The CVA Diary - Some Definitions

Some of the blog readers may have recognised what the pictures I posted on last physic's friday have been about. I am currently working on the implementation of a xVA simulation, where xVA stands for all kind of VAs (CVA - Credit Value Adjustment, DVA - Debt Value Adjustment, BCVA - Bilateral Credit Value Adjustment and FVA - Funding Value Adjustment).
But what are these VAs about: Counterparty risk represents a combination of market risk, which defines the exposure, and credit risk, which defines the counterparty credit quality. Part of the global regulation arising in the aftermath of the global financial crisis (e.g., Basel III) seems to view CVA as necessarily being a marked-to-market trading book component, alongside the OTC derivatives position from which it is derived. The risky price of a derivative can be expressed as the risk-free price (the price assuming no counterparty risk) minus a component to correct for the counterparty risk. The later component is often called CVA.

In next weeks physic's friday I will start with the description of the exposure engine - the heart of each xVA simulation.

C … what it takes to change

is the topic of the Ars Electronica Festival 2014, taking place in Linz from September 4-8. It is an inquiry into the conditions necessary to enable innovation and renewal to emerge and make an impact. C in creativity, but also in competition and collaboration (that seem to be conflicting ..). My favorite C its co-evolution.

The AEF is a yearly festival in Linz founded 1979 with the idea of understanding arts as catalyst for technologies. It embraces a symposium where  the big names and stars tell the young wilds that they know more … This might be a dilemma with this year's topic. Talking about changes should include change of methods, lectures, mentors, … and other popular idols killing innovation.

Interestingly enough, I found various contributions about the dilemma of change and elitism - is popularity identical with importance? … in the last days.

Excellent Sheep

Nobel Laureates, halo effects and idiosyncratic markets

I love math and I hate the fields medal

Assorted in blogs about economy (and finance).

From Goat Discrimination to Excellent Sheep

Two assorted links related to education have been posted at the Marginal Relolution Blog - 24-Aug (on the Goat Discrimination) and 25-Aug (on Excellent Sheep).

In a way it touches the polarity of education and inspired me to write this post.

Denunciation of the Ivy League?

Find a performance metric for a (higher) education system its not easy - you usually need to pick two of three goals.

William Deresiewicz has released his book Excelent Sheep this August. And it seems it has provoked a firestorm. Not surprisingly. Pointedly speaking, it does suggest: "don't send your kid to the Ivy League".

I am not an educator, not a didactics expert, not a neuroscientist, not a psychologist … I have not read the book yet (only summaries and reviews), so I do not assess it. However the semantics of Ivy League may suggest that the critics is driven by the resentment of the excluded. Or, denouncing the Ivy League is Ivy League style ….. but I find, this are weak arguments in principle.

There is one thing I do understand: the uncompromising focus on one goal in the triangle - highest curriculum standards and not a high pass rate and not giving everyone a chance to enter -  provokes a controversy, inevitably.

If performace-based funding of educational systems uses such a focused metric it may lead to an "elitary spiral"?

Popular idols may kill innovation?

Some neuroscientists claim that we cling too much to idols, like lectures, mentoring, even scientific methods, … as well as competition and performance reviews.

The didactics research of the last decades suggests the change from teacher- to learner-centered learning arrangements - the underlying philosophy is old, and called constructivism.

Newer is, that there is technology that supports explorative learning - in maths education we know the black-box / white-box principle: for motivation experiment with a black box and get a rough insights into a new mathematical topic, then acquire inside knowledge by learning what are the mechanisms behind (white box) and use a black-box intensifying knowledge in a later stage.

A technology supporting this principle is symbolic computation. The principle is recursive because "white" becomes "black" in a later stage and so on (a white-box may use black-boxes of earlier stages as building blocks).

And collaborating with people from other fields will help exploring new things.

Is it more important to have a high IQ or to use it? 

Brightest minds or hyper specialized professors, … ?  Today I found this interesting post: Nobel laureates, halo effects and idiosyncratic markets.

