#CQF Final Project JUNE 2018 Cohort Released | For CQF Help Contact us via Whats App :+91 8697669523/+91 9830552467 | www.findtutoronline.net

Hi All
Please see below instructions for the final project.
Assessment for Final Project June 2018 Cohort is carried out by the means of a programming project. It is designed to give an opportunity for further study of numerical methods required to implement and validate a quantitative model.

To complete the project, you must implement one of the listed topics mentioned Below :

1) Portfolio Construction with Robust Covariance

2)Machine Learning Techniques

3)Arbitrage Trading Strategy Design & Backtest

4)Pricing Credit Spread

5)Interest Rate Modeling for Counterparty Risk

6)Local Volatility in Interest Rates

Electives

Electives are not a condition to complete the project. They give ideas on what to implement/write in analysis and discussion.

The elective material to support your project work is now available on the portal under Advanced Electives. There are fourteen elective topics in total and you will need to choose two topics to assist you with your project. You will have up until the 30 November 2018 to make your choices. All fourteen electives will be visible up until this point.

To select your electives please log into your portal:
· Study
· Module Resources
· Advanced Electives

You will have up until the deadline to change your choices. After the deadline date, only the two electives you have chosen will be visible. All electives will be available on the Lifelong Learning section of the portal by this point.

Please see attached Project Brief for full instructions.

Delegates are advised to print out the section of this Brief for their chosen topic.

There is no CVA component for June 2018 cohort.

Submission date for the project is Monday 07 January 2019, 23.59 BST

Absolutely no extensions will be accepted, any delegate who fails to hand in their work will be deferred with no exceptions.

Submission Requirements

Submission date for the project is 7th January 2019.

Absolutely no extensions will be accepted, any delegate who fails to hand in their work will be deferred with no exceptions.
Submission Requirements
Submit working code together with a well-written report and originality declaration.
Project report to have an exact title from the list above and content must correspond to it. Guided length is 30-50 pages, excluding code.
Submissions to be uploaded to online portal only. Upload format: one written report, (PDF), one zip archive with code and data files, and one scanned declaration (PDF).

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Project Brief Enclosed Herewith.

1 Instructions

To complete the project, you must implement one topic from the list below. Each topic requires the speci c model at its core and your project must implement it. Final project is about you as quant gaining and demonstrating the ability to implement (code) numerical techniques and backtest or sensitivity-test model output (prices, portfolio allocations, pairs tradin, etc).

1. Portfolio Construction with Robust Covariance (PC)

2. Machine Learning Techniques (ML)

3. Arbitrage Trading Strategy Design & Backtest (AB)

4. Pricing Credit Spread (CR)

5. Interest Rate Modeling for Counterparty Risk (IR)

6. Local Volatility in Interest Rates (LV)

Note. If you continue from a previous cohort please submit for the topic as described in this current Brief. There is no CVA component for June 2018 cohort.

1.1 Project Report and Submission Requirements

Submit working code together with a well-written report and originality declaration.

There is no set page length, be as condensed or as elaborate as you see t. However, your re-port must have an analytical quality and discussion of results/robustness/sensitivity/backtesting as appropriate to the topic.

Use charts, test cases and comparison to empirical research papers where available.

The report must contain a su cient description of the mathematical model, numerical methods and their convergence/accuracy/computational properties.

Please feature the numerical techniques you coded { make a table.

Mathematical sections can be prepared using LaTeX or Equation Editor (Word). For Mathematica and Python notebooks, make sure they are presentable and include source.

Submission date is 7 January 2018, 23:59 BST

Work done must match the Brief. There is no extension to the Final Project.

Projects without declaration or working code are incomplete and will be returned.

All projects are checked for originality. We reserve an option of a viva voce before a project grade and therefore, quali cation to be awarded.

1.2 CQF Electives Choice

We ask you to choose two Electives initially in order to preserve focus. All Electives content will be available later for your advancement. Electives give support but not a condition to Final Project, and several viable combinations for each topic are possible.

The e ective approach is to select one topical elective and one code development elective. Top-ical electives are identi ed for each topic, eg, ML, PC, AB CR, IR, LV. Coding electives give numerical techniques across models, they are are identi ed with Dev label.

For portfolio construction topic, Risk Budgeting elective recommended as the primary choice. Counterparty Credit Risk elective is main choice for two topics, Credit Spread Pricing and In-terest Rates for Counterparty Risk. Machine Learning Techniques topic can be started with Machine Learning with Python elective.

Project Workshops I and II supplement on methods not covered in electives and provide focus to the implementation of each topic. Webex sessions that follow workshops venture into imple-mentation examples.

Machine Learning with Level II on Python for quant nance, capabilities of scikit-learn libary
Python { ML (OLS, logistic), tensorow library example of ’deep learning’ (classi er)
{ use the Github link below and explore. Outcome: returns modelling

(for either prediction or backtesting), and Least Squares Monte Carlo
for pricing Bermudans in LMM framework.

