What is and how to use a data lake


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From storing huge information on economic parameters, market prices, customers behaviour, stress tests definitions and results, compliance legislations and rules, the financial sector and not only it tends to become a huge consumer of something that is called a data lake. Especially in a highly integrated risk management era and in a need for the fusion of multiple knowledge sources, when data pools are already there or available, data lakes are cost- and time-effective solutions coming in, both for data that you think you would like to analyse and for invading real time data.

Accordingly to its formal definition, a data lake is a storage repository that holds a vast amount of raw data in its native format. It means that data is not pre-categorized at the entry point and therefore, especially in online analytical processing, no optimal form is dictated by the fact that it has to support specific types of analysis. A data lake holds a vast amount of events.

The data lake solution provides a platform for a historical type of archive. It contains data from many different sources, with people in the organization being free to add or update data to the data lake. One launches Google type queries and then provides additional fields one may create and identify, searching interactively and expanding the description of the structure of the big data at the same time.

Its architecture seems to involve five components:

  1. A double historical layer that gives information on all historical data. The batch layer has investigating services to search, locate, and access the historical data lake. The results are periodically re-computed and cached in a serving layer. One can use for example Hadoop to sift through the data and extract the chunks that answer the questions at hand, eventually replacing OLAP (online analytical processing).
  2. A speed, on-the-fly layer that runs searching on updating, fresh data (say maximum one hour old), in real time, at low latency, eventually starting from a cached resulted field. It queues and streams the data, while updating the data lake, giving a view on the most recent data and favouring decision takings.
  3. Lake services, which prepare, integrate and store the information from the data lake in loose pre-definable historical and on-the-fly double catalogues. With the searching results becoming available, new relationships are created between different sources of big data. Here data maybe safe and properly protected via tokenizing, encryption, key management and security audits.
  4. A data reservoir, where you check the reliability and do the cleansing of the data, where people in the organization may access as necessary. There the data is prepared to answer specific questions. Through this type of container, big data actually becomes useful
  5. A reactively managed engine to handle the real time constraints (an engine programmed to respond to the events, to scale to multiple cores and multiple server nodes, to be reselient to software, hardware and connection failures and to react in real time) that provides the libraries for the analyst experts to do their work.  With loosly coupled event handlers, as in reactive programming, the actual location of data looses importance from the tracing/functionality’s point of view and the data analysis gains in scalability and in event triggered response to the final user’s request.

The last layer actually represents the presently used software, with about three times more processing time being currently consumed by unautomated data sorting to make the data’s usage possible.

The data lake concept is about 5 years old, and there are not yet clearly differentiated vendors of complete data lake solutions.  Let them be  financial solutions or not Financial ones. Everybody is at the very beginning, but everybody is equally in the very need of competitive advantage. One visualises the financial data in the way that is understood and used at its best, as the V^3 approach (variety, velocity and vagueness of data) in operational risk.

Whatever the final goal is, clearly identifying what a data lake software concept might be about, in my opinion, a very good starting point.

Negative interest rates move to real world


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About one week ago the Danish Nordea Kredit started paying mortgage holders to borrow money by charging a negative interest rate. When doing short-selling of shares in the stock market, you promise to sell somebody shares for a price lower than they are selling for, because when the time comes to deliver the shares you are expecting to pick them up cheap. From financial point of view, the negative interest rates mortgages could be represented as a short sale of currency, in the context of a deflationary economy.

Mathematically, the credit risk spread can be mapped, through averaging, to the probability of default. A negative spread would then translate to negative probabilities. For the very moment, the thing that jumps to my mind is what Paul Dirac used to write more than 70 years ago: « Negative energies and probabilities should not be considered as nonsense. They are well-defined concepts mathematically, like a negative of money. » Negative energies do get employed in particle physics. Dirac associated them to antiparticles/holes, which do conceptually resemble to short selling. Anyway, there is a more terre à terre immediate explanation. For the negative interest mortgages, technically the negativity is covered by the negative yield of the mortgage-backed bonds that then the bank emits. In the very end, it is supported by the final investor, as a plus profit is taken by the bank from the spread between the two.

But what about the negative yield corporate bonds, which start to emerge? Technically again, the risk free interest rate is considered to be the rate offered by the central bank. If this rate is negative, investors loose less by investing into a negative yield bond. But why would somebody want to lose money, by exchanging more money now for less money in the future? It looks like the premium you willingly pay for your own future liquidity or, otherwise, against your own probability of default (debt valuation adjustment). When a house builder takes/validates a credit from a bank, a hole is created, representing the money he owns to the bank. The builder’s hole versus the bank is filled in by the money he receives from a new house owner, who has built his house by borrowing money from the same bank. The bank, via the money, transfers the hole from the house builder to the house owner. Further assuming that everybody has debts, money operates as the visualisation mechanism of the hole, which travels in the opposite direction (when money enters, the hole leaves). When money does not fill in any hole (the money owner had no debt), it is only useful as it represents the fill in for future holes or future debts that the owner will encounter. In Feynman’s terms, who gives a second interpretation to negative energies, money represents a hole/debt in the future that travelled back in time, to the present, and future liquidity can be seen as a reflexion on own credit risk, like when paying for pension/retirement.