I don't see these things as denunciation or critics, … I see it as challenges. So I do not understand the firestorms.

Reverse education

Meeting with the UnRisk Academy heads from time to time, I really enjoy talking about things that are not in the core of our business. Like, how can nine-year olds be motivated to learn much more about mathematics - equation solving for kids.

This leads to new ideas about our learning offerings for quants and risk experts .. and the technologies that serve them.

In the magic-forest-of-learning, a "Goat" may be transmuted into an Excellent Sheep or a Majestic Lion of original thinking ….? It's our choice.

Math without formulas

I am currently attending the IPTA 2014 conference in Bristol (IPTA standing for Inverse Problems - From Theory to Applications). Yesterday, I gave my invited minisymposium talk on "Large Scale Adaptive Optics". I had challenged myself to give a presentation to a specializes mathematical community without the use of a single formula in my slides.

I succeeded, and, not surprising to me (as I have seen too many talks showing battlefields of mathematical formulas), my talk was very well received by the audience. 

A recommendation: If you ever have the chance to see a talk by Samuli Siltanen, grab this chance. He is a brilliant and witty presenter.

Need to Pick Two of Three Things?

This post was inspired when walking through online education portals after the goat discrimination joke.

You can't have everything? In the evaluation of products, projects, systems, ... you often seek a "performance metrics" and it seems to be organized by a kind of triangle - you want

• High Standards
• High Production
• Flexibility

In education this may mean 

• High Curriculum Standards
• Low Dropout Rate (high pass rate)
• Open Access

From this three goals of education you can only pick two. If the curriculum is difficult, how can everyone pass? You need to select before the entry (difficult enough). Similar with the other pairs.

in discrete manufacturing

• High Product Quality
• High Throughput
• A Broad Product Mix 

If you want precision and low time-in-process you usually choose a flow-orieneted, highly automated production line that is optimized for a certain class of parts. If you want a broad part mix, you organize intelligent manufacturing islands that can do many operations for a broad variety of parts in one set up in parallel. You get much-work-in-process but parts stay longer in the system.

In computing we have more flexibility, because computers are not mere tools but universal machines.

What about quant finance?

At UnRisk we did our best efforts to offer benefits in a triangle that has goals that are not in conflict

• High Quality of Risk-related Information 
• High Automation (Throughput)
• Large Diversified Portfolios (Broad Coverage)

This is enabled by the intelligent combination of the latest own and purchased technologies and the clever utilization of new computer muscles. Based on the underlying principle of optimality, multi-strategy approaches, clever maths and open information.

It is not easy and one would expect it has its price. But we offer it for low license and operational cost.

This Monday, Michael has posted about how this works in one example: Combing UnRisk products with Web Services. Products and technologies orthogonalized.

Not Open To Goats

HINDS Community College propagates its out-of-the-classroom educational opportunities. But they are expressively not open to goats. A link found here.

The UnRisk Web Service: Combining our Products

Today I want to give a small example on how the UnRisk Products may be combined in an elegant way.

On the one hand we have the UnRisk FACTORY, a web application which allows, in a very convinient way, to

• Set up Portfolios
• Perform automized Stress Tests and VaR Calculations

with all the corresponding data (portfolios, instruments, models, calculation results) being stored in the Database.

On the other hand we have the UnRisk PRICING ENGINE, which enables the user to

• Perform Valuations within Excel

BTW: Both products use the same Pricing Library: our Mathematica package UnRisk-Q.

Taking a look at the different Advantages of these products

• UnRisk FACTORY: comfortable user interface, automization, permanently storage of data in a database, easy access to all the data
• UnRisk PRICING ENGINE: flexibility of Excel

we thought of a possibility to combine these two products.

SOLUTION: The UnRisk Web Service.

