Bookshelf

Returned from Waterstones bookstore on Trafalgar Square with a these Wilmott's books. No electronic copy can replace a "taste" of original old paper book!

Hypothetical Risk Valuation System

IT systems of banks and investment funds are versatile. They have been grown for years and today financial institutions increasingly rely on the quality of their quantitative technology. Such a trend has made computational finance topmost important.

Computational finance is a cross-disciplinary field that focuses on the financial services industry and relies on mathematical finance, numerical methods and computer simulations to make investment decisions and facilitates portfolio risk management.

Despite the fact banks tend to develop risk valuation systems in-house all of them can generally fit into the given below schema.

How the system works

Credit Risk Modelling Slides

A good overview of credit crisis of 2007-2009 and credit risk modelling of CDO's using copula correlation models.

Copula approach to Default Correlation and the Credit Crisis of 2008/2009 from Robbin Tops

Quant Bookshelf

A good resource with a list of readings for those who are interested in better understanding quants to speak the same language at least! ;)
http://www.quantstart.com/

Quantitative Finance Videos

Nathan Whitehead made a list of videos related to quantitative finance. Most are based on the book "Paul Wilmott on Quantitative Finance, 2nd Edition". And some are from "The Concepts and Practice of Mathematical Finance" by Mark Joshi.

Asset Based Securities Regulatory Changes

What does ABS mean? 

An asset-backed security (ABS) is a security whose income payments and hence the value is derived from and collateralized (or "backed") by a specified pool of underlying assets.
The pool of assets is typically a group of small and/or illiquid assets which can’t be sold individually.
The market appeared in the 1980’s and is developing till now

ABS Basic Structure

The pools of underlying assets can include:

• Common payments e.g.: credit cards, auto loans, mortgage loans
• Other cash flows e.g.: aircraft leases, royalty payments, movie revenues

Pooling is also called securitization.

Hence, Collateral Debt Obligations (CDO) and Mortgage Backed Securities (MBS) are sub-types of ABS

ABS Types by Cashflow Filling

Fully-supported: repayment is supported by a financial guarantee (surety bond, letter of credit, third party guarantee or irrevocable liquidity facility). 

This provides both liquidity and credit protection for investors: the provider of the support has agreed to provide funds to the SPV (Special Purpose Vehicle) to repay investors without regard to the value of assets owned by the SPV.

Partially-supported: repayment primarily depends on the cash flow expected to be realized on the pool of assets, as well as liquidity and credit enhancement from third parties.

Roles in ABS

• Seller / Originator
• Special Purpose Vehicle (SPV)
• Servicer
• Trustee
• Underwriter
• Investors

The seller assembles the pool of financial assets backing the securities by purchasing them. 

Synonyms: originator, depositor or other sponsor

The seller then describes the financial assets in offering materials and sells the securities backed by them to investors. 

The seller also chooses other participants: underwriter, servicer, trustee.

Underwriters, their counsel and other experts conduct a ‘due diligence’ - review of the assets, the structure of the transaction and the parties involved to obtain protection under the securities laws that the prospectus or other sales document is accurate. 

The servicer is appointed as an independent contractor who performs its servicing obligations for the benefit of the transaction investors. 

The servicer typically collects all the income from the assets, enforces the assets as needed and may perform any evaluations needed to substitute assets.

Trustee in ABS is a broad term role:

• Indenture (agreement) trustee
• Issuing and paying agent
• Securities registrar, transfer agent and calculation agent with respect to the securities
• Custodian of the assets (on behalf of the issuer) 
• Analytics provider
• Back-up servicer 

REG AB

The Regulations AB were dopted in 2004

Requires specific disclosure regarding ABS transaction counterparties in transactions registered with the SEC, including trustees, sponsors, depositors, issuing entities, servicers, originators and others. 

The crisis of 2007-2009 revealed the lack of transparency of ABS trading.

REG AB II Proposal

Proposed in April 2010, July 2011, August 2013 and is aimed to further enhance ABS market transparency.

Lastly re-opened for comments on Feb 25, 2014 till March 28, 2014.

New regulation will impose substantial changes in the rules and forms of the offering process, disclosure and reporting requirements for ABS 

Proposal is a different name of REG AB II

SEC believes that compliance date for the new rules should NOT extend more than a year past their adoption date

Though SEC requests comment on whether any of the proposals should be phased (from 6 months to 2 years). 

