FX liquidity management and technology firm, Integral Development, has announced the upcoming launch of Integral FX Benchmark, a second by second pricing benchmark to cater to rising demand of TCA tools.
According to Integral, the firm has collaborated with Stanford University in the development of the methodology to calculate the second-by-second benchmark rates. Commenting about their participation in the product’s press release, Kay Giesecke, Associate Professor of Management Science and Engineering at Stanford University said, “The Integral FX Benchmark is a perfect example of how Stanford collaborates with the industry to conduct research that helps revolutionize the use of Big Data for the benefit of market participants. Access to accurate information is a key component of market efficiency that drives growth and innovation.”
In terms of the need for proper benchmark rates, also commenting in the release was Michael Melvin, Managing Director and Senior Research Advisor at BlackRock, who stated, “Even if one had a historical data set to allow model estimation, the challenge has been to have a proper continuous exchange rate series that supports trade evaluation on an ongoing basis.”
Big Data
Within the corporate world, ‘big data’ has become both an important trend and catchphrase. Used to term the ability to create efficient business decisions based on the reviewing of data, it has become increasingly more part of modern society thanks to improvements in technology that allow for lower costs to record and monitor information. As a result, firms have greater abilities to review which factors are most important for their businesses.
However, just having access to data isn’t always enough. One of the biggest knocks against big data is overkill. While too little information is always problematic, having too much can lead to difficulties in implementing changes and understanding what are the key data points. When reviewing FX execution, deviation from market rates is also a result of the order size of a trade and time of day. As such, in addition to just comparing benchmark rates, data filtering needs to be accomplished to understand which sample size of orders are best for analysis.
According to Integral, the firm has collaborated with Stanford University in the development of the methodology to calculate the second-by-second benchmark rates. Commenting about their participation in the product’s press release, Kay Giesecke, Associate Professor of Management Science and Engineering at Stanford University said, “The Integral FX Benchmark is a perfect example of how Stanford collaborates with the industry to conduct research that helps revolutionize the use of Big Data for the benefit of market participants. Access to accurate information is a key component of market efficiency that drives growth and innovation.”
In terms of the need for proper benchmark rates, also commenting in the release was Michael Melvin, Managing Director and Senior Research Advisor at BlackRock, who stated, “Even if one had a historical data set to allow model estimation, the challenge has been to have a proper continuous exchange rate series that supports trade evaluation on an ongoing basis.”
Big Data
Within the corporate world, ‘big data’ has become both an important trend and catchphrase. Used to term the ability to create efficient business decisions based on the reviewing of data, it has become increasingly more part of modern society thanks to improvements in technology that allow for lower costs to record and monitor information. As a result, firms have greater abilities to review which factors are most important for their businesses.
However, just having access to data isn’t always enough. One of the biggest knocks against big data is overkill. While too little information is always problematic, having too much can lead to difficulties in implementing changes and understanding what are the key data points. When reviewing FX execution, deviation from market rates is also a result of the order size of a trade and time of day. As such, in addition to just comparing benchmark rates, data filtering needs to be accomplished to understand which sample size of orders are best for analysis.
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