer; (2004). Corr. 3d printing edition January 9.<br />
# Probability and Stochastic Processes, by Ionut Florescu, (2014) Wiley, ISBN: 978-0-470-62455-5<br />
# An Introduction to Statistical Learning with Applications in R by G. James, D. Witteb, T. Hastie, R. Tibshirani, (2013), Springer, ISBN: 1-4614-7137-0<br />
# New Introduction to Multiple Time Series Analysis by H. Lutkepohl, SpringerVerlag, (2005). ISBN: 3-540-26239-3.<br />
# Applied Multivariate Statistical Analysis by R.A. Johnson and D.W. Wichern, 6th ed., (2007) Prentice Hall. ISBN 0-13-187715-1<br />
# Nonlinear Time Series: Nonparametric and Parametric Methods, J Fan and Q. Yao. New York: Springer-Verlag, 2003. ISBN 0-387-95170-9. <br />
<br />
|GradingPolicy =<br />
<br />
=== Grade distribution ===<br />
<br />
<pre>HW 40% <br />
<br />
Midterm 20% <br />
<br />
Final Exam 40% </pre><br />
|LectureOutline =<br />
<br />
|Week1 =<br />
<br />
Week 1<br />
<br />
|Topic1 =<br />
<br />
Review of Statistical concepts. Estimators, Confidence intervals, Testing, Two way tables, Regression, ANOVA, logistic regression<br />
<br />
|Reading1 =<br />
<br />
Lecture notes<br />
<br />
|Week2 =<br />
<br />
Week 2<br />
<br />
|Topic2 =<br />
<br />
Principal Component Analysis, Independent Component Analysis, Scree plot, Returns and from PCA<br />
<br />
|Reading2 =<br />
<br />
Ch. 9 in [3] and notes<br />
<br />
|Week3 =<br />
<br />
Week 3<br />
<br />
|Topic3 =<br />
<br />
Factor models for asset returns. BARRA type models. Application to portfolio optimization.<br />
<br />
|Reading3 =<br />
<br />
Ch. 15 in [2], Ch. 6 in[1]<br />
<br />
|Week4 =<br />
<br />
Week 4<br />
<br />
|Topic4 =<br />
<br />
Discriminant Analysis and Mixture models. Introduction to classification. Regime switching and Hidden Markov Chains.<br />
<br />
|Reading4 =<br />
<br />
Lecture notes<br />
<br />
|Week5 =<br />
<br />
Week 5<br />
<br />
|Topic5 =<br />
<br />
Lasso Regression and related sparse regression techniques. Applications to finance. Copula methods and applications to risk management.<br />
<br />
|Reading5 =<br />
<br />
Lecture notes and Ch. 19 in [2]<br />
<br />
|Week6 =<br />
<br />
Week 6<br />
<br />
|Topic6 =<br />
<br />
Bayesian Statistics Methods. Hierarchical Bayes. Conjugate prior/posterior techniques. Sequential Bayes. Applications to estimating parameters for financial models.<br />
<br />
|Reading6 =<br />
<br />
Ch. 8 in [7] and Ch. 12 in [3] lecture notes<br />
<br />
|Week7 =<br />
<br />
Week 7<br />
<br />
|Topic7 =<br />
<br />
Midterm Examination<br />
<br />
|Reading7 =<br />
<br />
|Week8 =<br />
<br />
Week 8<br />
<br />
|Topic8 =<br />
<br />
Review of Univariate Time series ARIMA and ARCH type models.<br />
<br />
|Reading8 =<br />
<br />
Notes and Ch. 1 in [1]<br />
<br />
|Week9 =<br />
<br />
Week 9<br />
<br />
|Topic9 =<br />
<br />
Time series with an external component. ARIMAX models. Granger Causality.<br />
<br />
|Reading9 =<br />
<br />
Ch. 2 in [1] and notes<br />
<br />
|Week10 =<br />
<br />
Week 10<br />
<br />
|Topic10 =<br />
<br />
Long Memory time series models. Hurst parameter estimation, R/S analysis. ARFIMA/FARIMA, FIGARCH models.<br />
<br />
|Reading10 =<br />
<br />
Ch. 8 in [2]<br />
<br />
|Week11 =<br />
<br />
Week 11<br />
<br />
|Topic11 =<br />
<br />
