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Monitoring Risks in Financial Markets

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The project develops new econometric methodologies and models to understand and monitor the interplay between liquidity, asset prices volatilities and covolatilities, with the goal of identifying early signals about incoming risks that can arm the financial system as a whole. The project moves beyond the standard approach of analysing liquidity and volatilities in a univariate setup. The proposal points to methodological and modelling contributions that have implications for policy makers and financial markets operators. The project is articulated into two main workpackages (WP): WP1: Monitoring structural changes in volatilities and correlations: monitoring procedures are relevant for bank supervisors, policy makers and portfolio managers. The early identification of changes in regimes is relevant for bank supervisors, policy makers and portfolio managers. Structural breaks in correlations play an important role in determining bull and bear markets as well as in identifying contagion effects between markets and asset classes. They are also associated to changes in the relationships among macroeconomic and financial variables. The main goal of sequential monitoring procedures is to detect the occurrence of a change point once new data become available minimizing the time delay. Thus detecting and dating break points are two strictly connected issues. We will develop multivariate procedures to monitor changes in volatilities, correlations and liquidity risks. WP2: Monitoring liquidity and its systemic dimension: we extend the univariate analysis of liquidity to a multivariate setting, with the goal of assessing how an illiquid episode on a given stock can increase the probability of observing an illiquidity episode also for another stock, and viceversa. We study this notion of commonality in illiquidity adopting a number of statistical/econometric models and techniques previously employed for the assessment of the connectedness in the market/credit risk literature, such as through dynamic models for volatilities and correlations, with the notion of conditional Value-at-Risk and marginal expected shortfall, or by extrapolating factors in the liquidity measures with the goal of identifying a network structure and disentangle the determinants of systemic risk

 

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