We introduce an intuitive method of enhancing low-frequency volatility measures used to compute Value-at-Risk (VaR) by incorporating intradaily liquidity information from the limit order book. Using the quote slope of Hasbrouck and Seppi (2001), a compound liquidity measure comprising the...

Classical financial market theories built upon the assumption of a perfect market have been coping with frictions on both developed and emerging markets. There are numerous factors affecting the operation of financial markets and their participants’ behavior, but illiquidity is a continuous...

Large data sets in finance with millions of observations have becomewidely available. Such data sets enable the construction of reliablesemi-parametric estimates of the risk associated with extreme pricemovements. Our approach is based on semi-parametric statisticalextreme value analysis, and...

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem,...

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...

Classical financial market theories built upon the assumption of a perfect market have been coping with frictions on both developed and emerging markets. There are numerous factors affecting the operation of financial markets and their participants' behavior, but illiquidity is a continuous...