One of the main issues in the recent Chinese financial reform is aimed at effectively measuring systemic risk and taking appropriate measures to ensure its sustainability and prevent new crises. In this paper, we firstly introduced the present macro-prudential policies implied in China and pointed out the existing problems. Secondly, we analyzed the banks’ assets riskiness and the banks’ probability to default, then, by means of a leave-one-out model, we measured each commercial bank systemic risk contribution. Thirdly, based on comprehensive empirical results and theoretical analysis, we provided some references for macro-prudential regulation and supervision. Results show that systemic risk is increasing in 2013–2017, in particular with reference to contagion risk, with a specific concentration within joint-stock commercial banks, suggesting a specific attention of regulators and supervisors for this category.

Battery energy storage systems (BESS) rely on accurate electricity price forecasts to maximize arbitrage profits in day-ahead markets. We examined whether specific forecasting models, ranging from statistical benchmarks to machine learning methods, consistently deliver superior financial outcomes for storage operators. Using real market data from the Italian day-ahead electricity market over 2020–2024, we compared univariate singular spectrum analysis (SSA), ARIMA, SARIMA, random forests, and a 30-day simple moving average under a unified trading framework. All models were evaluated based on their ability to generate arbitrage profits. Univariate SSA clearly outperformed all alternatives, achieving on average 98% of the theoretical maximum profit while maintaining the lowest forecast error. Among the other models, simpler approaches performed surprisingly well: they achieved comparable, if not superior, profit performance to more complex, hour-specific, or computationally intensive configurations. These results were robust to plausible variations in battery parameters and retraining schedules, suggesting that univariate SSA offers a uniquely effective forecasting solution for battery arbitrage and that simplicity can often be more effective than complexity in operational revenue terms.

The question of additional taxes on banking institutions has recently been debated. At the same time, financial regulation in the banking sector is undergoing many changes aimed at strengthening financial stability. This chapter uses SYMBOL, a micro-simulation model of the banking system, to estimate contributions to systemic risk of individual banks under various future regulatory scenarios and compares them to their potential tax liabilities under alternative designs of Financial Activity Taxes and Bank Levies. The results show that when contagion is not avoided, all taxes perform about the same way. However, when contagion is avoided, bank levies outperform FATs.