Solution Spotlight

Solution Spotlight

XXXX ALM and LDI Portfolio Construction

Shape Portfolios to Accurately Align Risk Tolerances and Investment Views

XXXX’s Galapagos Portfolio Construction for ALM/LDI provides a robust method for strategic asset allocation using genetic algorithm based optimization technology with applicability to ALM, LDI, and Total Return (alpha generating) businesses. XXXX can assist internal and 3rd party asset managers and investment advisors in determining optimal portfolio allocation by enabling more sophisticated consideration of investors’ risk budgeting.

XXXX Portfolio Construction Solution

XXXX provides customers with genetic algorithm based optimization operating on:

 Proprietary risk or economic capital model

 Third party risk or economic capital model

Galapagos can operate on Monte Carlo risk or economic capital models where traditional optimization methods cannot.

Galapagos Portfolio Construction is able to:

 Allocate assets strategically while considering risk budgets

 Handle a wide range of objective functions and constraints

 Create optimal portfolios based on risk views and tolerances

 Represent a liability benchmark as a series of cashflows or a replicating portfolio

 Generate a stochastic long term forecast of portfolio market values and cashflows

 Produce detailed output and charts to compare different portfolio allocations

Why Galapagos Portfolio Construction

Forward looking investment professionals are adopting a more robust approach to strategic asset allocation and portfolio construction. These investment professionals are able to optimize on a Monte Carlo based stochastic risk model using XXXX’ genetic algorithms. Traditional ptimizing techniques cannot operate on these risk models. As a result, portfolio allocations and trade recommendations based on traditional techniques can be inaccurate. There is no known competing optimization method that matches the flexibility of Galapagos in handling highly non-linear problems with complex constraints.

In Summary

 Modeling a fixed income portfolio requires consideration for path dependency, cashflows, convexity, and non-linearity

 Traditional linear, quadratic, and mathematical optimizers cannot operate accurately on sophisticated fixed income risk models

 Limited resources make implementing a sophisticated optimization and risk model challenging

Possible objectives

 Maximize P&L while constraining risk

 Minimize expected shortfall while constraining risk

 Minimize duration mismatch while constraining risk and P&L

 Match a liability benchmark while handling multiple constraints

Business Applications

 ALM (Asset Liability Management)

 LDI (Liability Driven Investment)

 Total Return Funds (Alpha Generating Funds)

Functions

 Matches liability and asset cashflows

 Supports complex non-linear risk models

 Solves diverse portfolio problems

 Suggests asset allocations that fit all constraints and have the most return/risk impact

 Includes a grid computing platform allowing a large number of scenario generation and analysis

 Provides data for reporting, analysis, and communicating risk/return trade-offs

 

Benefits

 Leverages fundamental analysis

 Creates and validates trading ideas

 Suggests optimal allocation strategies

 Allows for flexible risk budgeting

Impact

 Increases credibility and sophistication

 Differentiates against traditional approaches

 Improves portfolio performance

 

The document about Solution Spotlight was translated by Huayiwang Mandarin Translation Company.

 

解决方案重点介绍

XXXX资产负债管理（ALM）及负债驱动投资（LDI）的组合建构

订立投资组合来准确匹配风险容忍度与投资观念

 

XXXX科技公司（XXXX）针对ALM和LDI而提出的“XXXX投资组合建构”采用了基于遗传算法的最优化技术，为战略性资产配置提供了过硬的方法，不仅能够应用于ALM、LDI业务，同时也适用于完全回报（亦即创造超额利润）的业务。XXXX使内部的和第三方的资产管理者以及投资顾问能够对投资人的风险预算进行更周密精确地考虑，从而为他们判定最优的组合配置而提供帮助。

 

“XXXX投资组合建构”解决方案

XXXX就以下方面而为用户提供基于遗传算法的优化运作：

         自营风险或经济资本模型

         第三方风险或经济资本模型

XXXX能够在常规最优法所不能运行的蒙特卡罗风险模型或经济资本模型上运行。

“XXXX投资组合建构”能够改善风险与回报权衡过程中的互通性

“XXXX投资组合建构”能够处理以下事项：

         在考虑风险预算的同时实现战略性的资产配置；

         处理多种目标函数与约束；

         基于风险观点和风险容忍度来创建最优的投资组合；

         将负债基准表现为一系列现金流或是复制组合；

         为投资组合市场价值与现金流创建随机的长期预测；

         能够给出详尽的输出和图表，以比较不同的投资组合配置情况。

 

为何选择“XXXX投资组合建构”

具有远见卓识的投资专业人员都在采用一种更加过硬的方法来进行战略性资产配置和投资组合建构。这些专业人员采用XXXX的遗传算法，因而能够在基于蒙特卡罗法的随机风险模型上进行最优化。常规的最优法技术是无法在这些模型上运行，因此，以这些常规技术作为基础来提出的投资组合配置和交易建议就可能是错误的。在处理具有复杂约束的高非线性问题时，当前已知的最优法无一能够达到XXXX的灵活性。

小结

         固定收入投资组合建模需要考虑路径依赖性、现金流、凸性及非线性；

         常规的二次线性数学最优法不能在复杂的固定收入风险模型上运行；

         有限的资源使得实施精密的最优化和风险模型成为具有挑战性的事务。

 

可行目标

         在约束风险的同时实现盈亏最大化；

         在约束风险的同时将预期短缺最小化；

         在约束风险与盈亏的同时将久期错配最小化；

         在掌控多重约束的同时匹配负债基准。

 

使您的战略性资产配置过程更加智能化

 

适用业务

         ALM（资产负债管理）；

         LDI（负债驱动投资）；

         完全回报基金（超额盈利基金）。

 

功能

         匹配负债与资产现金流；

         支持复杂的非线性风险模型；

         解决分散投资组合问题；

         提示适合所有约束、并具有最大回报/风险影响的资产配置法；

         纳入一种网格计算平台，它允许创建并分析大量的设想情境；

         为报告、分析和风险/回报权衡过程的互通提供数据。

 

优点

         协助基础分析；

         创建并验证交易创意；

         提示最优配置策略；

         允许灵活的风险预算。

 

影响

         增加可信度和精密性；

         与常规方法区分开来；

         提高投资组合绩效。

 

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