1. Registration
   - Remove the investment-grade rating requirement for shelf eligibility and impose new requirements e.g. CEO certification
2. Disclosure
   - Increase the amount of disclosure provided in public offerings and
   - In shelf offerings, the amount of time that investors would have to examine the disclosure.
   - Require that in any private offering of “structured finance products” made in reliance on Rule 144A under the Securities Act of 1933, investors have the right to obtain all of the same initial and ongoing information as if the offering were SEC-registered.
3. Periodic reporting
   - This is less important thus is out of scope

Registration Process 

According to Reg AB, securities may be later offered “off the shelf” (Form S-3) if the securities are rated investment grade by a nationally recognized statistical rating organization (NRSRO)

Instead, Reg AB 2 (Form SF-3) would require new conditions:

ABS sponsor to retain at least 5% of each tranche of ABS offered and would not permit hedging those holdings

A certification filed at the time of each takedown by the CEO of the depositor that the assets in the pool will produce cash flows to service any payments on the securities as described in the prospectus.

Transaction documents to contain a mandate for the trustee to appoint a credit risk manager which would review the pool assets when certain trigger events occur. 

Purpose

• To avoid overflying on rating agencies adding “skin of the game”
• Stimulate inclusion of higher quality assets in ABS – risk retention

Opposition

• Depositor CEO could not be expected to have the knowledge necessary to certify the performance of the securities 
• The text of the certification could still be taken as a guarantee of future performance even though SEC believes certification requirement is *NOT* intended to serve as a guarantee of payment of the securities.
• Proposal for a 3-rd party opinion is complex, costly, and would not achieve its goals of strengthening the enforceability of representations and warranties regarding the pool assets.

Changes Disclosure Enhancements 

According to Reg AB, the issuer have to provide an incremental information - static pool data - so that investors can evaluate trends or patterns of the performance of specific types of assets originated at different points in time.

In addition, Reg AB 2 requires standardized data on specific loans and assets within the pool:

• both at time of registration (e.g., credit scores of the obligors and date through which interest is paid in residential MBS, dealer geographic location and vehicle manufacturer for automobile-backed securities, etc)
• and in an ongoing basis (e.g., whether an obligor is making payments as scheduled, efforts by the servicer to collect amounts past due, the losses that may pass through to investors, etc)

Static Pool Data Example

http://www.gmfinancial.com/investors-information/abs-information/static-pool-information/static-pool-data.aspx
http://www.stgeorge.com.au/corporate-business/institutional-financial-markets/static-pool-data
https://www.jpmorgan.com/pages/jpmc/ir/financial/abs/static/cc

Disclosure of Pool Asset Data

Purpose

• To bring more transparency for investors about the pool of assets

Opposition

• Originally the new asset-level data points would have been required to be publicly filed on EDGAR. – e.g.: geographic location, credit score, income and debt geographic location, credit score, income and debt. However, certain data points can disclose private information and break privacy law.

EDGAR - Electronic Data-Gathering, Analysis, and Retrieval system, performs automated collection, validation, indexing, acceptance, and forwarding of submissions by companies and others who are required by law to file forms with the U.S. Securities and Exchange Commission(the "SEC"). The database is freely available to the public via the Internet (Web or FTP).

Disclosure and Privacy Issues

Instead, later SEC proposed requiring disclosure of ranges or categories rather than exact information with respect to the obligor’s credit score, income and debt.

Also SEC considers whether sensitive asset-level data to be made available to investors and potential investors directly by the issuer on a website, rather than being filed on EDGAR.

Under the SEC proposed approach, asset-level information that does NOT implicate privacy concerns would still be filed on EDGAR and made available to the general public.

Purpose

• This approach allows SEC to get away from the need to resolve the privacy law issue in particular the Fair Credit Reporting Act.
• Fair Credit Report Act limits the ability to share some asset level data but in exception for disclosure to potential investors
• The SEC staff believes that issuers are best suited to determine who is a potential investor and whether the chosen method of dissemination falls within the exception.

Opposition

• The privacy issue does *NOT* disappear since issuers have to implement their own procedures of information delivery and privacy controls. E.g. by requiring user registration and accepting an agreement not to reverse engineer the data. 

Disclosure of Cashflow and Simulator

Additionally the proposal requires issuer to file waterfall computer program giving effect of flow of funds from the assets to the investors.

