Implement and validate a quantitative model

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Reference no: EM131318527 , Length: 40

Programming Project

Instructions

Projects are submitted in accordance with the current Brief. 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 the topic below plus CVA component.

SECTION 1: Time Series Analysis and Backtesting

SECTION 2: CVA Calculation for an Interest Rate Swap
Note:

The CVA component (section 2) is a mandatory addition as it balances exposure to the quant issues (interest rates, discounting) that would not be in focus otherwise.

Programming environment must have appropriate strengths and facilities to implement the topic (pricing model). Common choices range from Matlab to Python to C++, please exercise judgement as quants.

Use of R/Matlab/Mathematica/Matlab is encouraged where time series or presentation involved. Coding of numerical techniques/use of industry code libraries is expected.

‘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 as well as Excel spreadsheet functions (not robust).

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

To answer the question, "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 allowed at the delegate's own discretion and subject to a description of limitations for ready functions/borrowed code (in the report).

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, reflect on the issue in the project report.

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

The main purpose of the report is to facilitate access to numerical methods' implementation (the code) and pricing results.

The report must contain a sufficient description of the mathematical model, numerical methods and their properties. In-depth study is welcome but report must be relevant.

Identify numerical methods recorded and include their code/algorithms in an appendix.

Please give due attention and space for presentation and discussion of your pricing results. Present explicit sensitivity and/or risk analysis.
Use charts, test cases and comparison to research results where available.

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

Time Series Analysis and Backtesting

Summary

The aim to this topic is an estimation and analysis of tradeable relationships between two or more financial time series. Identifying and backtesting a robust cointegrated relationship means exposing a factor that drives both (or many) asset prices. The factor is traded by entering the long-short position given by cointegrating weights.

Through implementation you will have a hands-on introduction to Vector Autoregression (for returns) and Error Correction (for prices) models, which are the main variations of the mul- tivariate regression. Instead of econometric forecasing, a range of techniques and considerations applied known as ‘backtesting'. The techniques and quant recipes are specific to statistical ar- bitrage or systematic (algorithmic) strategy selected, for example, statistical arbitrage requires evaluating mean-reversion and optimality of trading of a spread.

A project that solely runs pre-programmed statistical tests on data is a preparation work, not the complete project. The project should have coding of necessary statistical tests from the first principles (explicit regression equations) by yourself. The least deliverables are a. implemented Engle-Granger procedure, b. statistical diagnosis and backtesting (split dataset in half or com- pute rolling estimates), and c. market factor backtesting via regressing returns from your strategy on market index returns or another factor. These are in addition to the underlying numerical methods on matrices and vector autoregression.5

Backtesting

The following notes o↵er choices to implement in aspects and questions of backtesting:
- All project designs (whether learning-level or in-depth) should include backtesting of a strategy. The strategy is realised by using cointegrating coefficients ØCoint as allocations w. That creates a long-short portfolio that generates a mean-reverting spread (cointegrated residual).

- Does cumulative P&L behave as expected (for a cointegration trade)? Is P&L coming from a few or lot of trades/time period? What are the SR/Maximum Drawdown? Behaviour of risk measures (volatility/VaR)? Concentration in assets and attribution?
- Impact of transaction costs (plot an average P&L value vs. number of transactions).

- Optionally, introduce liquidity and algorithmic flow considerations (a model of order flow). How would you be entering and accumulating the position? What impact bid-ask spread and transaction costs will make?

Time Series Analysis and Backtesting

Summary

The aim to this topic is an estimation and analysis of tradeable relationships between two or more financial time series. Identifying and backtesting a robust cointegrated relationship means exposing a factor that drives both (or many) asset prices. The factor is traded by entering the long-short position given by cointegrating weights.

Through implementation you will have a hands-on introduction to Vector Autoregression (for returns) and Error Correction (for prices) models, which are the main variations of the mul- tivariate regression. Instead of econometric forecasing, a range of techniques and considerations applied known as ‘backtesting'. The techniques and quant recipes are specific to statistical ar- bitrage or systematic (algorithmic) strategy selected, for example, statistical arbitrage requires evaluating mean-reversion and optimality of trading of a spread.

