Category Archives: Insights
If a firm does not have all of its positions modeled on the same risk platform, it cannot know its total risk. But strong tools and practices can help sell-side and buy-side firms alike to avoid creating dangerous conditions.
Broker-dealers understand the importance of producing accurate margin calculations for their clients’ accounts, but they need to know more than just how much margin each client must post; they need to know why the margin system came up with a given result.
In times of stress in the markets, not only does volatility increase for individual assets, cross-asset correlations can increase dramatically as well. This results in a “double whammy” for a typical portfolio because the portfolio’s volatility increases due to both effects.
Those responsible for maintaining a margin system often feel that they are drowning in data management issues. In part two of this series we discuss ways to make margin calculations far more efficient and meet the firm’s need for answers in real-time.
Computing, optimizing and monitoring real-time margin requirements across a multitude of instruments and customer accounts is a Herculean task. In this article, Imagine discusses the key challenges in this arena and will dive deeper into the specifics of solving its challenges.
Steven Harrison, President of Imagine Software, and Bruce Zulu, Director of Technical Support Services for the Business Intelligence division at Panopticon’s parent company, Altair, explain the collaboration and how it benefits clients.
Most market observers agree that the many uncertainties swirling around the US Presidential election are likely to generate volatility in the US equity market that could last well beyond November 3rd.
At first glance, constructing a volatility surface looks like a straightforward exercise – a closer look reveals there is a great deal more to consider.
When newcomers to the field of quantitative finance are assigned the task of writing up an analysis, they will often show numbers using five, six or even more digits to the right of the decimal point. This may be driven by the part of the brain that craves precision and exactness. Given the unstable nature of financial data, does that mean it is a fool’s errand to try to estimate risk?