Projects

Financial Networks: Examining Financial Linkages using Network Approach

funded by the Czech Science Foundation

GA20-11769S (2020-2022)

This project focuses on the construction of complex networks of financial markets’ linkages around the world. First, several measures of associations will be considered, describing different aspects of market relationships. Second, suitable subgraphs will be identified, in order to create a suitable network representation capturing main dependencies between markets. Finally, the network structure will be used in inference either as explanatory factor in modelling market characteristics, as well as a dependent variable to be explained by market nodal and edge attributes. This modelling should provide insights into phenomena such as return and volatility spillovers, as well as contagion in times of crisis.

A FINancial supervision and TECHnology compliance training programme (FINTECH)

funded by the European Union’s Horizon 2020 research and innovation programme

grant agreement No 825215 (2019-2020)

While innovation in finance is not a new concept, the focus on technological innovations and its pace have increased significantly. Fintech solutions that make use of big data analytics, artificial intelligence and blockchain technologies are currently introduced at an unprecedented rate. These new technologies are changing the nature of the financial industry, creating many opportunities for fintechs to offer more inclusive access to financial services (European Commission, 2018). The advantages notwithstanding, fintech solutions leave the door open for many challenges such as cyber-attacks, underestimation of creditworthiness, potential for fraud, compliance concerns, consumer and investor protection issues and disrupted market integrity, which represent central points of interest for regulators and supervisory bodies.

Forecasting Volatility in Emerging Financial Markets

funded by the Czech Science Foundation

GA18-05829S (2018-2020)

This project designs procedures for forecasting market volatility for financial markets, where high-frequency data are available for short periods, are not available, or are of poor quality. We contribute to the literature in that we are going to use a battery of forecasting models based on range-based volatility estimators and compare their performance against same models using realized volatility estimators. A complex thorough empirical comparison was not made before. Second, we contribute to the literature in that we investigate under which market conditions range-based forecasts are more likely to offer competitive forecasts. We test whether liquidity, turnover, volatility level, price discontinuities play a role.