1. Висше образование или студент с отличен … Continue reading →]]> UniversalOwner е фирма специализирана в разработката и внедряването на банков софтуер с централен офис в Люксембург и работеща с клиенти от цял свят (Азия, Африка и Европа). Търсим сътрудници със следните умения.

1. Висше образование или студент с отличен успех (специалности като математика или информатика са предимство).
2. Опит или силен интерес към банковата сфера (бакофис, управление на риска, финансов контрол).
3. Отлични компютърни умения (SQL, Linux OS, Office Software, Internet).
4. Опит в документиране на софтуер е предимство.
5. Умения за работа с клиенти и опит в поддръжка или внедряване на софтуер са предимства.
6. Отличен английски, допълнителни чужди езици са предимство.

През пробния период (3 до 6 месеца) ще помагате за изграждането на цялостния пакет около нов софтуерен продукт (документация, презентации, implementation tools, add-ons,…). Работата е предимно дистанционна и една от целите е да се запознаете със специфичната материя на продукта. В последстие ще имате възможност да се присъедините към екипите ни които внедряват продукта (при условие че сте мобилен worldwide) или евентуално да продължите като продуктов мениджър.

Предлагаме прилично заплащане и работа в малък, но сплотен екип.
Моля подробна автобиография на jobs@universalowner.com

Enjoy reading also the whole magazine here.

UniversalOwner specializes in quantitative risk … Continue reading →]]> The business maganzine ‘écho des entreprises’ features UniversalOwner in its March edition. The interview is a nice summary of the Frequently Asked Questions about us.

Enjoy reading also the whole magazine here.

UniversalOwner specializes in quantitative risk management software and services. We work directly for clients in the financial sector or as subcontractors to other software and consulting companies in the area.

Like any tool-set, quantitative risk measurement based on past data has limitations. These limitations are significant with many of the model implementations currently available. The financial markets today are much more vulnerable and interdependent compared to the last … Continue reading →]]> The problem

Like any tool-set, quantitative risk measurement based on past data has limitations. These limitations are significant with many of the model implementations currently available. The financial markets today are much more vulnerable and interdependent compared to the last century when the core modelling approaches applicable today were designed. In light of those limitations, it has become customary to apply stress testing as a supplement to quantitative risk measurement. Phrases like “our Value-at-Risk measure is supplemented with stress testing” is familiar from the risk management policy reports of multiple financial institutions. There is a perception that stress testing allows for the analysis of extreme events that the quantitative risk measurement model didn’t address. This perception is wrong.

Stress test is a re-valuation of the portfolio of the financial institution under an arbitrarily set scenario of extreme market events. Although stress testing can be used to assess losses under any scenario, it associates no probabilities with those scenarios. For example, if stress testing indicates that a financial institution will lose a billion dollars under the extreme scenario of a repetition of the Black Monday, it is difficult to make sense out of the result. Is that a scenario that will occur once every thousand days or once every thousand years? Without such quantification, stress testing is a placebo in terms of measuring extreme risk.

The choice and construction of stress test scenarios is arbitrary. Fundamentally stress testing as used today is a transformation of an arbitrarily chosen set of past data to the future. There is no reasonable way to assess how this past scenario will apply to the future; most likely it won’t.

The solution

The fundamental limitation of quantitative risk measurement based on past data stems from the past data. Past data might not necessarily contain a good-quality prediction for the future. Therefore, whatever statistical models are used, their predictions with respect to risk might turn out to be misleading. It is just because the future could turn out to be quite different from what has been observed in the past.

This limitation can be reasonably well addressed by stress testing. Limitations of the statistical model itself cannot.

Consider a company (e.g. a software provider, a retail chain, etc.) which is known to have very low rating among its employees due to poor management and low salaries. The past performance of the company could be impressive with high and predictable profits, steadily increasing, low volatility  stock price. However, nowadays in Europe (and, to a lesser degree, in China, Latin America, and many other regions) the population is getting older and older. Such a company faces hard consequences in the middle and long term, when it will not be able to recruit qualified staff.

The management of a financial institution does not necessarily have to be carbon-conscious to realize that excessive carbon exposure within the investment portfolio is a significant risk factor. At the relatively lenient or non-existing carbon regulation today, carbon-efficiency plays virtually no role in the performance of the assets under management. A stricter carbon regulation, on the other hand, could strongly impact the game for a number of industries from power generation to tourism.

These are just a few examples. Nowadays,  numerous social networks make it quite straightforward to obtain employee ratings. Every quarter, companies publish tons of disclosures on sustainability policies. For a careful reader or a good natural-language-processing software or simply by following industry XBRL standards it is possible to benchmark e.g. technology companies on their innovative power (yes, lack of innovation is the key risk driver of the 21st century). Numerous watchdogs on everything from environmental policy to corporate governance publish information on the web. Even Bloomberg has a sustainability news section. The emergence of shareholder platforms like Sharegate would make it even easier to quantify shareholder-friendliness of a company. A wide range of specialized sustainability rating agencies offer high-quality services and web-based information solutions to investors. This sort of information is fundamentally forward-looking and provides a basis for stress testing.

universalowner.com has developed a stress-testing methodology that addresses the true limitations of quantitative risk management. Instead of using stress tests as a placebo against out-dated statistical models, we analyze the sustainability risk factors behind the portfolio. This results in a forward-looking risk assessment.

Why is this relevant?

1. Obtain forward-looking risk assessments! The statistical models used to quantify risk based on past data should be sufficient to extract the maximum possible information out of the past data. They need to be supplemented by stress testing to address their fundamental limitation, namely reliance on past data. Stress testing should become a forward-looking risk assessment tool, not a placebo for wrong or out-dated statistical model assumptions.
2. Integrate environmental, social and governance factors in risk management! All financial institutions, not just the sustainability-conscious ones,  are interested to support efficiently the goals of sustainable growth and well-functioning of financial markets. For a mainstream institution, this is a difficult task since no clear economic mechanism guarantees any impact of sustainability factors on past performance and risk figures. The most natural way to integrate these goals is within the stress testing function of risk management.
3. Measure long-term risk! The financial success in the long-term is linked much more to the overall economic performance than to the individual performance of a given transaction, of a investment in an asset, in a fund, or in an industry. Environmental, social and governance factors have the potential to impact significantly multiple industries, regions and asset classes and therefore play an important role in the long-term success of a financial institution.