There is a lot of debate in particle physics on what antiparticles do represent and how negative energies can be made concordant with positive mass. And this is why maybe one could at least use the available mathematical recipes to deal with current financial events. As in order to produce an item one needs to start from primary ingredients, standardly valuated in money, a functioning, producing economy can be naturally assimilated to a Dirac sea of holes. And one can also seem to be naturally happier to pay for being able to invest the money and eventually be taxed on the gain (which is  none under negative investments), than for keeping the cash at zero rates and being taxed on the whole fortune. This does make the mathematics really less difficult understanding.

Willl the Swiss National Bank print more CHF?


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Negative interest zero coupon bonds are back for Switzerland. Buying bonds with negative yields can be due to two reasons:

  • We make the assumption that the value of the returned CHF currency, at the bond’s maturity, would be increased.
  • We make the assumption that the currency remains as it is, but any alternative investment presents a larger perceived investment risk (risk of credit, market, liquidity…etc)

The first reason is hardly a very strong one: given the 20% already appreciated value, there is not much room for the CHF currency to grow. About the second reason there is not much that can be done: the perceived alternative risk, be it fear or real, is an inner believe.

A way to stop the strong CHF value is the SNB to print money. But this leads to CHF, internal, inflation which, in long term, gradually erodes the competitiveness of the Swiss exporters, due to increased internal prices. Leaving the things as they are, this might be harmful for exporters: for the same quality, people will prefer to buy cheaper products. The words to stress here are “for the same quality”. To shield the CHF value increase, there is not the SNB to react, but the exporters, improving on the quality of exported items  and “Made in Switzerland” becoming a stamp of quality, if it not already is.

In any case, one of the  International Monetary Fund’s stress tests, published in September 2014, implied « resumption of ‘safe haven’ inflows to Switzerland, leading to a reassessment of the existing exchange rate floor » and output growth in Switzerland falling to due to the appreciation of the Swiss franc. And non-systemic Swiss banks proved to be sufficiently capitalised for such conditions, while the systemic ones had large remaining capital buffers, exceeded the currently defined capital ratios.

Crude oil as virtual currency unit


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Since July 2014 the crude oil prices keep going down and OPEC looks as not being in a hurry to change the oil extraction policy. Both spot and futures prices continue to fall but, in my opinion, them leading to a world-wide deflation is only one of the points of view. Because nothing forbids us to perform a change of measure, from the governmental currencies unit to the crude oil unit.

The convenience yield is the benefit or premium associated with holding an underlying product or physical good, rather than the contract or derivative product, due to its relative scarcity versus high demand. Looking at today’s prices of the crude oil futures, they are continuously increasing with the time to expiry:

JAN 2015 56.86

JUL 2015 58.59

JAN 2016 60.64

JUL 2016 62.32

JAN 2017 63.45

It seems that the American oil market is not in contango and just the international one is, so the storage costs are neglected. For exemple the oil tanks in Cushing peaked at more than 50 million barrels in 2013, but the amount of oil being stored in Cushing has fallen by 60 percent, to around 20 million barrels. This is why I have thought that the (increase in) storage price does not contibute to or explains the future prices.

Starting from the simplified formula for future contract prices

(future price) = (spot price) x exp[(cost of carry) x (time to expiry)]

where the cost of carry is the risk free rate minus the convenience yield, and based on the increasing future prices, the average convenience yield is overall smaller than the average risk free rate. As speculators intend to do nothing with the oil itself and spot price is since quite a time low, this supports the idea that market agrees to make a blind money units versus oil units swap within all the available maturities. Petrocurrency is not a brand new concept and one could always pay-at-time-T using future crude oil contracts expiring then. But, when using FX exchange against crude oil units via future crude oil contracts, the two arbitrary members of clearing house practically have to decide on an OVC-type time to expiry for a virtual transaction.

However, also end of July 2014, the Hong Kong Monetary Authority (HKMA) and the Bank of Thailand jointly announced the official launch of a new cross-border payment-versus-payment link between Hong Kong’s US-dollar real-time gross settlement system and Thailand’s baht-denominated RTGS system. Concomitantly with the announcement, Advanced Markets, with a virtual clearing network including Bank of America, BNP Paribas, Barclays, Citi, Deutsche Bank, JP Morgan, Nomura, Morgan Stanley and UBS, has launched trading in spot crude oil, Russian rubble and Thai baht. The offered cash instruments closely track trading in the related front-months crude oil futures contracts, and, unlike futures, there is no expiry date one has to agree on. Through this solution users can bid and offer to seek price improvement and generate client-to-client matches, the buy-side users baing able t to trade bank liquidity as well as prices from non-bank market makers.

