CQF_June 2017_Fianl Project Brief. (2) - Online Tutoring

Certi cate in Quantitative Finance Final Project Brief
JUNE 2017 COHORT This document outlines each available topic together with submission
requirements for the project report. By-step instructions o er a structure
not a limit to what you can implement. A separate source, Q&A on Robust
Modelling discusses the most frequent issues and relevant detail of
numerical methods. This Brief, Project Workshops and Q&A are your primary guidance. Please
make sure you have reviewed the workshops and topical lectures relevant to
your chosen topic.
| |Portfolio Construction, Time Series, HJM/LMM/SABR, |
| |Copula Method |
| | |
| |Credit Analytics, CVA |
| | |
| |Local Volatility, PDEs, One-factor rate models |
| | |
| |Table 1: Support Allocation | 1
1 Instructions To complete the project, you must implement one topic, plus CVA component,
as given and described in this current Brief. Each topic covers speci c
pricing or allocation model, but numerical techniques can vary and include
advances from Time Series Analysis, Data Analytics and Machine Learning.
Final project mark is entered as Module Six mark. All. CVA Calculation for Interest Rate Swap. This is a mandatory addition
as it balances the exposure to the quant modelling in rates and credit.
Can be implemented on an Excel spreadsheet (or CQF example spreadsheets
reused). 1. Credit Portfolio Fair Spread and Sensitivity Analysis 2. Portfolio Construction with Robust Volatility 3. Data Analytics: Asset Allocation with Views Features (advanced) 4. Arbitrage Trading on Cointegration with Backtest 5. Local Volatility in Interest Rates 6. LIBOR and OIS Rates: Market Volatility (advanced) Each of the topics is more speci c than elective because the topic focuses
on a speci c pricing model (for derivatives), speci c portfolio
construction approach (Black-Litterman), or speci c time series analysis
(cointegration). Electives are usually broader and cover the subject area.
Advanced topics are recommended to delegates with experience in respective
areas. 1.1 Submission Requirements Submit working code together with a well-written report and originality
declaration. Project report to have an exact topic title and content must correspond
to it. Recom-mended length is 30-50 pages, excluding code. CVA component would be a chapter in your report, 4-5 pages or more
depending on your venturing to interest rate modelling and analysis of
output. Submissions to be uploaded to online portal only. Upload format: one
written report (PDF), one zip archive with code and data les, and one
scanned declaration (PDF). Submission date is Monday, 8 January 2018, 23:59 GMT Submissions must match the Brief. There is no extension to the Final
Project.
2
1.2 CQF Electives We ask you to indicate the choice of two Electives in order to preserve
focus { content for all electives will be available later for your
advancement. Electives are not a condition to Final Project. You will be
using one/a few techniques (but not all) covered in electives on Data
Analytics, Python Applications, Computational Methods for Black-Scholes
Pricing, and Counterparty Credit Risk. Risk Management and Machine Learning with Python are recommended as stand-
alone intro-ductions to these professional areas. |Algorithmic Trading |An example of strategy tting the reversion to the |
| |Log-Periodic Power |
| |Law (LPPL), a kind of statistical arbitrage. Discusses|
| |optimisation in |
| |context of trading strategies (Slide 51 onwards). |
| |Multicharts software. |
| |Notes on trader's psychology. Outcome: relevant to |
| |Arbitrage Trading |
| | |
| | |
| |of SDEs. Dynamic programming solves optimisation over |
| |a stochastic |
| |variable (eg, asset price, interest rate, risk |
| |premium). Outcome: this |
| | |
| |optimal allocations by Merton index of satisfaction, |
| |Kelly criterion and |
| |venture to Risk-Sensitive Asset Management. |
| | | | | | | | |
|Behavioural Finance |Heuristics, Biases and Framing. Excursion to game |
| |theory and proba- |
| |bility to formalise the psychology of choice. Outcome:|
| |the elective is |
| | |
| |Data Analytics topic. |
| | | | | | |
|Risk Management |The base you need to be a risk manager (read Coleman |
| |guide) and |
| |tools for Basel regulation: (a) weakness of |
| |risk-weighted assets, (b) |
| |extreme value theory for ES and capital requirement |
| |and (c) adjoint |
| |automatic di erentiation to compute formal |
| |sensitivities (derivatives). |
| |Other numerical methods covered the same as |
| |Counterparty Risk Elec- |
| |tive. Outcome: this elective is best taken for your |
| |own advancement. |
| | | | | | |
|Counterparty Credit|CDS, survival probabilities and hazard rates reviewed.|
| |Three key nu- |
|Risk |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 in simple and great detail, in-depth |
| |review to prepare for |
| |Credit Spread topic if necessary. |
| | | | |
|Modeling | | |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 |
| | | | |
| | | |
|ods | | |tion, root nding (Bisection, Newton), polynomial |
| | | |interpolation, and |
| | | |numerical integration (trapezium and Simpson rules), |
| | | |vector and ma- |
| | | |trix norms. Outcome: a refresher of methods that |
| | | |support modelling |
| | | | |
| | | | | | | | |
|Python Applications | |Reviews quant numerical techniques, now with |
| | |Python examples |
| | | |and notes on computational e ciency: Normal from |
| | | |Uniform RN, |
| | | |linear equations and eigenvalues, numerical |
| | | |integration, root nd- |
| | | |ing (Bisection, Newton), random numbers with arrays |
| | | |and seeding,