Vector autoregressive and moving average VAR, VARMA models<br />
<br />
|Reading11 =<br />
<br />
Ch. 3 in [1], Ch. 11 in [2]<br />
<br />
|Week12 =<br />
<br />
Week 12<br />
<br />
|Topic12 =<br />
<br />
Unit root non-stationarity, Co-integration. Co-integrated VAR and VARMA. Error corrected models.<br />
<br />
|Reading12 =<br />
<br />
Ch. 5 in [1] and Ch. 12 in [2]<br />
<br />
|Week13 =<br />
<br />
Week 13<br />
<br />
|Topic13 =<br />
<br />
Multivariate volatility models. Multivariate GARCH. Cholesky decomposition and volatility modeling.<br />
<br />
|Reading13 =<br />
<br />
Ch. 7 in [1]<br />
<br />
|Week14 =<br />
<br />
Week 14<br />
<br />
|Topic14 =<br />
<br />
Multivariate volatility (cont.). Go-GARCH, Dynamic orthogonal components, principal volatility components<br />
<br />
|Reading14 =<br />
<br />
Ch. 13 in [2]<br />
<br />
}}</div>Shiweihttps://web.stevens.edu/hfslwiki/index.php?title=FE641_Multivariate_Statistics_and_Advanced_Time_Series_in_Finance&diff=5590FE641 Multivariate Statistics and Advanced Time Series in Finance2018-10-25T00:41:02Z<p>Shiwei: Created page with "{{Template:Test_Temp |Name = |Email = |Phone = |Office = |Photo_file = |Office_Hour = |Classroom = |Classtime = |Opening_Term = |Opening_Term1 = |Opening_Term2 = |C..."</p>
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<br />
|Opening_Term_Spring_Web_Camp =<br />
<br />
|Opening_Term_Fall_Web_Camp =<br />
<br />
|Course_Intro =<br />
<br />
The main objective is to equip students with advanced statistical analytical techniques to have better success in the financial engineering domain. This course is preparing the students to use tools whenever statistical analysis of data is involved and to provide students with a solid foundation of statistical problem solving empirical methods with the ability to summarize, and calibrate observed multivariate data.<br />
<br />
|Outcomes =<br />
<br />
A student graduating this course:<br />
<br />
# Will understand and summarize complex data sets through graphs and numerical measures<br />
# Will know how good estimators are by providing confidence intervals or approximate confidence intervals<br />
# Will know how to estimate and calibrate parameters of mathematical models using real data<br />
# Assess relationships between multiple random variables and stochastic processes<br />
# Use the techniques learned to study multivariate data from most domains.<br />
# Will gain experience in analyzing multivariate time series data<br />
# Will gain knowledge of multivariate time series models, including vector AR and ARMA models with exogenous variables<br />
# Will have a good understanding of co-integration and error-correction models<br />
# Will have a good understanding of factor models and their applications<br />
# Will have a good understanding of structural specification of a linear vector process<br />
# Will be able to model multivariate volatility<br />
<br />
|Description =<br />
<br />
The course is an advanced statistics course designed to incorporate the newest areas of statistics research and applications in the Stevens Institute curriculum. Topics include multivariate statistics methods such as principal components, independent components, factor analysis, discriminant analysis, mixture models, and lasso regression. Advanced topics in time series such as Granger causality, vector auto regressive models, co-integration, and error corrected models, VARMA models and multivariate volatility models will be presented.<br />
