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Derivatives
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Here is where we really shine, and where the open source approach of stdfin really pays off.
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Simple plain vanilla interest rate derivatives, caps and floors and swaps, even European exercise swaptions have very straightforward and standardized methods to them. These typically have a bid-offer spread of a fraction of a basis point in any given market. *IBOR curves can be built with any of the usual splicing and interpolation options particular to your opportunity costs as a financial intermediary.
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Futures themselves have the simplest market risk profile, yet there are a few things about them. Delivery for non cash-settled contracts such as US T-Bonds, cheapest to deliver and related yield analytics, and the constant innovation in the futures space, have nontrivial value.
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Forex, which conflates bilateral interbank environments with the capital flows and liquidity picture itself, is an area where risk analytics are useful, at least in a strategic and decision making role. As far as reporting and assurability, that's your business not ours.
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Index swaps, amortizing notional swaps, and similar specific product-based derivative structures are frighteningly straight forward to value as-is. Plug in your index or other assumptions, however sound or asinine, and you get a value. And who would ever know until it matures? However, adding stdsim to directly model index contingencies in a Monte Carlo-like context can add enormous value to your understanding of market risk.
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Options, on individual equities, futures contracts, indexes of various kinds, are all straight forward. However, adding stdsim to directly model those underlying objects in a contingent Monte Carlo-like context can add enormous value to your understanding of market risk.
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Capital markets options, such as American and Bermudan exercise swaptions, resettable caps, swaps based on underlying objects with convexity such as CMS and CMT swaps, swaps with cancellation options, other complex "non-Markovian" instruments, or your own proprietary structures, all require a scenario driven approach anyway. Adding stdsim to directly model index contingencies in a Monte Carlo-like context can add enormous value to your understanding of market risk.
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The stdfin product supports most commonly used non-proprietary closed-form option market environment models, such as Black-Scholes, Black-Derman-Toy, Ho-Lee, Heath-Jarrow-Morton, and so on. The stdsim product enhances the Monte Carlo approach, allowing arbitrary specification for *ARCH and other volatility term structure models to be used. We do diffusion, trees, lattices, and sampling.
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