Credit Suisse Group is a Switzerland-based multinational financial services holding company, headquartered in Zürich, that operates the Credit Suisse Bank and other financial services investments. The company is organized as a stock corporation with four divisions: Investment Banking, Private Banking, Asset Management, and a Shared Services Group that provides marketing and support to the other three divisions.Credit Suisse was founded by Alfred Escher in 1856 under the name Schweizerische Kreditanstalt in order to fund the development of Switzerland's rail system. It issued loans that helped create Switzerland's electrical grid and the European rail system. It also helped develop the country's currency system and funded entrepreneurship. In the 1900s Credit Suisse began shifting to retail banking in response to the elevation of the middle-class and the growing popularity of savings accounts. Credit Suisse partnered with First Boston in 1978. After a large failed loan put First Boston under financial stress, Credit Suisse bought a controlling share of the bank in 1988. In the 1990s, Credit Suisse acquired the Winterthur Group, Swiss Volksbank, Swiss American Securities Inc. and Bank Leu among others. In the year 2000, it added the U.S. investment firm Donaldson, Lufkin & Jenrette.The company restructured itself in 2002, 2004 and 2006. It was one of the least affected banks during the global financial crisis, but afterwards began shrinking its investment business, executing layoffs and cutting costs. During the period between 2008 and 2012, Germany, Brazil, and the United States began a series of investigations into the use of Credit Suisse accounts for tax evasion. In May 2014, the company pleaded guilty to decades of conspiring to help US citizens "hide their wealth" in order to avoid taxes, and agreed to pay $2.6 billion in fines.In 2014, Credit Suisse had 888.2 USD Bn of assets under management according to the Scorpio Partnership . Wikipedia.
Credit Suisse | Date: 2016-12-28
A processor-implemented method for executing a synthetic cross-currency basis swap, including determining a notional value for the swap, the notional value being denominated in a first currency; calculating a synthetic spread based on a spot rate between the first currency and a second currency, a first reference rate for the first currency, and a second reference rate for the second currency; calculating a first payment to be paid by a first party to the swap by multiplying the notional value by a predetermined number of basis points; calculating a second payment to be paid by a second party to the swap by multiplying the notional value by the synthetic spread; and facilitating an exchange of payment between the first party and the second party.
News Article | September 15, 2017
"Brent crude oil is used as a benchmark to price roughly two-thirds of the crude oil supply traded in the world, but investors seeking exposure to Brent crude oil through an exchange-traded security have very limited options," said Paul Somma, head of Exchange Traded Notes at Credit Suisse. "These ETNs provide a way for sophisticated investors to implement innovative short-term trading and hedging strategies based on the ETNs' leveraged exposure to Brent crude oil futures contracts." About the ETNs and the Index The ETNs seek to provide a 3x leveraged long or 3x leveraged inverse return, as applicable, based on the daily performance of the S&P GSCI® Brent Crude ER (the "Index") (as adjusted for fees). The ETNs do not attempt to, and should not be expected to, provide returns that reflect 3-to-1 positive or inverse leverage on the return of the Index for periods longer than a single day. The ETNs rebalance their theoretical exposure on a daily basis, increasing exposure in response to that day's gains or reducing exposure in response to that day's losses. The ETNs are intended to provide traders and similarly sophisticated investors with an exchange traded security that allows them to express their short-term market views on the performance of Brent crude oil futures contracts composing the Index. The Index tracks a hypothetical position in the nearest-to-expiration ICE Futures Europe Brent crude oil futures contract, where that position is rolled each month into the futures contract expiring in the next month. The Index is calculated by S&P Dow Jones Indices LLC and its levels are disseminated real-time under the Bloomberg ticker symbol "SPGSBRP" and the Reuters instrument code ".SPGSBRP". More information on the ETNs can be found on: The ETNs are not intended to be "buy and hold" investments. Instead, the ETNs are intended to be daily trading tools for traders and similarly sophisticated investors to express short-term market views and manage daily trading risks. The ETNs are designed to achieve their stated investment objectives on a daily basis, and, therefore, their performance over different periods of time can differ significantly from their stated daily objectives. The ETNs are considerably riskier than securities that have intermediate or long-term investment objectives, and may not be suitable for investors who plan to hold them for a period of more than one day. Investors should actively and frequently monitor their investments in the ETNs, on a daily or intraday basis, and any decision to hold the ETNs for more than one day should be made with great care and only as the result of a series of daily (or more frequent) investment decisions to remain invested in the ETNs for the next one-day period. If you hold the ETNs for more than one day, it is possible that you will suffer significant losses in the ETNs even if the performance of the Index over the time you hold the ETNs is positive, in the case of UBRT, or negative, in the