<br />
|Textbook =<br />
<br />
[1] Multivariate Time Series Analysis with R and Financial Applications by Ruey S. Tsay (2014), Wiley: ISBN: 978-1118617908.<br />
<br />
[2] Modeling financial time series with S-Plus®, by E. Zivot, and Wang, J. (2007), Springer Science &amp; Business Media.<br />
<br />
[3] Analysis of Financial Time Series, 3rd Ed., Tsay (2010), Wiley.<br />
<br />
|Reading =<br />
<br />
# Time Series Analysis: Forecasting and Control, 4th ed., by Box, Jenkins and Reinsel (2008), Wiley. Chapters 10 and 11.<br />
# A Course in Time Series Analysis by Pena, Tiao and Tsay (2001), John-Wiley. Chapters 14 and 15.<br />
# Time Series Analysis by J. Hamilton (1994), Princeton University Press. Chapters 10, 11, 13, 18, 19 &amp; 20.<br />
# Time Series Analysis by State Space Models by Durbin and Koopman (2001), Oxford University Press.<br />
# Elements of Multivariate Time Series Analysis by G. C. Reinsel (1993), SpringerVerlag.<br />
# Introductory Statistics with R, by Peter Daalgard, Springer; (2004). Corr. 3d printing edition January 9.<br />
# Probability and Stochastic Processes, by Ionut Florescu, (2014) Wiley, ISBN: 978-0-470-62455-5<br />
# An Introduction to Statistical Learning with Applications in R by G. James, D. Witteb, T. Hastie, R. Tibshirani, (2013), Springer, ISBN: 1-4614-7137-0<br />
# New Introduction to Multiple Time Series Analysis by H. Lutkepohl, SpringerVerlag, (2005). ISBN: 3-540-26239-3.<br />
# Applied Multivariate Statistical Analysis by R.A. Johnson and D.W. Wichern, 6th ed., (2007) Prentice Hall. ISBN 0-13-187715-1<br />
# Nonlinear Time Series: Nonparametric and Parametric Methods, J Fan and Q. Yao. New York: Springer-Verlag, 2003. ISBN 0-387-95170-9. <br />
<br />
|GradingPolicy =<br />
<br />
=== Grade distribution ===<br />
<br />
<pre>HW 40% <br />
<br />
Midterm 20% <br />
<br />
Final Exam 40% </pre><br />
|LectureOutline =<br />
<br />
|Week1 =<br />
<br />
Week 1<br />
<br />
|Topic1 =<br />
<br />
Review of Statistical concepts. Estimators, Confidence intervals, Testing, Two way tables, Regression, ANOVA, logistic regression<br />
<br />
|Reading1 =<br />
<br />
Lecture notes<br />
<br />
|Week2 =<br />
<br />
Week 2<br />
<br />
|Topic2 =<br />
<br />
Principal Component Analysis, Independent Component Analysis, Scree plot, Returns and from PCA<br />
<br />
|Reading2 =<br />
<br />
Ch. 9 in [3] and notes<br />
<br />
|Week3 =<br />
<br />
Week 3<br />
<br />
|Topic3 =<br />
<br />
Factor models for asset returns. BARRA type models. Application to portfolio optimization.<br />
<br />
|Reading3 =<br />
<br />
Ch. 15 in [2], Ch. 6 in[1]<br />
<br />
|Week4 =<br />
<br />
Week 4<br />
<br />
|Topic4 =<br />
<br />
Discriminant Analysis and Mixture models. Introduction to classification. Regime switching and Hidden Markov Chains.<br />
<br />
|Reading4 =<br />
<br />
Lecture notes<br />
<br />
|Week5 =<br />
<br />
Week 5<br />
<br />
|Topic5 =<br />
<br />
Lasso Regression and related sparse regression techniques. Applications to finance. Copula methods and applications to risk management.<br />
<br />
|Reading5 =<br />
<br />
Lecture notes and Ch. 19 in [2]<br />
<br />
|Week6 =<br />
<br />
Week 6<br />