E.g.: how borrower’s loan payments are distributed, how losses are divided among investors, when administrative expenses are paid to service providers

The computer program would be filed on EDGAR in the form of downloadable source code in Python.

Investors could use the computer program to:

• perform cash flow simulations (based upon assumed interest rates, default rates, prepayment speeds, etc.), 
• generate present value estimates for ABS and 
• monitor ongoing performance. 

Purpose

• The computer program is intended to provide investors with a tool to perform their own quantitative analysis and be less dependent upon third parties.

Opposition

• The requirements for the “waterfall” program are not clear so far.
• Commenters also raised liability concerns. 

Privately-Issued Structured Finance Products

Require that in any private offering of “structured finance products” investors have the right to obtain all of the same initial and ongoing information as if the offering were SEC-registered.

Private are offerings made in reliance on Rule 144A under the Securities Act of 1933

However, according to the most recent comments it suggests that asset-level disclosure in the private market will not be driven by Regulation AB II, but by investor demand. 

Whether new disclosure requirements for Rule 144A offerings will be included in the final rules when adopted by the SEC remains to be seen.

References

1. Re-Opening of Comment Period for Asset-Backed Securities Release, SEC Release Nos. 33-9552, 34-71611, available at https://www.sec.gov/rules/proposed/2014/33-9552.pdf
2. Asset-Backed Securities; Proposed Rule, SEC Release Nos. 33-9117, 34-61858, 75 Fed. Reg. 23328 (May 3, 2010), available at https://www.sec.gov/rules/proposed/2010/33-9117fr.pdf 
3. http://www.bingham.com/Alerts/Files/2010/04/A-Guide-to-the-SECs-Proposed-Revisions-to-the-Rules-and-Forms-for-Offerings-of-Asset-Backed-Securities 
4. Re-proposal of Shelf Eligibility Conditions for Asset-Backed Securities and Other Additional Requests for Comment, SEC Release Nos. 33–9244, 34–64968, 76 Fed. Reg. 46948, Aug. 5, 2011), available at https://www.sec.gov/rules/proposed/2011/33-9244fr.pdf
5. http://www.bingham.com/Alerts/2011/08/SEC-Re-Proposes-Shelf-Eligibility-Conditions-and-Filing-Requirements-for-Transaction-Documents-in-Offerings-of-Asset-Backed
6. http://www.bingham.com/Alerts/2014/01/SEC-to-Consider-Adopting-Regulation-AB-II-on-February 

FAQ in Quantitative Finance

Reading now "Frequently Asked Questions in Quantitative Finance" by Paul Wilmott. It contains lots of fundamental terms and lets the reader understand principle ideas of different approaches, methods, models, etc. It encourages the reader to get a broad picture of problems solved by quants.
Recommended!

Modern Portfolio Theory References

Recently I've got to refresh MPT knowledge and hence there are few references to share.

Biography 

• http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/markowitz-bio.html
• http://news.stanford.edu/news/2006/june7/memldant-060706.html 

MPT and Markowitz Model 

• http://mertens.com.ua/books/files/finmrkts_ch06.doc
• http://www.thedigeratilife.com/blog/index.php/2009/05/14/modern-portfolio-theory-manage-risk-diversification/ 

Operations Research 

• http://en.wikipedia.org/wiki/Quadratic_programming 
• http://en.wikipedia.org/wiki/Lagrange_multiplier 

Python Implementation

• http://travisvaught.blogspot.com/2011/09/modern-portfolio-theory-python.html
• https://github.com/tvaught/experimental/blob/master/portfolio_metrics/mpt.py

Trade Lifecycle

Thousands of people in financial industry are explicitly involved in financial trading all over the world. Hundreds of thousands are involved implicitly. Based on my experience the percentage of people consciously doing their job is not high. Nevertheless it is highly important to understand at least the high level principles of process happening behind the scenes of financial institution when a trade is done.

Who Works on the Trade?

First, it is necessary to understand what stakeholders work with the trade during its life.