Oil units are different from the conventional currency units because they are employed by using futures agreements –type contacts, rather than bearer bonds type. But they keep being negotiable and they are legal tender as the exchange houses make sure they cannot be refused in the final settlement. In addition, there is the practical, administrative advantage of making possible the equivalent of paying a negative interest rate on bearer bonds, being normal that spot and future prices fluctuate up and down. They also incorporate the mark to future market concept, enlarging the present mark to market one. The current low crude oil price experience brings the silver line that on long term one could move from an oil virtual, base currency to an energy one.

Anyway, with the lowered demand from China and steady US supply, such oil prices are here to stay. Longer time the oil sticks to such a price, more powerful its currency concept will sound. Taking into account the about 30 bn EUR of Chinese lending to Russian corporations, with lots of it secured by oil shipping to China, the oil currency equivalence is just an issue of sufficient usage.

Concrete elements of a Liquidity Contingency Plan


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The liquidity management should include a liquidity contingency plan, scenario planning and testing of the plan. But little guidance can be found on what liquidity warning indicators should be monitored and what explicit strategic action should be tested. This is why I have summarised the points below.

Warning indicators

The purpose of early warning indicators is to alert management to the possibility of an impending liquidity crisis so that action can be taken quickly and early enough to avert it. Their monitoring may include:

  • On the liability side:
    • unexpected and significant levels of withdrawals of retail deposits or non-renewal of wholesale funding facilities;
    • core retail deposit volumes falling below projected levels; and
    • a shortening of deposit maturities and a rise in requests to break fixed term deposits.
  • On the asset side:
    • retail advances growing faster than projected;
    • a lengthening of loan maturities;
    • larger than expected drawdown of committed facilities;
    • a significant rise in undrawn committed facilities;
    • a rise in defaults and delinquencies; and
    • prepyments of loan facilities falling below historic behavioural norms.

Strategies to Test

The steps that can be taken to improve liquidity and on which one should have a sound idea on how the market reacts include:

  • raising retail deposit interest rates;
  • raising loan interest rates to discourage new borrowings and stabilise the balance sheet;
  • usage of potential sources of funds;
  • transferring liquidity to the affected group entity;
  • capping balance sheet growth; increasing  on the offered products; and
  • issuing of public statements (both locally and internationally) to deal with reputation risk.

The contingency funding plan should test:

  • outright sales, or sales under repurchase agreements, of marketable assets;
  • drawdown of committed facilities;
  • funding from other group entities;
  • contingent liquidity swaps.

Also, customers may withdraw fixed term or notice deposits under interest penalty and one should factor these balances in.

Are SEPA payments filling for the Euro-denominated LCR?


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It is not only that using payments-on-behalf-of (POBO) structures under the Single Euro Payments Area (SEPA), the companies can practically run their entire Eurozone operations through a single euro-denominated bank account. But SEPA has established a single clearing system for the 34 participating countries.

The Liquidity Coverage Ratio is defined as the ratio between available High Quality Liquid Assets and the net cash outflows. The denominator is the difference between expected cash outflows and expected cash inflows and has a minimum value of 25% of the total expected cash outflows.

Assume that, overall, the cash inflows are expected to be larger than the cash outflows for a viable financial institution and assume the institution will use a unique euro-denominated account to settle the SEPA transactions through a Pan-European Automated Clearing House as STEP2. At extreme, this means that the financial institution will have only euro-cash inflows from the clearing house (or at least close to zero euro-cash outflows), which will make the LCR mathematically ramp to very large values.

This might make the euro-denominated high quality liquid assets (usually government debt) useless from the LCR perspective.

Haircut risk to be born by the central counterparties clearing?


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Basel strongly recommends Central Counterparties to be used, in order to manage the credit risk in Over The Counter contracts. According to the ISDA’s Variation Margin Gains Haircut (VMGH) mechanism (« CCP Loss Allocation at the End of the Waterfall »), it looks like this credit risk is going to be primarily covered by the Central Counterparty via  (haircuts in) the gains of the Clearing Members  which arise since the default is spotted. As the clearing is done via the Central Counterparty, there is always one of the two participants in the Over The Counter deal that is going to make a gain.

