Three Essays on Using High Frequency Data in Estimating Financial Risks

This dissertation comprises of three stand-alone research papers, all considering the use of high frequency financial data for financial market risk measurement. The first chapter considers the extraction of liquidity information from the intraday limit order book to enhance the daily market risk measures for the dimension of liquidity risk. While liquidity risk in trading is understood to be important for practitioners, it is not well understood how it should be quantified and included in estimating market risks. The second and third chapter contribute to the ongoing research on realized measures in the multivariate context. Chapter two provides some new ideas about how to characterise the ex-post dependence, which we term as "realized dependence", between two or more assets using intraday data. Chapter three describes a new approach via partial identification to deal with the biases that currently plague the estimation of realized covariance and correlation.