Unicredit银行奥地利开发并迅速部署了一致的企业范围内市场数据引擎
挑战
解决方案
结果
- 开发时间减少了50%
- Risk management improved
- Operational, audit, and maintenance costs reduced
“Many financial institutions are struggling to adapt their models to the volatility and limited availability of credit in today’s markets. Using MathWorks products, we can develop and deploy models in response to new market conditions in days or weeks, instead of months.”
Peter W. Schweighofer, UniCredit Bank Austria
为了有效地管理动荡的全球市场风险，金融机构必须迅速调整其内部财务模型。如果没有一致的市场和所有资产类别的静态数据存储库，以及用于计算派生和合成市场数据的流线流程的过程，进行这些调整是不可能的。
UniCredit Bank Austria AG used MathWorks tools to develop its market data calculation engine, which computes the near-time and end-of-day derived market data required formarket risk和绩效管理。matlab®based engine is integrated in the bank’s Unified Market Data (UMD) data warehouse and is accessible via the bank’s existing J2EE Web architecture.
“Knowledge of the prevailing market conditions, the models, and the algorithms resides in the business division,” explains Peter W. Schweighofer, senior market risk manager at UniCredit Bank Austria. “With MathWorks tools, the risk managers can develop algorithms and financial models, and the IT division can quickly deploy the applications. Because we can implement changes in our models and get them into production quickly, we can rapidly respond to new market data and conditions.”
挑战
银行内的业务部门存储了相同市场数据的不同版本，并使用不同的系统计算出派生数据。这种方法增加了维护开销，数据和处理算法的不一致性导致了高度操作风险。“We needed enterprise-wide data management to ensure consistent results and sound consolidated financial statements group-wide,” says Schweighofer.
该银行还需要构建用于清洁和计算派生数据的处理引擎。他们想将这些数据访问给与市场，运营和对手风险管理有关的群体；资产责任管理；市场合规检查；和资本充足性。最后，为了使整个欧洲的子公司获得访问权限，奥地利Unicredit银行希望开发一个访问市场数据引擎的J2EE框架。
解决方案
UniCredit Bank Austria used MATLAB and MATLAB Compiler SDK™ to develop UMD and integrate it into the bank’s Web infrastructure. They chose these tools because they wanted to build a proprietary system, were familiar with the risk profile and the revaluation functions of the traded products, and used MathWorks software elsewhere in the bank.
关键的第一步是清洁实时滴答数据，每天从各个市场数据供应商那里收到的20,000多种金融工具的记录到1亿记录。使用MATLAB，业务团队开发了算法，这些算法可以识别异常和缺少数据，并可以通过插值或使用最后已知的好数据来自动纠正它们。
他们还开发了用于计算日期和日末派生的市场数据的算法，包括公司和财政部信用差异曲线，波动性表面，通货膨胀掉期和零企业产量曲线。
使用“优化工具箱”™，团队通过最大程度地减少模型预测的结果与市场上观察到的实际结果之间的误差函数来校准模型。
The team used Financial Instruments Toolbox™ to calculate bond yields and Financial Toolbox™ to perform forward rate and short-term interest rate future strip calculations.
结果对历史市场数据和现有算法进行了反测试和验证。
Once the business team verified the algorithms, the IT team used MATLAB Compiler SDK to create MATLAB based Java®classes from the MATLAB programs. They then deployed these classes to an application server, and the integrated system was tested for enterprise deployment.
UMD is currently accessed by hundreds of financial managers and traders throughout the bank for market conformity, risk analysis, reporting, and trading.
结果
开发时间减少了50%。“With MATLAB, we can focus on business logic instead of implementation details,” says Schweighofer. “We can deploy an algorithm in a Java environment the same day, without any additional coding. This approach enabled us to cut our development time in half, if not more.”
Risk management improved。Schweighofer说：“ UMD现在是与奥地利Unicredit银行的所有交易活动有关的交易活动的后端。”“这是一种可扩展的数据引擎，我们可以迅速适应市场条件下的波动。”集成系统加强了企业范围内的数据一致性和可靠性，这是本地和国际法规的要求。
Operational, audit, and maintenance costs reduced。Schweighofer说：“通过消除冗余系统并提高数据质量和可追溯性，我们提高了审计合规性，这进一步降低了成本。”“每天在手动数据管理任务上花费的时间从几个小时减少到不到30分钟，使我们的员工能够专注于更具战略性的活动。”
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