Assessing Quality of Various Market Data Sources for Risk Management Purposes

Student: Ksenofontova Ekaterina

Supervisor: Victor A Lapshin

Educational Programme: Financial Markets and Financial Institutions (Master)

Final Grade: 7

Year of Graduation: 2017

The importance of data quality, and hence an emphasis on filtering bad data, has risen in recent years. Financial market data represent the basic blocks for many types if financial analysis, including risk-management. There are a number of causes that lead to a loss of data quality. These include duplicate records, missing data, incorrect data, and even errors created during data entry. This study investigates various filtering techniques that can be used for cleaning financial data. It also examines the influence of data quality on the market risk exposure calculations. The key findings of the research indicate that the moving window filtering algorithm has yielded satisfactory results in most cases of detecting outliers and contributes to the precision of quantitative risk assessment models.

Full text (added May 15, 2017)

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