A project that solely runs pre-programmed statistical tests on data is a preparation work, not the complete project. The project should have coding of necessary statistical tests from the first principles (explicit regression equations) by yourself. The least deliverables are a. implemented Engle-Granger procedure, b. statistical diagnosis and backtesting (split dataset in half or com- pute rolling estimates), and c. market factor backtesting via regressing returns from your strategy on market index returns or another factor. These are in addition to the underlying numerical methods on matrices and vector autoregression.5

Backtesting
The following notes o↵er choices to implement in aspects and questions of backtesting:
- All project designs (whether learning-level or in-depth) should include backtesting of a strategy. The strategy is realised by using cointegrating coefficients ØCoint as allocations w. That creates a long-short portfolio that generates a mean-reverting spread (cointegrated residual).

- Does cumulative P&L behave as expected (for a cointegration trade)? Is P&L coming from a few or lot of trades/time period? What are the SR/Maximum Drawdown? Behaviour of risk measures (volatility/VaR)? Concentration in assets and attribution?
- Impact of transaction costs (plot an average P&L value vs. number of transactions).

- Optionally, introduce liquidity and algorithmic flow considerations (a model of order flow). How would you be entering and accumulating the position? What impact bid-ask spread and transaction costs will make?

https://www.dropbox.com/s/jcl6f1okikdqfs6/Programming%20Project%20Resources.zip?dl=0

Reference no: EM131318527

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Reviews

len1318527

12/19/2016 7:05:30 AM

I appreciate I am sending over a lot of stuff – they are however meant to make your life easier and help us both in achieving the best possible outcome, so please do go through these to get a better understanding of the project and clear direction. The resources attached include very good material on both sections of the project (CVA component, Time Series & Backtesting) as well as useful material on Statistical Package R.As always, please let me know if anything is unclear or if you have trouble downloading the documents. Word Count/ Number of Pages 30-40 pages I am looking for an expert to work on a quantitative finance programming project. The project requires both a working code (with explanations) and a written report. I would be looking for the code to be written in MatLab, R, Python or C++. Please review the requirements I will provide further notes to help give a direction to the report

len1318527

12/19/2016 7:05:07 AM

I have attached the resources I have available – will you please pass these to the subject-matter expert. Please note that there should be no plagiarism and the project should not be solely based on these resources – they are rather provided for direction on the quantitative techniques to use. I would like to ask you to treat these resources confidentially and not pass them over to anyone or reproduce them in any way. If equations/formulae/explanations from these resources are used I would require you to clearly reference them by having a clear references section at the end of the project and by numbering accordingly the places within the project that the resource has been used. The same is required when another resource (including papers, books, websites, journals etc.) is used. If in doubt if something constitutes plagiarism, please ask me and I will confirm as soon as possible. The code should be original and not extracted from somewhere – the basis of this assignment is the code itself so it should reflect its originality.

len1318527

12/19/2016 7:04:42 AM

I am aiming for high First Class grade. Minimum of 90%. If you believe this will not be the case, I would like to know in advance. If after receiving the completed project I find that it does not meet the criteria I have stated in my previous and current communications, I would expect you to be available to make amendments or additions to the project as required All the work has to be fully completed and submitted to me Note: I have attached the requirements and instructions again for your reference. I need to emphasise, in case it wasn't clear in the instructions, that graphs/charts generated by the code are very important for the project to score a high mark. The tutor must: • Deliver high-quality work (aim for minimum achieved grade of 90%) • Fully meet the requirements as set out in the supporting documents • Provide the first half of the assignment completed for review. (UK time). • Provide the completed assignment on time.

len1318527

12/19/2016 7:04:25 AM

Requirements, Attachments, Instructions & Specifications from the Client as under: Client Expectations: Length of project: should be in the range of 30-40 pages in Microsoft Word (Font: Arial, Size: 11). The code has to be provided separately and has to be fully documented. Before starting the project, I would require to know which programming language you are using and which version. The requirements and instructions of the project should be 100% followed. If there is anything that is unclear or you believe you cannot fully deliver, I would like to know before proceeding. If in doubt about any of the requirements I would expect you to ask me first. As I said, I have relevant material which I can share to give you the right direction on the techniques that should be used. I will judge and review the work to check that it satisfies the requirements and the quality I am looking for (in terms of techniques used, accuracy, code, level of analysis, explanations and write up of report),

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