The R community is in luck because every day there are more and more companies and institutions which develop packages to link their products and R. That is one of the consequences of the expansion of R between the data scientist community and also reinforces itself through a feedback loop. The more interfaces with data sources, the more users switching to R; and then, the more companies interested in R. That is what in business jargon is called a virtuous circle.
Recently I have been playing with «Shiny» and I have thought that a visual interface for the SREMBI could be a good idea to get started.
As you know, the SERAMBI is based on 4 variables for each of those I have set beforehand a weight and a long term equilibrium following some conventions. However, these assumptions are not the divine truth, but the user can play a bit with the values to get a better understanding of how the SREMBI works and maybe to find another equilibrium.
So, here is the Shyni App.
The SREMBI goes down slightly during the last quarter of year considering the latest data available, from 1.01 to 0.97. However, analyzing the global trend, we can observe a tendency of the index to move around the equilibrium during the last two years, since 2012Q1. In essence, it means that the bubble already finished in 2011 and since then, the Spanish Real Estate Market has been living, let’s say, in its balanced zone.
During the peak of the financial crisis, regulators around the world moved quickly to ask to the banks for an aggregate exposure of their risks. Up to that moment, Banks usually faced the different risks on isolated departments without any communication between them. The thing worked as follows: The Credit risk department tried to develop complex Credit Risk system in order to classify their clients and separate the wheat from the chaff. The Treasury department hired a bunch of Market Risk Analyst in order to keep the Mark to market under control and the guys of Operational Risks, who usually worked in a different floor and nobody knew what the hell were they doing, tried to make up a methodology that brings out a reasonable data. At the end, the Senior Risk Manager in a rush, pulled together all these information on his MS Excel spreadsheet, added and ready. That used to be all.
This system broke down because of a lot of reasons, but, fortunately, few months later the Basel committee attempted to address that problem by publishing a document: “Principles of effective risk data aggregation and risk reporting”. It sets out core principles around the aggregation of risk data. So, it’s a big deal because for the first time in banking regulation there are explicit requirements for accuracy, completeness and timeliness.
Among all papers about Real Estate market – And believe me, I’ve read quite a few- the UBS quarterly reports about the Swiss Real Estate market is the smartest, the clearest and the most fine-tuned that I’ve read ever. Quarterly, they analyze several variables linked with Real Estate market and mixing them up, build an index called “UBS Swiss Real Estate Bubble Index”. The mission of such index is to summary a bunch of financial and economic information in order to clarify the situation and to show clearly what too many people sometimes simply don’t want see.
I agree with those guys. I also think that Switzerland is facing a new Bubble. On their last report, they already warned that the Real Estate Market had come in a risky stage that maybe doesn’t have way back. Actually, the Swiss’s experience also say that once you reach the “risk level”, the only way to come back to equilibrium is to go through a bubble. It happened in the lately 80s and could repeat again. Furthermore, if we analyze other economies, through each Real Estate bubble, the same pattern repeats itself. It seems that the Real Estate market is passionate market that once someone heats it, it’s impossible to stop it.
The same happened in Spain. We were fliving inside a bubble during years but nobody wanted to realize of that and we let it explode alone. I wish we had had a Real Estate market index like the UBS’s index. Perhaps, all would have been different!
Most of the Swiss residents living close to the borders ask themselves how is possible that by crossing the border one can buy the same thing much cheaper. Last Saturday afternoon for instance, I went to Konstanz (Germany) to have a walk for the City Center, visit the Christmas market and drink a couple of Glühwein. I left my car on the parking of a big Shopping Center and I calculated that more than 70% of the cars parked there had Swiss plates. I guess that all of them are attracted for the same, the low prices. Of course, I also took advantage of the occasion to buy a pair of shoes which I had already seen in Switzerland one week before. By doing this, I have saved more than 20% of their value.
How is it possible? Further considering that Germany, Austria, France or Italy are not exactly cheap countries and, besides, their VAT is in all cases much higher. The answer is on the peculiar evolution of CHF foreign exchange. I published this graph on my last post, where we can see clearly this abnormality.
When I face a new post, sometimes, I waste the most of the time trying to get the data. The same happens within a company or in academic world. If any of you has ever been doing academic research or developing financial models, knows that get the data, load it on your working platform and have it ready to run is part of the way.
Today, let me show you two easy steps to download financial data from internet into an Excel spreadsheet through VBA and after that, how to import this data set into R through a new package named “XLConnect”. To do so, I have taken the EUR/CHF foreign exchange as example.
In the summer of 2008, as a consequence of the events of Freddie Mac, Bern Sterns and Lehman Brothers, a new concept broke out on economics supplements: “Too big to fail”. This new concept defines the threshold from which a financial institution is so large that one hypothetical bankrupcy might bring down the whole economy with it as well. These kind of institutions, also named “Systemically financial institutions” (SIFI) have been addressed specifically on the new Basel III framework. The focus of the new developments has been to raise bank capital requirements and to establish capital surcharges for these SIFIs . The BIS Committee (through the Financial Stability board) also released a closed list of these SIFIs (see here), in which are the two big Swiss banks.
However, last month, the SNB announced that Zürcher Kantonalbank (ZKB), the country’s biggest cantonal bank, also represents a systemic risk to the country’s financial system (See here). Why has SNB taken such decision? What does it mean?
Last week, thanks to one LinkedIn contact, I found out that Nassim Nicholas Taleb had released a new book. A first draft is available for free here. The new book – named “Probability, Fat Tails and Antifragility: Elements of Risk Engineering” – is a technical development of the issues that the author has been explaining in her last books. I haven’t had enough time to assimilate the whole information yet – likely one needs the entire life to understand it – , but in spite of that, I’d like to stand out some ideas that I share with the author.
Volatility is one of the key points of finance and just for this reason academics and finance professionals have come up with lots of different approaches to tackle it. Among all of them, for me, the implicit volatility is the most elegant way to figure it out. I should admit that its mysterious smile always has seduced me. So, now that the SNB has settled that UBS is a healthy bank again, I have decided to have a quick look and check out if UBS is smiling again.