Duration models and value at risk using high-frequency data for the peruvian stock market

Abstract

Most empirical studies in nance use data on a daily basis which is obtained by retaining the last observation of the day and ignoring all intraday records. However, as a result of the increased automatization of nancial markets and the evolution of computational trading systems, intraday data bases that record every transaction along with their characteristics have been stablished. These data sets prompted the development of a new area of research ( nance with high frequency data), and in 1980 a literature based on the mechanisms of trading began (forms of trading, rules on securities trading, market structure, etc.), originating the Theory of Market Microstructure for the valuation of nancial assets, whose models advocate that timing transmits information. Then the literature proposed an extension to risk management by calculating the implied volatility, which is estimated by the realized volatility on an intraday level, and its applications for a ner value at risk (VaR).