Maria Elena De Giuli

This paper focuses on the study of an economic equilibrium problem for an electricity market model in a multistage-stochastic framework, where,stage by stage, the uncertainty evolves with continuity. We analyze the point of view of a finite number of power companies in a sequence of competitive markets.Each of them produces electricity, both with conventional and renewable-based plants, participates in the trade in the spot markets that open after the uncertainty is revealed, and signs bilateral and forward contracts. Moreover, we capture the risk attitude of each power company by considering a suitable coherent risk measure in the problem’s formulation. In order to prove the existence of at least one equilibrium solution, we introduce a suitable quasi-variational inequality formulation. In this light, we also investigate suitable regularity properties of the involved superdifferential operator in the presence of certain parameter perturbations in Banach spaces.

We study the optimal management of a photovoltaic system's battery owned by a self-consumption group that aims to minimize energy consumption costs. We assume that the photovoltaic system is composed of a photovoltaic panel and a battery, where the photovoltaic panel produces energy according to a certain stochastic process. The management of the battery is the responsibility of a group administrator, who makes the joint decision to either store part of the photovoltaic energy production and sell the remaining energy at the electricity spot price, or discharge part of the energy stored in the battery and sell it in the electricity market. Inspired by European Union and Italian legislation, which promote incentives for energy transition and renewable energy production, we assume that the group receives a monetary incentive for the virtual self-consumed energy, defined as the minimum between the power bought from the grid to satisfy the group's power demand and the energy sold to the market. In this case, the energy sold by the group is a mix of part of the photovoltaic production that is not stored and part of the energy discharged from the battery. We model the problem as a stochastic optimal control problem, where the optimal strategy is the joint charge-discharge decision that minimizes the group's energy consumption costs. We find the solution numerically by applying a finite difference scheme to solve the Hamilton-Jacobi-Bellman equation associated with the value function of the optimal control problem.

In order to characterize non-linear system dynamics and to generate term structures of joint distributions, we propose a flexible and multidimensional approach, which exploits Wasserstein barycentric coordinates for histograms. We apply this methodology to study the relationships between the performance in the European market of the renewable energy sector and that of the fossil fuel energy one. Our methodology allows us to estimate the term structure of conditional joint distributions. This optimal barycentric interpolation can be interpreted as a posterior version of the joint distribution with respect to the prior contained in the past histograms history. Once the underlying dynamics mechanism among the set of variables are obtained as optimal Wasserstein barycentric coordinates, the learned dynamic rules can be used to generate term structures of joint distributions.

In this paper, we propose a multivariate procedure based on multidimensional visibility graphs to detect changes in the market volatility of UK financial indices, considered both before and after Brexit main events. We produce a graph-theoretical representation of volatility time series derived from equity indexes, government rates and currencies to investigate the behavior of the aggregate market volatility through the use of global centrality measures. By employing a stylized agent-based model, we show that the proposed approach is able to discriminate between periods of high and low volatility, both in the temporal dimension and cross-sectionally among multiple time series. We aim at recognizing whether external news related to the Brexit process could induce significant ‘after-shocks’ (and also ‘pre-shocks’) in the system, by producing dynamic relaxation in the values of centrality measures, in line with the cascade effects described by the Omori earthquake law. In particular, high volatility cascades dissipate into the market via power-law relaxation. When compared with other categories of events, such as Bank of England monetary policy announcements, we observe significant market inefficiency in processing Brexit related news. We also find that strong market surprise related to specific Brexit news or a correct discount of some Brexit announcements can produce an inverse Omori law exhibiting convex relaxation.

The global financial crisis in 2008, and the European sovereign debt crisis in 2010, highlighted how credit risk in banking sectors cannot be analysed from a uniquely micro-prudential perspective, focused on individual institutions, but it has instead to be studied and regulated from a macro-prudential perspective, considering the banking sector as a complex system. Traditional risk management tools often fail to account for the complexity of the interactions in a financial system, and rely on simplistic distributional assumptions. In recent years machine learning techniques have been increasingly used, incorporating tools such as text mining, sentiment analysis, and network models in the risk management processes of financial institutions and supervisors. Network theory applications in particular are increasingly popular, as they allow to better model the intertwined nature of financial systems. In this work we set up an analytical framework that allows to decompose the credit risk of banks and sovereign countries in the European Union according to systematic (system-wide and regional) components. Then, the non-systematic components of risk are studied using a network approach, and a simple stress-test framework is set up to identify the potential transmission channels of distress and risk spillovers. Results highlight a relevant component of credit risk that is not explained by common factors, but can still be a potential vehicle for the transmission of shocks. We also show that due to the properties of the network structure, the transmission of shocks applied to different institutions is quite diversified, both in terms of breadth and speed. Our work is useful to both regulators and financial institutions, thanks to its flexibility and its requirement of data that can be easily available.

In this paper we aimed to examine the profitability of technical trading rules in the Bitcoin market by using trend-following and mean-reverting strategies. We applied our strategies on the Bitcoin price series sampled both at 5-min intervals and on a daily basis, during the period 1 January 2012 to 20 August 2019. Our findings suggest that, overall, trading on daily data is more profitable than going intraday. Furthermore, we concluded that the Buy and Hold strategy outperforms the examined alternatives on an intraday basis, while Simple Moving Averages yield the best performances when dealing with daily data.