Enterprise Infrastructure for Consistent Pricing and Risk Analytics
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1 Enterprise Infrastructure for Consistent Pricing and Risk Analytics How technological innovations are intersecting with financial industry trends to bring about next generation analytics infrastructure Satyam Kancharla Senior Vice President, Numerix
2 Who are the key stakeholders for analytics? Inside Financial Institution: Quantitative How do I get my latest models into production quickly? Finance What s my economic capital? Traders What s the risk of my positions? Operations Where are my Marks? Outside Financial Institution: Risk Managers What s my global exposure to the market and counterparties? Regulators What s the systemic risk?
3 Enterprise Infrastructure Today Quants Traders Risk Mgrs Finance Operations Settleme Collateral Management nts Risk Middleware Market Market Data Data Ref. Data Ref. Data Trade Reposit Trade Reposit ory ory Interest Rates FX Credit Derivatives Equities Commo - dities Bonds
4 Something like this!
5 So what are the challenges? Highly fragmented data and quantitative models Lack of Standards Lack of consistency, transparency & flexibility Slow time to market due to complex interdependencies, changes are expensive Systems bloat & spiraling costs building new systems while maintaining old systems Too much operational risk no control on data or models Complex, expensive development needed for Risk Analytics Locally optimized but typically no organizational blueprint
6 Industry Trends
7 Regulations
8 Higher Regulation New regulations such as Dodd Frank, EMIR, Basel III & MiFID II may transform financial institutions into becoming highly regulated utilities. Increased supervision - extended fair value reporting, new expected loss model, more frequent disclosures Increased supervision - increased oversight of risk management capabilities/practices Higher capital requirements - new market-risk and securitization framework, counterparty credit risk (CCR) capital charges Higher regulatory capital charges will continue to pressure and contract markets and make some asset classes unprofitable (e.g. structured credit, structured products) May result in significant unintended consequences such as causing liquidity or working capital contractions or incentivizing firms to abandon otherwise beneficial hedging transactions
9 Impact of Regulation on ROE Source: McKinsey
10 Central Clearing / Trade Standardization Mandated central clearing of certain derivatives and reporting of some over- the-counter (OTC) trades to electronic trade repositories. New regulation is also shifting some products to trading through centralized trading facilities (SEFs) Standard pricing models (e.g. SNAC CDS) Standardized data, definitional and reporting requirements Tighter spreads, smaller margins, greater volume G20 Estimates 85% of all outstanding OTC derivatives will be Centrally Cleared $601 T Notional
11 Push for Standards Goal is standard data representation for major types of data: Industry-wide Standard Legal Entity ID Standardization of Trade, Lifecycle & Legal Agreements including CSAs Security/Trade Description via FPML / FIBO (Financial Industry Business Ontology) Push for algorithmic representation of complex products via payoff languages or similar CFTC, ISDA, EDM Council, OMG, etc actively involved in Standard setting
12 Higher Volumes & Real-Time Analytics Needs More trading on Exchanges and Exchange like venues and Standardization implies higher trade volumes High Frequency Trading results in huge amounts of data for processing and analysis Near real-time Limit Monitoring is now a must CVA (Credit Value Adjustment) requires real-time, portfolio level measures requiring significant computation capability as well as complex data management and aggregation Margin calculation is another complex portfolio level computation required at trading time Increasing buy-side sophistication means, buy-side clients expect advanced real-time pre-trade analytics as well as superior execution and post-trade services
13 Higher Flexibility / Transparency / Consistency Needs Uncertainty around economic climate and regulatory landscape requires Flexibility as well as Nimbleness of deployment Greater transparency needed both for internal business decisions and handling regulation Need for consistency across the organization in terms of modeling Ensures apples to apples aggregation Particularly important for Liability Driven Investments (LDI) in the ALM context Modeling standards still evolving: Curve construction Stress testing / Scenario Generation Correlation Modeling, etc Product mix is also evolving: Push for standardization compete with low interest rate environment to devise novel yield enhancement products Concerns around long-term inflation risk is driving innovation in Inflation and Commodity products
14 Technology Innovations
15 Cloud Computing Source: New Yorker
16 Cloud Computing Reduces operational costs drastically due to lower maintenance complexities and shared usage Good bye Fat Client, Good bye Workstation Transforms fixed costs into variable costs providing unprecedented flexibility to the IT organization Mature development paradigms combining Distributed Caches, Message Oriented Middleware (MOM), Service Oriented Architecture (SOA), etc are now available Reliable Multi-tenancy & Cloud management technologies for public / private / hybrid cloud exist today and are maturing rapidly Commoditization of compute clouds provides virtually unlimited resources without huge upfront investments Alas, we are all devices now!!
17 Technologies for handling Big Data Rich, scalable, big data solutions now available from leading vendors such as Microsoft Distributed data cache and MapReduce-like Frameworks now being widely deployed in the financial services sector Flexibility of on-premise or cloud deployment Real time data analysis support Highly sophisticated BI client tools for self service BI
18 Other Technology Innovations Semantic Data Processing More sophisticated Semantic layer now being added to data for rich, self describing interfaces Effort to use the Web Ontology Language (OWL), a knowledge representation language standard successfully used in other industries Semantic layers have enough information for downstream aggregation and even automatic UI generation Next Generation RAD Environments: High level domain specific (usually, scripting) languages combined with inbuilt ETL, quant modeling, and visualization capabilities Such frameworks bring together the best of both worlds: nimble execution in a RAD environment + sophisticated back-end with scalable, robust technology Ability to rapidly productionalize due to common standards and technology between RAD framework and enterprise back-end servers Is it time to put Excel back in its place?
19 Bringing Technology Innovations to Industry challenges
20 Putting it together Increased Regulation Standardization Central Clearing Volumes Real-time Needs Transparency / Flexibility Buy side sophistication Cloud/Centralized Computing MOM/SOA Frameworks Big Data Solutions Data Caches / Visualization Tools Semantic Data RAD Frameworks Consolidated, Centralized Data Shared, centralized Analytic Servers / Clouds Common Standards Ability to Absorb Massive Amounts of Information Quickly Reliable Messaging Standards Transparent, flexible calculation logic with capability to use enterprise back-end Lots of on-demand parallel processing power Little to no processing on the client On-demand, self-service BI tools capable of Providing Insight
21 Enterprise Infrastructure Tomorrow? Quants Traders Risk Mgrs Finance Operations Data Cache / BI Layer / Tools with Thin Clients/Apps Enterprise Service Bus Collateral Management Public / Private Cloud Market Data Ref. Data Standards Trade Reposit ory Interest Rates FX Credit Derivatives Equities Commo - dities Bonds
22 Conclusions Enterprise Analytics Infrastructure has to be rethought in the light of new needs and recent technologies We must embrace standards, starting with internal ones Risk & Pricing Models, being a source of derived data, need to be centrally managed and controlled just like data itself Centralizing data, quantitative models into the backend while preserving and in fact, enhancing RAD capabilities is now possible While this requires co-ordination and planning, the resulting infrastructure will be faster, cheaper and make the organization significantly more competitive
23 Thank You!
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