case of DBRT. Accordingly, the ETNs should be purchased only by sophisticated investors who understand the Index (as defined below) and the consequences of investing in the ETNs that are designed to provide exposure to three times (3x) or negative three times (-3x), as applicable, the daily performance of the Index. The ETNs do not provide direct exposure to the spot price of Brent crude oil. Because the Index is composed of Brent crude oil futures contracts and does not track the spot price of Brent crude oil, the Index and, in turn, the ETNs can be expected to perform very differently from such spot price. Investing in the ETNs involves significant risks. See the section entitled "Risk Factors" beginning on PS-7 of the Pricing Supplement. Credit Suisse AG Credit Suisse AG is one of the world's leading financial services providers and is part of the Credit Suisse group of companies (referred to here as 'Credit Suisse'). As an integrated bank, Credit Suisse offers clients its combined expertise in the areas of private banking, investment banking and asset management. Credit Suisse provides advisory services, comprehensive solutions and innovative products to companies, institutional clients and high-net-worth private clients globally, as well as to retail clients in Switzerland. Credit Suisse is headquartered in Zurich and operates in over 50 countries worldwide. The group employs approximately 46,230 people. The registered shares (CSGN) of Credit Suisse's parent company, Credit Suisse Group AG, are listed in Switzerland and, in the form of American Depositary Shares (CS), in New York. Further information about Credit Suisse can be found at www.credit-suisse.com. Credit Suisse has filed a registration statement (including prospectus supplement and prospectus) with the Securities and Exchange Commission (the "SEC"), for the offering of securities. Before you invest, you should read the applicable Pricing Supplement, the Prospectus Supplement dated June 30, 2017 and Prospectus dated June 30, 2017, to understand fully the terms of the ETNs and other considerations that are important in making a decision about investing in the ETNs. You may get these documents without cost by visiting EDGAR on the SEC website at . Alternatively, Credit Suisse, Credit Suisse Securities (USA) LLC, or any agent or dealer participating in an offering will arrange to send you the applicable Pricing Supplement, Prospectus Supplement and Prospectus if you so request by calling +1 800 320 1225. The ETNs' Pricing Supplement, including the Prospectus dated June 30, 2017 and Prospectus Supplement dated June 30, 2017: You may access the Prospectus Supplement and Prospectus on the SEC website at or by clicking on the hyperlinks to each of the respective documents incorporated by reference in the Pricing Supplement.
Bonato M.,Credit Suisse
Finance Research Letters | Year: 2011
This paper studies the behavior of the conventional measures of skewness and kurtosis when the data generator process is a distribution which does not possess variance or third or fourth moment and assesses the robustness of the alternative measures for these particular cases. It is first shown that for symmetric fat-tailed distribution skewness is far from being a valid indicator of the presence of asymmetry. Secondly, a Monte Carlo simulation is performed to investigate the behavior of the alternative measures of skewness and kurtosis when applied to distributions which do not possess finite higher moments. Finally, an application to the series of daily returns on a large cap US stock is presented to explain why alternative measures are a better tool to describe the distribution of financial returns. © 2010 Elsevier Inc.
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2013.3.4 | Award Amount: 4.68M | Year: 2013
NanoStreams co-designs a micro-server architecture and software stack that address the unique challenges of hybrid transactional-analytical workloads, which are encountered by emerging applications of real-time big-data analytics. To this end, NanoStreams brings together embedded system design principles, application-specific compilers, and HPC software practices.The processor technology that underpins the NanoStreams micro-server is an amalgam of RISC cores and nano-cores, a new class of programmable custom accelerators. Novel automatic compiler generation and parameterization technology enables low-effort programming and integration of nano-cores into application-specific, many-core accelerators. The proposed heterogeneous Analytics-on-Chip processor forms the backbone of the NanoStreams micro-server, which further leverages a hybrid DRAM-PCRAM memory system and a non-cache-coherent scale-out architecture to achieve extreme energy-efficiency.The software stack of the NanoStreams micro-server is rooted in domain-specific languages for analytical queries, which the project implements with a streaming dataflow execution model. The language runtime system uses real-time scheduling, performance isolation techniques and region-based memory management to minimize latency on the transactional path and maximize throughput on the analytical path. NanoStreams virtualizes lightweight PCRAM-based persistent memory, for direct user access and locality optimization.The project will deliver a real-silicon prototype, based on the Xilinx Zynq platform and ARM-Linux. The quantitative objective of NanoStreams, in comparison with contemporary HPC servers, is to reduce analytical response time of commercial in-memory databases by at least 30%, while sustaining transactional throughput and improving system energy-efficiency and programmability. NanoStreams will demonstrate these advances with industry-standard workloads and four real-world case studies.