But this so-called Haircut (in the gains) Risk should show up somhow in the pricing of the OTC instrument, given that Over The Counter derivatives pricings are based on potential gains. Haircut risk looks like a type of credit risk, which appears conditional of the market (risk and gains), and might be triggered by and triggers itself liquidity risk, as the taken amount might be returned once the recovery done.  It is interesting how to price it, as it is a idiosyncratic risk (linked to the specific defaulting CM) which can be hedged, by making it market insenistive, with no future gains to show up.  But it turns out to be a hybrid with systemic risk, as it depends a lot on how many counterparties are defaulting, on average, within the Central Counterparty net.

Integrated risk modelling (credit, liquidity and market) becomes more and more necessary.

Time to develop financial instruments for negative interest rates?

Interest rates are artificially kept at very low levels in Europe, in a hope of relaunching the local economy. But, contrary of the desire to improve the liquidity of the market, this might be the very one to be hurt. Money market funds are withdrawing their assets from Europe and prefer to relocate them to locations where they can get a decent profit, given their risk profile. Especially during the liquidity crisis of the Credit Crunch, money market funds proved their usefulness in providing the so much needed cash to the markets. Without an important source of funding, how is the European banking going to react?

From 2004 to 2006 the ECB collateral in non-marketable assets represented less than 5%, while in 2013 it represented more than 30% of the total collateral value. On the other hand, the central government securities keep being in an amount more or less constant, while their percentage figures keep decreasing, from about 25% in 2006 to about 15% in 2013. If Europe wants to keep providing liquidity to the market, and if this is going to be done in exchange of even more non-marketable collateral, the real value of the funding it provides might not sustain a future financial shock.

The dollar lost even more of its credibility since the USA debt and budget issue being brought in the spotlight. If interest rates will increase in Europe, the EUR will appreciate even more, thus hurting an already fragile economy. On the other hand, with R&D in decline (only 9 out of the top most 100 high-tech companies located in Europe), the economy can not rebound by itself, outside of foreign investments. China, lately interested in investing abroad started doing not so well as before, to afford the pace of investing abroad.

Negative interest rates are one of the most viable options and represents, at least from financial engineering point of view, a blowingly rich subject.

What is the illiquidity of Bitcoins?


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When having a look at one of the virtual currencies trading sites (crypto-trade.com) it is interesting to notice that you can buy bitcoins for $815, but you can sell them at $753, while for the EUR it is worse: buy it at EUR 650 and eventually sell it at EUR 370. One has bought and sold about 1000 EUR-worth bitcoins, and about 4000 USD-worth bitcoins on 26th of November 2013. It definitely looks good from the point of view of the trading sites.

For 26th of November 2013, on intersango.com, at 7PM GMT there was a request

  • of 679 EUR for 1.79 BTC, with an average price of 377 EUR,
  • of 6568 EUR in further 58 Bc with an average price of 113 EUR.

Next interested buyer was for 1,000 BTC for 0.12 EUR each. This makes 60 BTC sold at an average price of 120 EUR.

  • If you have 100 BTC to sell, you will sell them for an average price of EUR 72.
  • If you have 1000 BTC, you’ll sell them for an average price of 7 EUR.

Given that there are already 12 000 000 BTC in total, and the price is exponentially descending with the sold BTC-amount, how could one say that the Market Capitalisation is 8,000,000, 000 EUR?

The value of a thing is the amount of money the other are prepared to pay and its liquidity. And, if you want to speculate on BTC, how much are your bitcoins really worth?

LCR impact on financial risk modelling


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“EBA FINAL draft Implementing Technical Standards on supervisory reporting under Regulation (EU) No 575/2013”, 26 July 2013, states that the Liquidity Coverage Ratio (LCR) has 31.03. 2014 as first reporting reference date, which is getting closer. But how should we get the reported values?

According to bcbs238 (“Basel III: The Liquidity Coverage Ratio and liquidity risk monitoring tools”, January 2013), one has to also look at the calculations of

  • credit risk: in credit risk calculations the negative exposure (we own to the counterparty) is set to zero, as it does not have an impact if the counterparty defaults. However, via the LCR, this negative exposure has to be considered as net cash outflow:

120. Increased liquidity needs related to excess non-segregated collateral held by the bank that could contractually be called at any time by the counterparty: 100% of the non-segregated collateral that could contractually be recalled by the counterparty because the collateral is in excess of the counterparty’s current collateral requirements.

  • market risk: if Option 1 is activated, we can borrow the missing amount of  High quality liquid assets (HQLA) at a fee, from a central bank. While the credit risk is technically not impacted by these new contracts, they will impact and generate market risk (interest rate and foreign exchange risks):

58. Option 1 – Contractual committed liquidity facilities from the relevant central bank, with a fee: For currencies that do not have sufficient HQLA, as determined by reference to the qualifying principles and criteria, Option 1 would allow banks to access contractual committed liquidity facilities provided by the relevant central bank (ie relevant given the currency in question) for a fee.

These two requirements imply an integrated credit, liquidity and market risk modelling in order to obtain consistent results.