Market Models provides an authoritative and up–to–date treatment of the use of market data to develop models for financial analysis. Written by a leading figure in the field of financial data analysis, this book is the first of its kind to address the vital techniques required for model selection and development. Model developers are faced with many decisions, about the pricing, the data, the statistical methodology and the calibration and testing of the model prior to implementation. It is important to make the right choices and Carol Alexander s clear exposition provides valuable insights at every stage. In each of the 13 Chapters, Market Models presents real world illustrations to motivate theoretical developments. The accompanying CD contains spreadsheets with data and programs; this enables you to Market models: a guide to financial data analysis Market models: A guide to financial data analysis. Home > Research > Our research > STUDY WITH US ABOUT US NEWS & EVENTS INTERNATIONAL RESEARCH HOME implement and adapt many of the examples. The pricing of options using normal mixture density functions to model returns; the use of Monte Carlo simulation to calculate the VaR of an options portfolio; modifying the covariance VaR to allow for fat–tailed P&L distributions; the calculation of implied, EWMA and historic volatilities; GARCH volatility term structure forecasting; principal components analysis; and many more are all included. Carol Alexander brings many new insights to the pricing and hedging of options with her understanding of volatility and correlation, and the uncertainty which surrounds these key determinants of option portfolio risk. Modelling the market risk of portfolios is covered where the main focus is on a linear algebraic approach; the covariance matrix and principal component analysis are developed as key tools for the analysis of financial systems. The traditional time series econometric approach is also explained with coverage ranging from the application cointegration to long–short equity hedge funds, to high–frequency data prediction using neural networks and nearest neighbour algorithms. Throughout this text the emphasis is on understanding concepts and implementing solutions. It has been designed to be accessible to a very wide audience: the coverage is comprehensive and complete and the technical appendix makes the book largely self–contained. Market Models: A Guide to Financial Data Analysis is the ideal reference for all those involved in market risk measurement, quantitative trading and investment analysis. Item Type: Book Schools and Departments: School of Business, Management and Economics > Business and Management Subjects: H Social Sciences > HG Finance Depositing User: Carol Alexander Date Deposited: 27 Sep 2012 12:36 Last Modified: 27 Sep 2012 12:36 URI: http://sro.sussex.ac.uk/id/eprint/40646

Market Models: A Guide to Financial Data Analysis

A survey on FinTech

In-memory big data analytics under space constraints using dynamic programming

In the business world the accounting cycle is characterized by numerous interconnected processes [1]. However, despite the invention of new technologies useful in each step of the process, their integration still appears limited and their potential is still little exploited. Here, the authors link all the available technologies to every step of the financial accounting cycle, envisaging the use of a cloud platform on which to build a solid integrated system. Although integration is far from easy (it is no coincidence that it has not happened yet) this model takes into account all the practical needs of the stakeholders involved in the process: accountants, chartered accountants, auditors, governments, public and fiscal authorities, shareholders, creditors, data analysts. This novel approach can seriously offer a breakthrough to accounting and finance big data management [2][3]. This represents an effective method to solve various challenges such as tax evasion, tax avoidance, money laundering, accounting errors, high data storage costs, high audit risks, difficult and unreliable reconciliations, data collection and data analysis, lack of comparability between financial statement of companies based in different countries [4][5].

Integrated Cloud Financial Accounting Cycle: How Artificial Intelligence, Blockchain, and XBRL will Change the Accounting, Fiscal and Auditing Practices

Summary
The recent booming development of cloud computing has enabled a dramatical revolution for current enterprises to create values. Numerous benefits of utilizing cloud services are enhancing the efficiency of improving or creating new businesses. This trend is also associated with the rapid growth of the wireless networks, such as 5G. However, the great increase of deploying clouds also leads to new concerns in data security. One of the major concerns is private leakage that derives from insiders' malicious operations or attacks. This problem has raised a great restriction for financial firms to execute cloud applications. Focusing on this issue, we propose a new solution, entitled Privacy-Preserving Smart Storage (PS2) model that targets at solving the privacy leakage problems within the existing threat models. The proposed approach uses a novel distributed data storage method to prevent financial enterprises from insiders' massive data mining-based attacks.

Privacy‐preserving smart data storage for financial industry in cloud computing

In this paper, we present the QuantCloud infrastructure, designed for performing big data analytics in modern quantitative finance. Through analyzing market observations, quantitative finance (QF) utilizes mathematical models to search for subtle patterns and inefficiencies in financial markets to improve prospective profits. To discover profitable signals in anticipation of volatile trading patterns amid a global market, analytics are carried out on Exabyte-scale market metadata with a complex process in pursuit of a microsecond or even a nanosecond of data processing advantage. This objective motivates the development of innovative tools to address challenges for handling high volume, velocity, and variety investment instruments. Inspired by this need, we developed QuantCloud by employing large-scale SSD-backed datastore, various parallel processing algorithms, and portability in Cloud computing. QuantCloud bridges the gap between model computing techniques and financial data-driven research. The large volume of market data is structured in an SSD-backed datastore, and a daemon reacts to provide the Data-on-Demand services. Multiple client services process user requests in a parallel mode and query on-demand datasets from the datastore through Internet connections. We benchmark QuantCloud performance on a 40-core, 1TB-memory computer and a 5-TB SSD-backed datastore. We use NYSE TAQ data from the fourth quarter of 2014 as our market data. The results indicate data-access application latency as low as 3.6 nanoseconds per message, sustained throughput for parallel data processing as high as 74 million messages per second, and completion of 11 petabyte-level data analytics within 53 minutes. Our results demonstrate that the aggregated contributions of our infrastructure, parallel algorithms, and sophisticated implementations offer the algorithmic trading and financial engineering community new hope and numeric insights for their research and development.

Jessica J. Yu

Papers

QuantCloud: Big Data Infrastructure for Quantitative Finance on the Cloud