<br />
|Topic6 =<br />
<br />
Bayesian Statistics Methods. Hierarchical Bayes. Conjugate prior/posterior techniques. Sequential Bayes. Applications to estimating parameters for financial models.<br />
<br />
|Reading6 =<br />
<br />
Ch. 8 in [7] and Ch. 12 in [3] lecture notes<br />
<br />
|Week7 =<br />
<br />
Week 7<br />
<br />
|Topic7 =<br />
<br />
Midterm Examination<br />
<br />
|Reading7 =<br />
<br />
|Week8 =<br />
<br />
Week 8<br />
<br />
|Topic8 =<br />
<br />
Review of Univariate Time series ARIMA and ARCH type models.<br />
<br />
|Reading8 =<br />
<br />
Notes and Ch. 1 in [1]<br />
<br />
|Week9 =<br />
<br />
Week 9<br />
<br />
|Topic9 =<br />
<br />
Time series with an external component. ARIMAX models. Granger Causality.<br />
<br />
|Reading9 =<br />
<br />
Ch. 2 in [1] and notes<br />
<br />
|Week10 =<br />
<br />
Week 10<br />
<br />
|Topic10 =<br />
<br />
Long Memory time series models. Hurst parameter estimation, R/S analysis. ARFIMA/FARIMA, FIGARCH models.<br />
<br />
|Reading10 =<br />
<br />
Ch. 8 in [2]<br />
<br />
|Week11 =<br />
<br />
Week 11<br />
<br />
|Topic11 =<br />
<br />
Vector autoregressive and moving average VAR, VARMA models<br />
<br />
|Reading11 =<br />
<br />
Ch. 3 in [1], Ch. 11 in [2]<br />
<br />
|Week12 =<br />
<br />
Week 12<br />
<br />
|Topic12 =<br />
<br />
Unit root non-stationarity, Co-integration. Co-integrated VAR and VARMA. Error corrected models.<br />
<br />
|Reading12 =<br />
<br />
Ch. 5 in [1] and Ch. 12 in [2]<br />
<br />
|Week13 =<br />
<br />
Week 13<br />
<br />
|Topic13 =<br />
<br />
Multivariate volatility models. Multivariate GARCH. Cholesky decomposition and volatility modeling.<br />
<br />
|Reading13 =<br />
<br />
Ch. 7 in [1]<br />
<br />
|Week14 =<br />
<br />
Week 14<br />
<br />
|Topic14 =<br />
<br />
Multivariate volatility (cont.). Go-GARCH, Dynamic orthogonal components, principal volatility components<br />
<br />
|Reading14 =<br />
<br />
Ch. 13 in [2]<br />
<br />
}}</div>Shiweihttps://web.stevens.edu/hfslwiki/index.php?title=Template:Courses_FE&diff=5589Template:Courses FE2018-10-25T00:20:38Z<p>Shiwei: </p>
<hr />
<div><big>Core Courses</big><br />
<br />
* [[FE610 Stochastic Calculus for Financial Engineers]]<br />
* [[FE620 Pricing and Hedging]]<br />
* [[FE621 Computational Methods in Finance]]<br />
* [[FE630 Portfolio Theory and Applications]]<br />
* [[FE680 Advanced Derivatives]]<br />
* [[FE800 Project in Financial Engineering]]<br />
<br />
<big>Elective Courses</big><br />
* [[FE530 Introduction to Financial Engineering]]<br />
* [[FE535 Introduction to Financial Risk Management]]<br />
* [[FE540 Probability theory for Financial Engineering]]<br />
* [[FE541 Applied Statistics with Applications in Finance]]<br />
* [[FE542 Time Series with Applications to Finance]]<br />
* [[FE543 Introduction to Stochastic Calculus for Finance]]<br />
* [[FE545 Design, Patterns and Derivatives Pricing]]<br />
* [[FE550 Data Visualization Applications]]<br />
* [[FE555 2D Data Visualization Programming for Financial Applications]]<br />
* [[FE570 Market Microstructure and Trading Strategies]]<br />
* [[FE575 Introduction to Econophysics]]<br />
* [[FE580 Securitization of Financial Assets]]<br />
* [[FE582 Foundations of Financial Data Science]]<br />
* [[FE590 Introduction to Knowledge Engineering]]<br />