It enables,

1. via UnRisk-Q functionality, the user to bring all data, which is stored in the UnRisk FACTORY database, to Mathematica.
2. via our UnRisk Link for Excel, to bring this data to the Excel frontend

I will describe 1., the UnRisk-Q Web Service functionality, in a later blog – today I am concentrating on 2., Import to Excel:

Now we have reached our starting point: we have set up a workflow (UnRisk FACTORY …. UnRisk Web Service … UnRisk-Q … UnRisk PRICING ENGINE) on top of which we can implement a lot of new functions, like, e.g., the following (and this, finally, is my example ;-) ):

Take the expected cashflows of all underlying instruments of a Portfolio and display the aggregation of these cashflows in a list of given time intervals.

Here are two screenshots:

Screenshot 1: Result of the Portfolio valuation (UnRisk FACTORY)

Screenshot 2: Result of the Cashflow Aggregation in Excel

At the end I summarize the steps needed to get these results to Excel:

1. Set up the Portfolio in the UnRisk FACTORY (can be easily set up by the FACTORY user)
2. Set up an automatically executed (e.g. every day) task to perform Stress Tests on this Portfolio (can be easily set up by the FACTORY user)
3. Use the UnRisk Web Service (installed in the  the UnRisk FACTORY environment) functionality to extract the valuation results from the UnRisk FACTORY (this functionality comes with UnRisk-Q)
4. Write some simple Mathematica code to aggregate the Expected Cashflows
5. Use the UnRisk Link for Excel (comes with the UnRisk PRICING ENGINE) to bring these results to Excel

The needed UnRisk software is delivered to our UnRisk FACTORY (the UnRisk FACTORY database is the basis of the UnRisk Web Service) customers in one bundle.

I am very happy that we found a way, the UnRisk Web Service, to enable our customers to combine the UnRisk products so that they get the optimal benefit of the different "worlds".

Sunday Thought - The Good Use Of Computers

MIT Professor David Autor has written about labor market polarization discussing the paradox of Michael Polanyi: "We can know more than we can tell ..".

No, I will not contribute to the "end of labor" discussion, because I do not have the knowledge, informative data … and at the other hand, I think the problem its too complex for predictive modeling. I rather recommend to think: provided it happens, how can we get used to it.

But the paradox inspired a thought:

As a car driver, I cannot be copied by a machine?

I am a great car driver, but I have no theory about great car driving. I cannot tell you, why I'm able driving smooth through curves without fidgeting ... It's an implicit knowledge that cannot be replaced by a computer program … but hold on, why do car drivers cause accidents in the worst cases stop lives?

How, do I react to a moose suddenly trampling out of the bushes? As usual, make way and clash with another car …?

But, self-driving cars will save lives

Why? It's less about cars and their controls, but information and communication, connected local intelligence, learning and adaptation.

It's about sensors that provide much more information a driver could capture, about blazingly fast reactions much faster than those of humans, imaging technologies that can see much further and deeper and anticipate danger.

And if not, machines do not have a "social brain". Consequently, the self-driving car may decide to clash with the moose in a certain way as the best of a all possible solutions.

The polarity of computer use

It's common sense that computers are great in doing routine jobs faster and cheaper. But computers can do things we cannot do properly - solve extremely difficult problems in time.

So it might be wise to flip the human-machine interaction: let computers overtake, if situations become really difficult and unusual behavior is needed. Build und use computerized systems that can overcome Polanyi's paradox.

(Financial) risk management is a field where this flip should happen. Currently not so few market participants are spending tons of money to install systems that guide them through situations where the conditions are well known and dangers are cleared out. But what about the situations where dangers are greater and less known? Situations that keep risk manager awake at night?

Putting computers to good use must include the risky horror.

Shhhh - Coming Soon

This week I will interrupt my series of blog posts dealing with quantum finance and give a picture puzzle which I will resolve next week.

Use the pictures below to find out on what really exciting topic I am currently working on ...

The Advantages of Dyslexia

"There are three types of mathematicians, those you can count and those who can't". A bad joke. But when I studied algebra, I sometimes felt a "dubious, shady glance" was needed to see the "impossible structure".

Why with reading difficulties can come other cognitive strengths is excellently explained in this article of the Scientific American.

Maybe it's a kind of frogs and birds trade off. People who have difficulties digging in the details think differently and understand the big picture better.