Update README.md

Author: jaehyukchoi

README.md

@@ -1,2 +1,80 @@
-# 2019.M1.ASP
-2018-19 Module 1 (Fall), Applied Stochastic Processes
+# Applied Stochastic Processes (FIN 514, 2019-20 Module 1)
+
+## Announcements
+* Email is the preferred method of communication. Class mailing list will be created as [email protected].
+
+## Course Slides and Other Resources
+* Prelims: [Probability Statistics Review](files/Prob_Stat_Review.pdf) | [MC Method](files/MCmethod.pdf) ([Py demo](py/MC_Demo.ipynb))
+* Past Exam: [2017-18 ASP](files/ASP2017_Midterm.pdf), [2016-17 StoFin Midterm](files/SF2016_Midterm.pdf) and [Final](files/SF2016_Final.pdf)
+* Black-Scholes model ([Py demo](py/BlackScholes_ImpliedVol.ipynb), [MC demo](py/BlackScholes_MC.ipynb)): Also see Ch. 10 of [StoFin Course Notes](https://github.com/PHBS/2017.M3.StoFin/blob/master/files/SCFA_Notes.pdf)
+* Normal (Bachelier) model ([Slides](files/Normal_Model.pdf))
+* Implied volatility ([Slides](files/ImpVol.pdf), [Py demo](py/BlackScholes_ImpliedVol.ipynb))
+* Spread/Basket options ([Slides](files/SpreadBasketOption.pdf))
+* SABR model ([Slides](files/SABRmodel.pdf))
+* Copula ([Slides](files/Copula.pdf), [Py demo](py/Demo_Copula.ipynb))
+* ...
+
+## Lectures
+* __18__ (11.09 Tues): Course project presentation
+* __17__ (11.06 Tues): Research Presentation (NSVh model) and HW4 review
+* __16__ (11.02 Fri): Research Presentation (Sum of BSM models) and HW3 review
+* __15__ (10.30 Tues): Copula ([Slides](files/Copula.pdf), [Py demo](py/Demo_Copula.ipynb)), Github Pull-request
+* __14__ (10.26 Fri): Copula ([Slides](files/Copula.pdf), [Py demo](py/Demo_Copula.ipynb))
+* __13__ (10.23 Tues): Midterm exam ([Solution](files/ASP2018_Midterm.pdf))
+* __12__ (10.19 Fri): Review for midterm exam
+* __NO CLASS__ on 10.16 Tues
+* __11__ (10.12 Fri): SABR model ([Slides](files/SABRmodel.pdf)): Conditional MC method
+* __10__ (10.09 Tues): HW2 review, SABR model ([Slides](files/SABRmodel.pdf)), Stochastic Finance review
+* __09__ (09.28 Fri): SABR model([Slides](files/SABRmodel.pdf): Volatility smile, Local volatility model)
+* __08__ (09.25 Tues): [Spread/Basket option implementation](py/TestCode_BasketSpread.ipynb), Debugging in Python, Import([Py Demo](py/HW4/Demo_Advanced_Import.ipynb))
+* __07__ (09.21 Fri): [Black-Scholes Implementation](https://github.com/PHBS-2017-ASP-Classroom/BSMmodel_Base), Spread/Basket options ([Slides](files/SpreadBasketOption.pdf))
+* __06__ (09.18 Tues): Black-Scholes and Normal models in MC ([Py Demo](py/BlackScholes_MC.ipynb)), Normal model ([Slides](files/Normal_Model.pdf)), Correlated Normal RNs ([Py Demo](py/CorrelatedNormals_Demo.ipynb))
+* __05__ (09.14 Fri): HW2, Black-Scholes implementation ([Py Demo](py/BlackScholes_FunctionVsClass.ipynb)), Implied volatility ([Slides](files/ImpVol.pdf), [Py demo](py/BlackScholes_ImpliedVol.ipynb))
+* __04__ (__09.12 Wed__ instead of __10.16 Tues__): Python crash course ([Basic](py/PythonCrashCourse_Derek_Banas.ipynb) | [Numpy](py/PythonCrashCourse_Numpy.ipynb)).  More [cheatsheets](https://ehmatthes.github.io/pcc/cheatsheets/README.html) also available in [MLF CMS](http://cms.phbs.pku.edu.cn/claroline/document/document.php?cidReset=true&cidReq=FN570).
+* __03__ (09.11 Tues): Continued ([Py demo](py/MC_Demo.ipynb))
+* __02__ (09.07 Fri): Scientific computing, Monte Carlo method, Random number generation ([Slides](files/MCmethod.pdf)). 
+* __01__ (09.04 Tues): Course overview ([Syllabus](files/syllabus.pdf)), Probability Statistics Review ([Slides](files/Prob_Stat_Review.pdf))
+
+## Homeworks:
+* ### __Set 4__ 
+* ### __Set 3__ 
+* ### __Set 2__ 
+* ### __Set 1__ 
+
+## Classes: 
+* Lectures: Tues & Fri 1:30 – 3:20 PM
+* Venue: PHBS Building, Room 211
+
+## Instructor: [Jaehyuk Choi](http://www.jaehyukchoi.net/phbs_en)
+* Office: PHBS Building, Room 755
+* Phone: 86-755-2603-0568
+* Email: [email protected]
+* Office Hour: Tues & Fri 10:30 – 11:30 AM or by appointment
+
+## Teaching Assistance: TBA
+* Email: [email protected]
+* TA Office Hour: TBA (Room 213/214)
+
+## Course overview: 
+Applied Stochastic Processes (ASP) is intended for the students who are
+seeking advanced knowledge in stochastic calculus and are eventually interested in the jobs in
+financial engineering. As the name indicates, the course will emphasis on applications such as
+numerical calculation and programming. On completion of this course, the students will learn
+how financial observations (e.g. stock prices and FX rate) are modelled with stochastic
+processes and how they can be computed using analytics or computer simulations.
+
+## Prerequisites: 
+[Stochastic Finance](https://github.com/PHBS/2018.M3.StoFin) (FIN 519), a year 1 required course for quantitative finance program, is a prerequisite for the ASP since it provides theoretical background. Undergraduate-level knowledge in probability, statistics, linear algebra and programming skill (Python) are also highly recommended. 
+
+##  Extra Reading Materials
+* Monte Carlo Methods in Finance by Peter Jaeckel
+* Option Valuation Under Stochastic Volatility by Alan Lewis
+* [Stochastic Calculus and Financial Applications](http://www-stat.wharton.upenn.edu/~steele/StochasticCalculus.html) by J. Michael Steele
+([Stochastic finance course notes](https://github.com/PHBS/2016.M3.StoFin/blob/master/files/Notes%20Steele.pdf))
+
+## Assessment/Grading Details
+Attendance 20%, Mid-term Exam 30%, Assignments 20%, Course Project 30%
+* __Midterm exam__: 10.23 Tues. Open-book exam without computer/phone/calculator use. No final exam.
+* __Course project__: Presentation (11.09 Fri). Group up to 3 people.
+* __Attendance__: Randomly checked. The score is calculated as 20 – 2`x`(#of absence). Leave request should be made 24 hours before with supporting documents, except for emergency. Job interview/internship cannot be a valid reason for leave
+* __Grade__ in letters (e.g., A+, A-, ... ,D+, D, F). __A- or above < 30% and C+ or below > 10%__.