* [[FE595 Financial Systems Technology]]<br />
* [[FE625 Emerging Markets: Risks and Models]]<br />
* [[FE635 Financial Enterprise Risk Engineering]]<br />
* [[FE641 Multivariate Statistics and Advanced Time Series in Finance]]<br />
* [[FE655 Systemic Risk and Financial Regulation]]<br />
* [[FE670 Algorithmic Trading Strategies]]<br />
* [[FE710 Applied Stochastic Differential Equations]]<br />
* [[FE720 The volatility surface: risk and models]]</div>Shiweihttps://web.stevens.edu/hfslwiki/index.php?title=FE515_Introduction_to_R&diff=5588FE515 Introduction to R2018-10-25T00:11:34Z<p>Shiwei: </p>
<hr />
<div>{{Template:Test_Temp<br />
|Name =<br />
<br />
[[Ziwen Ye]]<br />
<br />
|Email =<br />
<br />
zye2@stevens.edu<br />
<br />
|Phone =<br />
<br />
|Office =<br />
<br />
Altorfer 301<br />
<br />
|Photo_file =<br />
<br />
https://kolmogorov.fsc.stevens.edu/cms/images/stories/crop/400px-Ziwen_Y.JPG<br />
<br />
|Office_Hour =<br />
<br />
|Classroom =<br />
<br />
Hanlon Lab 2<br />
<br />
|Classtime =<br />
<br />
M 14:00-16:00<br />
<br />
|Opening_Term =<br />
<br />
Fall On Campus, Spring On Campus<br />
<br />
|Opening_Term1 =<br />
<br />
Fall<br />
<br />
|Opening_Term2 =<br />
<br />
Spring<br />
<br />
|Campus1 =<br />
<br />
On Campus<br />
<br />
|Campus2 =<br />
<br />
On Campus<br />
<br />
|Opening_Term_Fall_On_Camp =<br />
<br />
X<br />
<br />
|Opening_Term_Spring_On_Camp =<br />
<br />
X<br />
<br />
|Opening_Term_Summer_On_Camp =<br />
<br />
|Opening_Term_Summer_Web_Camp =<br />
<br />
|Opening_Term_Spring_Web_Camp =<br />
<br />
|Opening_Term_Fall_Web_Camp =<br />
<br />
|Course_Intro =<br />
<br />
This course is designed for graduate students. Starting from 2018 fall semester, this course is extended to 2 hours each week.<br />
<br />
Upon completion the students will gain an understanding of the programming syntax and should be able to use R in any future courses.<br />
<br />
|Outcomes =<br />
<br />
|Description =<br />
<br />
|Textbook =<br />
<br />
Lecture Notes and Code<br />
<br />
* The art of R programming: a tour of statistical software design. Norman Matloff, First Edition, 2011. ISBN-10: 1593273843, ISBN-13: 978-1593273842<br />
* An Introduction to Analysis of Financial Data with R. Ruey Tsay, First Edition, 2012. ISBN-10: 0470890819, ISBN-13: 978-0470890813<br />
* Introduction to the Practice of Statistics. David S. Moore, George P. McCabe, Bruce A. Craig, Eighth Edition, 2014. ISBN-13: 978-1464158933, ISBN-10: 1464158932<br />
<br />
|Reading =<br />
<br />
CRAN: http://www.wikibooks.org<br />
<br />
R-help Info: https://stat.ethz.ch/mailman/listinfo/r-help<br />
<br />
R-help Archive: http://r.789695.n4.nabble.com<br />
<br />
Quick R: http://www.statmethods.net<br />
<br />
|GradingPolicy =<br />
<br />
The plan is to schedule 5 assignments for this semester. The assignments will due exactly before the next class. All LATE SUBMISSION will be punished unless you send me an email BEFORE DUE and get approved. If your submission passes the due for less than 24 hours, your highest score will be 67%; between 24 and 48 hours, your highest score will be 33%; after 48 hours this assignment will be graded as 0. If the assignments I give out is more than 5, the lowest grade will be dropped in final grading calculation.<br />
<br />
