Fixed Income Quantitative Analysis


GENESIS: Comprehensive Fixed-Income Analysis & Portfolio Management Suite

GENESIS is the GFA analytical platform that supports the quantitative analysis central to our activities. There are 5 major modules which can be accessed separately or in a unified system. All of the GFA consulting services draw upon this same environment. A distinguishing feature of GENESIS is its broad coverage which includes traditional fixed income, municipal bonds, and a wide range of derivative products. An integrated database supports all of the modules including extensive input data for the various analyses.

BONDMAX: providing multiple structured optimization strategies for your portfolio

Portfolio optimization can be thought of as a method for quantifying the optimal integration of risk and return expectations with desired portfolio objectives and policy. BondMax provides users with an easy to use analytic platform with which they can perform the highest quality fixed income portfolio optimization strategies.



Immunization: Selecting assets such that the existing business is immune to a general change in the rate of interest. Implementation of this strategy allows you to lock in a series of fixed rate of returns over a pre-specified horizon.

Functional Duration Matching: Maximizing the expected return of your portfolio while matching the functional duration of your portfolio and your liability stream as closely as possible.

Liability Matching: Constructing a lowest cost portfolio with which to pay out the liability streams.

Index Tracking: Replicating the characteristics of an index with a portfolio while maximizing the expected return of the portfolio.

Swapping: Determining the cheapest to buy or most expensive to sell alternative for a set of fixed income securities with the same interest rate risk.

Custom Strategies

Additionally, users can create a customized optimization strategy using the customized option in BondMax. For example, given a benchmark target, and a number of interest rate scenarios, the user can set up an objective function to maximize the expected return of the portfolio subject to conditions that:

    1.The Portfolio Functional Duration is equal the Benchmark Functional Duration; and

    2.The Portfolio Return is better than the benchmark return under the worst case scenario.