Data Analytics { Dev Level I on Python for quant nance, data structures (Dataframe), numpy
(AB, PC) for numerical analysis, pandas for nancial time series analysis, data vi-
sualization. Outcome: a pre-requisite if you have not coded in Python.

Python Applications { Quant numerical techniques with Python examples and notes on com-
Dev (CR, IR, LV) putational e ciency: Normal from Uniform RN, linear equations and
eigenvalues, numerical integration, root nding (Bisection, Newton),
random numbers with arrays and seeding, Binomial/Poisson/LogNormal
distributions, SDE simulation (GBM, OU cases). Introduction to
Jupyter Notebook, arrays and indexes. Outcome: relevant particularly

for Monte-Carlo in Credit Spread and Interest Rate topics.

Table 2: Primary Elective Choices
3

Counterparty Credit CDS, survival probabilities and hazard rates reviewed. Three key nu-
Risk { CR, IR merical methods for quant nance pricing (Monte-Carlo, Binomial Trees,
Finite Di erence). Monte Carlo for simple LMM. Review of Module Five
on Credit with a touch on the copula method. Outcome: covers CVA

Computation clearly and reviews of credit spread pricing techniques.

Risk Budgeting { PC Reviews the nuance of Modern Portfolio Theory, ties in VaR and Risk
primary choice Decomposition with through derivations and expectation algebra. Gives
simple examples of gures you need to compute and then combine with
portfolio optimisation. Risk-budgeting portfolio from Video Part 10.

Advanced Volatility Considers the main kinds of PDEs (stochastic volatility and jump di u-
Modeling { LV primary sion) and their analytical solution: the main approach to solve stochastic
volatility (Heston model) is via Fourier Transform. In-depth on integra-
tion Outcome: Local Volatility topic o ers a classic pricing PDE, which

can be solved by techniques from this elective.

C++ { Dev Consider this a revised version of C++ Primer/ initial certi cate course.
Small examples will guide you through identi ers, data types and op-
erations with them. That is followed by debugging, control structures
and vignette cases on useful numerical techniques such as CDF, inte-
gration and use of cmath. The large nal part is about functions and
elements of object-oriented programming. Outcome: teaches good pro-

gramming style and generic techniques you are likely to apply in other
coding languages.

Table 3: Primary Elective Choices (Cont)

Machine Learning with Python has computational material on Github https://gist.github.com/yhilpisch/648565d3d5d70663b7dc418db1b81676

PyAlgoTrade documentation http://gbeced.github.io/pyalgotrade/docs/v0.18/html/index.html

More electives are available, particularly ones that you might like to return to for your profes-sional advancement { they are described at the end of this Brief.

1.3 Coding for Quant Finance

Choose programming environment that has appropriate strengths and facilities to imple-ment the topic (pricing model). Common choices range from VBA to Python to C++, please exercise judgement as quants: which language has libraries to allow you to code faster, validate easier.

Use of R/Matlab/Mathematica is encouraged { as a main environment or an addition. Sometimes there is a need to do time series/covariance matrix/rank correlation computa-tion, or to present results and the formulae in coherent form.

Project Brief give links to nice demonstrations in Matlab, and Webex sessions demonstrate Python notebooks { doesn’t mean your project to be based on that ready code.

Python with pandas, matplotlib, sklearn, and tensorow forms a considerable challenge to Matlab, even for visualisation. Matlab plots editor is clunky and it is not that di cult to learn various plots in Python.

‘Scripted solution’ means the ready functionality from toolboxes and libraries is called, but the amount of own coding of numerical methods is minimal or non-existent. This particularly applies to Matlab/R.

Projects done using Excel spreadsheet functions only are not robust, notoriously slow, and do not give understanding of the underlying numerical methods. CQF-supplied Excel spreadsheets are a starting point and help to validate results but coding of numerical techniques/use of industry code libraries is expected.

The aim of the project is to enable you to code numerical methods and develop model pro-totypes in a production environment. Spreadsheets-only or scripted solutions are below the expected standard for completion of the project.

What should I code? Delegates are expected to re-code numerical methods that are central to the model and exercise judgement in identifying them. Balanced use of libraries is at own discretion as a quant.

Produce a small table in report that lists methods you implemented/adjusted. If using ready functions/borrowed code for a technique, indicate this and describe the limitations of numerical method implemented in that code/standard library.

It is up to delegates to develop their own test cases, sensibility checks and validation. It is normal to observe irregularities when the model is implemented on real life data. If in doubt, reect on the issue in the project report.

The code must be thoroughly tested and well-documented: each function must be de-scribed, and comments must be used. Provide instructions on how to run the code.

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