For this course, all students will have the midterm and final exams. Both exams are 2 hours length and will be held during the class. As a coding class, we only test the coding skill from students. Therefore, both exams will be open book. Students can use any materials during exams (such as notes, Google search engine and etc.) to help them answer all questions. However, any communication tools (such as Skype, email and etc.) and tutoring websites are NOT allowed.<br />
<br />
If students have any concern or questions regarding to the teaching contents and homework, they are encouraged to seek help from the instructor. Discussing homework with classmates are prohibited for this course. All code and reports must be written by yourself. Copying solutions from sources other than your brain is strictly forbidden. This kind of behavior will be considered as academic dishonesty/misconduct and will be dealt with according to the Stevens honor board policy.<br />
<br />
=== Grade distribution ===<br />
<br />
<pre>Assignments – 30%<br />
<br />
Midterm – 30%<br />
<br />
Final – 40%<br />
<br />
Bonus – TBD (Bonus includes but not limited to attendance and bonus questions)</pre><br />
|LectureOutline =<br />
<br />
|Week1 =<br />
<br />
Week 1<br />
<br />
|Topic1 =<br />
<br />
R basics(1)<br />
<br />
Data structures &amp; Loops<br />
<br />
|Reading1 =<br />
<br />
|Week2 =<br />
<br />
Week 2<br />
<br />
|Topic2 =<br />
<br />
Labor Day, No Classes<br />
<br />
|Reading2 =<br />
<br />
|Week3 =<br />
<br />
Week 3<br />
<br />
|Topic3 =<br />
<br />
R basics(2)<br />
<br />
Self-defined functions<br />
<br />
”apply” functions<br />
<br />
|Reading3 =<br />
<br />
|Week4 =<br />
<br />
Week 4<br />
<br />
|Topic4 =<br />
<br />
R basics(3)<br />
<br />
Generating random variables<br />
<br />
Discreet distribution &amp; Sampling<br />
<br />
|Reading4 =<br />
<br />
|Week5 =<br />
<br />
Week 5<br />
<br />
|Topic5 =<br />
<br />
Date and time objects<br />
<br />
Simple return and compounded return<br />
<br />
Plots<br />
<br />
|Reading5 =<br />
<br />
|Week6 =<br />
<br />
Week 6<br />
<br />
|Topic6 =<br />
<br />
Download data through R:<br />
<br />
Bloomberg API, Yahoo API (Equity and option)<br />
<br />
|Reading6 =<br />
<br />
|Week7 =<br />
<br />
Week 7<br />
<br />
|Topic7 =<br />
<br />
Yahoo API (advanced)<br />
<br />
Basic statistics<br />
<br />
|Reading7 =<br />
<br />
|Week8 =<br />
<br />
Week 8<br />
<br />
|Topic8 =<br />
<br />
Linear regression models<br />
<br />
Stepwise selection &amp; goodness criteria<br />
<br />
|Reading8 =<br />
<br />
|Week9 =<br />
<br />
Week 9<br />
<br />
|Topic9 =<br />
<br />
Midterm<br />
<br />
|Reading9 =<br />
<br />
|Week10 =<br />
<br />
Week 10<br />
<br />
|Topic10 =<br />
<br />
T-test and ANOVA<br />
<br />
|Reading10 =<br />
<br />
|Week11 =<br />
<br />
Week 11<br />
<br />
|Topic11 =<br />
<br />
Newton’s method and gradient descent<br />
<br />
|Reading11 =<br />
<br />
|Week12 =<br />
<br />
Week 12<br />
<br />
|Topic12 =<br />
<br />
Volatility<br />
<br />
GBM and BS Model<br />
<br />
|Reading12 =<br />
<br />
|Week13 =<br />
<br />
Week 13<br />
<br />
|Topic13 =<br />
<br />
GGplot<br />
<br />
|Reading13 =<br />
<br />
|Week14 =<br />
<br />
Week 14<br />
<br />
|Topic14 =<br />
<br />
Rmarkdown and LaTeX<br />
<br />
|Reading14 =<br />
<br />
}}</div>Shiwei