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As we return from summer holidays, it is the same as the many global insurers make their (hopefully) final preparations to comply with a significantly expanded Solvency II regime taking effect January 2016. There are a number of areas included for this deadline, and those of particular importance include the need for expanded risk reporting with greater accuracy and robust modeling in pricing

As a technology provider to financial firms, how often have you felt “nearly there” on client requirements, or given up on RFP’s where you would have had a good shot if only you had been able to offer that additional bit of functionality? How many times have you pondered over investing money that really isn’t in your budget in order to win critical new business or keep an important client? I think

In the past several years, leading quant teams have been utilizing Algorithmic Differentiation (AD) to accelerate their valuation and risk management calculations. While AD has only been applied in financial markets recently, the methodology is several decades old. For many years, this technique has been successfully applied to a variety of fields including Oceanography, Physics, Geology

Lately I’ve noticed some inaccurate ideas regarding Algorithmic Differentiation (AD) floating around in the press. And so, I feel obliged to set the record straight. For those unfamiliar with AD, it is a mathematical technique that helps firms with derivatives on their books rapidly solve complex pricing and analytics problems. In fact, firms utilizing AD typically experience staggering

On July 7, FINCAD, Ascent and PRMIA held a workshop in Hong Kong at the Central Plaza that explored key considerations for trading, pricing, and risk managing target accrual redemption forwards (TARFs). TARFs are becoming an increasingly popular structured product because they allow investors to hedge their currency exposure at a relatively low cost. As part of the Hong Kong-based event, Tony Webb