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2013.6.2 | Award Amount: 4.31M | Year: 2013
The GreenDataNet project aims at designing and validating a new, system-level optimisation solution allowing urban data centres to radically improve their energy and environmental performance. The objective is to develop a set of beyond state-of-the-art technologies that will allow urban data centres to reach 80% of renewable power and decrease their average Power Usage Effectiveness (PUE) from 1.6-2.0 today to less than 1.3. GreenDataNet will enable energy monitoring and optimisation of IT, power, cooling and storage at three levels: servers and racks, individual data centres, and networks of data centres. To further reduce the need for grid power, GreenDataNet will also work on the integration of local photovoltaic energy in combination with an innovative, large-scale storage solution that will facilitate the connection of data centres to smart grids. Within this project, second-life electric vehicle Li-ion batteries will be investigated as a more advantageous solution for data centres to become actual smart grid nodes.The whole solution will be implemented as an open-source platform to allow third parties to provide additional optimisation modules and ensure the long-term sustainability of the project. Three demonstration sites will be utilised to test and validate the GreenDataNet concept: a data centre from Credit Suisse in Switzerland, a data centre from CEA in France that includes a large photovoltaic area and a smart grid test platform, and a pilot site in the Netherlands that is being used by a Dutch consortium working on Green IT technologies. In addition, research on heat reuse vs. free cooling will be conducted in a new data centre built by ICTroom in Belgium. Performance indicators that go beyond PUE will be experimented in the project and will support the work of the consortium in standardisation bodies like CEN/CENELEC/ETSI. Based on the project outcome, GreenDataNet will release guidelines to help make data centres more sustainable in the future.
Credit Suisse | Date: 2014-11-24
A development and testing environment with reduced database storage requirements that uses synthetic data based on anonymized real data, which allows the use of sensitive data for testing while protecting such data as required by privacy laws, secrecy laws and company policies.
Credit Suisse | Date: 2016-01-06
The enterprise database system provides methods, data, and user interfaces for performing reassessments and creating financial and accounting disclosure reports. Data fields for entities are monitored for changes that are evident at the end of reporting periods and may trigger the need to reassess the categorization of the entity. The system receives a request to perform a reassessment based on changes to particular data fields during the reporting period. The system retrieves entities that require reassessment based on the trigger events applicable to the entities. A reassessment is performed for each of the entities having a trigger event and the reassessment is stored in a historical database. Based on the reassessment, the system generates prompts to re-categorize the reassessed entity. Following the reassessment and categorization, the system can generate a disclosure report that presents information about the newly categorized entity.
Credit Suisse | Date: 2014-10-17
A method for costing a web service operable for querying one or more data sources, the method including calculating the cost of operating the web service, measuring web service usage data using one or more service metrics, wherein the one or more service metrics include at least one derived service metric, choosing at least one service metric from the one or more service metrics, wherein one or more of the at least one chosen service metrics is a derived service metric, calculating the cost of the web service based on the at least one chosen service metric, and charging for usage of the web service based upon the calculated cost of the web service.
Credit Suisse | Date: 2013-12-31
A system for expense calculation, accrual, allocation, and reconciliation, including an expense calculation module configured to receive transaction data relating to a transaction and to apply at least one charge rule to the transaction data to calculate expense data detailing the expenses expected to be charged in association with the transaction; an accounting control module configured to receive the expense data as an input and to apply at least one accounting rule to the expense data to create enhanced data relating to the transaction; and an invoice reconciliation module configured to receive as inputs the expense data as well as invoice data related to the transaction, and to determine whether the invoice data matches the expense data for the transaction.
Credit Suisse | Date: 2012-09-26
Method of failure detection in an operating system, the method comprising sending a request from a client to a server through a router, storing a token at the router, carrying out the request at the server, and returning a reply from the server to the client through the router, wherein the method comprises the alternative steps of, in response to a failure in the server, generating at the router an error reply based on the token and returning the error reply from the router to the client.