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The present work investigates the impact on financial intermediation of distributed ledger technology (DLT), which is usually associated with the blockchain technology and is at the base of the cryptocurrencies' development. "Bitcoin" is the expression of its main application since it was the first new currency that gained popularity some years after its release date and it is still the major cryptocurrency in the market. For this reason, the present analysis is focused on studying its price determination, which seems to be still almost unpredictable. We carry out an empirical analysis based on a cost of production model, trying to detect whether the Bitcoin price could be justified by and connected to the profits and costs associated with the mining effort. We construct a sample model, composed of the hardware devices employed in the mining process. After collecting the technical information required and computing a cost and a profit function for each period, an implied price for the Bitcoin value is derived. The interconnection between this price and the historical one is analyzed, adopting a Vector Autoregression (VAR) model. Our main results put on evidence that there aren't ultimate drivers for Bitcoin price; probably many factors should be expressed and studied at the same time, taking into account their variability and different relevance over time. It seems that the historical price fluctuated around the model (or implied) price until 2017, when the Bitcoin price significantly increased. During the last months of 2018, the prices seem to converge again, following a common path. In detail, we focus on the time window in which Bitcoin experienced its higher price volatility; the results suggest that it is disconnected from the one predicted by the model. These findings may depend on the particular features of the new cryptocurrencies, which have not been completely understood yet. In our opinion, there is not enough knowledge on cryptocurrencies to assert that Bitcoin price is (or is not) based on the profit and cost derived by the mining process, but these intrinsic characteristics must be considered, including other possible Bitcoin price drivers.

We analyse the structural aspects of the banking Risk Appetite Framework (RAF), suggesting an operational application in the light of the detailed recommendations of the banking supervisors. We develop a quantitative approach that could be used to adapt to the requirements of these regulations and that might be useful for management purposes. This approach is empirically applied to the balance sheets of the Italian banking system. Our findings show that the Italian banks are generally underexposed in terms of credit risk and market risk, so there is room for shifting the risk profiles towards higher thresholds with a view to improving the credit institutions' profitability while keeping their RAF consistent with the regulatory bodies' requirements. The quantitative model can be applied effectively to different types of risk, making the necessary adjustments according to the particular features of the profile being examined.

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