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The first application to option pricing was by Phelim Boyle in 1977 (for European options). In 1996, M. Broadie and P. Glasserman showed how to price Asian options by Monte Carlo. An important development was the introduction in 1996 by Carriere of Monte Carlo methods for options with early exercise features.
Other common pricing-methods are simulation and PDEs. Option-adjusted spread (OAS) is the yield spread which has to be added to a benchmark yield curve to discount a security's payments to match its market price, using a dynamic pricing model that accounts for embedded options. OAS is hence model-dependent.
Microsoft SQL Server Analysis Services (SSAS [1]) is an online analytical processing (OLAP) and data mining tool in Microsoft SQL Server. SSAS is used as a tool by organizations to analyze and make sense of information possibly spread out across multiple databases, or in disparate tables or files.
Note that whereas equity options are more commonly valued using other pricing models such as lattice based models, for path dependent exotic derivatives – such as Asian options – simulation is the valuation method most commonly employed; see Monte Carlo methods for option pricing for discussion as to further – and more complex – option ...
See Asset pricing for a listing of the various models here. As regards (2), the implementation, the most common approaches are: Closed form, analytic models: the most basic of these are the Black–Scholes formula and the Black model. Lattice models (Trees): Binomial options pricing model; Trinomial tree; Monte Carlo methods for option pricing
Regarding cloud resources, Microsoft Azure offers two deployment models: the "classic" model and the Azure Resource Manager. [75] In the classic model, each resource, like a virtual machine or SQL database, had to be managed separately, but in 2014, [ 75 ] Azure introduced the Azure Resource Manager, which allows users to group related services.