New Delivery Models for Options Analytics
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Overview: This whitepaper discusses and proposes a solution to the Big Data challenge faced by industry participants in the US equities options markets. Managing options trading books, running strategies and discovering alpha all require large and increasing amounts of market and historical data, reference data and analytic computations. Analytics are especially key to options trading as participants need to transform raw data into implied volatilities in order to perceive value, and sensitivity measures (i.e. "the Greeks") in order to measure risk and construct hedges. Analytic results become the "exposures" needed for portfolio analytics, risk management and portfolio margining calculations.
Even for sophisticated firms with significant investment in ultra-low latency (ULL) capabilities, the most expensive infrastructure is typically set up with capacity to monitor the average level of position inventory to manage risk, and to operate on a limited dynamic symbol set (identified through historical analysis and back-testing) to generate alpha. Excess capacity of ULL technology can eat into profitability, and is avoided. However, this can leave potential growth opportunities on the table that could be met in an economic manner by additional low-latency analytic data and remotely.