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Macroprudential Policy: a Blessing or a Curse?

After the destructive impact of the global financial crisis of 2008, many believe that pre-crisis financial market regulation did not take the “big picture” of the system sufficiently into account and, subsequently, financial supervision mainly “missed the forest for the trees”. As a result, the need for macroprudential aspects of regulation emerged, which has recently become the focal point of many policy debates. This has also led to intense discussion on the contours of monetary policy after the post-crisis “new normal”. Here, I review recent progress in empirical and theoretical research on the effectiveness of macroprudential tools, as well as the current state of the debate, in order to extract common policy conclusions. The work highlights that, despite the achievements in the literature, the current experience and knowledge of how macroprudential instruments work, their calibration, and the mechanisms through which they interact with each other and with monetary policy are rather limited and conflicting. Moreover, I critically survey and note the current challenges faced by macroprudential regulation in creating stable, yet efficient financial systems. At the same time, emphasize the importance of accepting that many risks may remain, requiring that we proceed prudently and develop better plans for future crises.

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Measuring Demand Elasticity in the Italian Power Market: a Bayesian Experiment under dual pricing scheme

This study run experiment aiming to provide a flexible and analytically elegant framework to reliably estimate the price elasticity of electricity demand. Inference pertains to the demand at hourly level in the Italian wholesale electricity market and uses individual demand bid data. Individuals' bids represent the ex-ante willingness to pay and thus allows for constructing a market demand grounded in the consumer behavior theory, by exploiting the duality approach. Bayesian econometric estimation is applied, relaxing homoskedasticity assumptions of the traditional linear regression model. It allows to identify robust results, showing that elasticity varies significantly among hours of the day, zone segmentation as well as the level of equilibrium price. Bayesian inference provides also the opportunity to include prior information sourced from previous studies and the institutional struc- ture governing the agents' behavior. This prior information involves some degree of uncertainty, for this reason Bayesian approach assigns it a probability distribution. Using Bayes rule, prior information are then updated according to the observed data. Results validate the market reform designed to foster competition and increase wel- fare even through the time-varying pricing schemes that trigger the consumers' price reaction.

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