Trade Info Structure

Below you can find a sample of trade attributes structured into different categories (the trade is not real):

• General
• Identifier: E54123
• Asset class: Equity
• Type: Spot
• Status: Awaiting confirmation
• Trade date: 3 June 2009
• Transaction time: 11:09 GMT+1
• Transaction location: London
• Economic
• Buy or Sell: Buy
• Notional: 20 000
• Ticker: CAD
• Exchange: LSE
• Currency: GBP
• Price: 15.27p 
• Sales
• Salesperson: Elizabeth Smith
• Sales credits: 150 
• Legal
• Jurisdiction: UK 
• Booking
• Desk: Equity trading
• Trader: John Baker
• Assistant: Mark Eton
• Trading book: GBP Equity trading 
• Counterparty
• Counterparty: The Bank Address: Liverpool st, London
• Payment Type: SWIFT
• Payment Code: UIT TRY XXX
• Counterparty reference: LCE1985-04B
• Settlement Date: 5 June 2009 
• Timeline
• Trade date: 15 June 2009
• Settlement date: 17 June 2009
• Maturity date: 15 Sep 2009

Life cycle

Different stakeholders work with the trade at different time. This is similar to a conveyor at a plant.

Black Scholes Option Pricing

Background

Major break-through in the valuation of derivatives came with two finance professors at MIT, Black and Scholes, came out with a formula that related the price of a call option to the price of the stock to which the option applies. Even though the model is not used by financial institutions today it still contains ideas that used in financial modelling.

The Black-Scholes formula is a partial differential equation that can be used to price the present value of an option under certain assumptions. The equation describes the Markov process of underlying asset price and it looks as given below:

There is an analytical solution of the equation, the walkthrough is given in this video:

Monte Carlo Simulation

Assuming the Brownian motion over this short period will be a normal (Gaussian) distribution with a mean of 0 and a variance of the time interval the iterative formula will be as follows:

Let's take the case of Option Call holder. The favourable cases of underlying asset movement (F) is given on the picture below:

Below you can find an implementation of Monte Carlo simulation of option pricing in python.

The program will generate a number of asset price paths. At option expiry time obviously the price will differ. Option price then will be the average of all gains received at expiry time.

Usefull Links

1. J.Hull Futures, Options and Other Derivatives 
2. http://pyvideo.org/video/1154/derivatives-analytics-with-python-numpy-0
3. https://www.enthought.com/store/ 
4. http://www.python.org/download/releases/2.7/ 
5. http://www.youtube.com/watch?v=i0sGAds8ztI&list=TLJV-9_NNgcRU 
6. http://www.personal.psu.edu/alm24/undergrad/bingqianMonteCarlo.pdf 
7. http://www.automatedtrader.net/glossary/Black-Scholes

JP Morgan Heads HPC

JP Morgan is now able to run risk analysis and price its global credit portfolio in near real-time after implementing High Performance Computing (HPC) capabilities.
Prior to the implementation, JP Morgan would take eight hours to do a complete risk run, and an hour to run a present value, on its entire book. If anything went wrong with the analysis, there was no time to re-run it. It has now reduced that to about 238 seconds, with an FPGA time of 12 seconds.
Read full article here

Parallel Programming for Quantitative Finance

Investment banks like calculations that require powerful computational resources. In many cases Monte-Carlo simulations are run on huge GRID systems that cost a lot.
Such systems are usually home grown and really look expensive to replicate elsewhere.

Few monthes ago I read an article in RISK magazine that outlined the same problem and as an option a  chipper and more flexible approach was mentioned that is based on multi-core CPU and GPU.
http://www.nvidia.co.uk/content/EMEAI/PDF/risk-magazine-tesla-april2013.pdf

GPU programming probably is not too complex although it definitely requires some background desk devs or quants might not have. A company Xcelerit http://www.xcelerit.com/ made an attempt to ease the parallel programming. They provide SDK that allows quants develop and execute their C++ programs on a high-performance environment.

Furthermore they have a library that comes with base statistics functions, market data adapters and a number of interfaces for commonly used software packages, e.g. MATLAB, Excel.
http://www.xcelerit.com/xcelerit-forges-new-tools-for-quantitative-finance/

They also outlined some case studies of inefficiencies of sequential program execution on a GRID compared to multi-core CPU architectures.
http://blog.xcelerit.com/efficiently-using-computing-grids-in-the-financial-industry/

Examples of SDK usage
http://blog.xcelerit.com/xcelerit-sdk-user-experience/

HSBC usage
http://blog.xcelerit.com/hsbc-run-risk-in-real-time-with-xcelerit-and-gpus/

Let's see whether this attempt of Xcelerit will have a successful continuation.