The lithium battery energy storage system (ESS) faces problems such as market price fluctuation and uncertainty of frequency modulation (FM) signals when participating in power market. Day-ahead bidding behavior bears revenue risk and real-time bidding behavior needs to adjust. It is necessary...

This research explores the application of extreme value theory in modelling and quantifying tail risks across different economic equity markets, with focus on the Nairobi Securities Exchange (NSE20), the South African Equity Market (FTSE/JSE Top40) and the US Equity Index (S&P500). The study...

We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for...

We investigate the impact of structural shocks on the joint distribution of future real GDP growth and inflation in the euro area. We model the conditional mean of these variables, along with selected financial indicators, using a VAR and perform quantile regressions on the VAR residuals to...

This study examines the systemic operational risk in Morocco's banking sector using a Panel VAR model based on data from three banks over ten years. The model includes real GDP, interbank rate (TMP), and bank credit, alongside indicators of operational, credit, and liquidity risks. The Impulse...

This paper investigates tail-risk transmission and asymmetric dependence in commodity markets using an asymmetric fuzzy vine copula framework applied to gold, crude oil, natural gas, and silver from 1 January 2015 to 1 January 2025, extracted from Yahoo Finance. Bootstrap-based trapezoidal fuzzy...

In deregulated electricity markets, Generation Companies (GENCOs) are exposed to substantial financial risk due to volatile and uncertain electricity prices. Traditional generation asset valuation approaches, which rely primarily on expected profit, fail to adequately capture downside risk under...

This work presents a practical approach to improve risk quantification for heavy-tailed insurance claims through model averaging and grid map visualization, addressing the drawbacks of traditional single "best" model selection commonly used in actuarial and model-fitting literature. This is a...

For many decades, time-series forecasting has been applied to different problems by scientists and industries. Many models have been introduced for the purpose of forecasting. These advancements have significantly improved the accuracy and reliability of predictions, especially in complex...

This study examines the impact of the Russia-Ukraine war on crude oil tail risk using the Conditional Autoregressive Value at Risk (CAViaR) framework. We analyzed 2364 daily observations of West Texas Intermediate (WTI) crude oil futures spanning 1 January 2015 to 11 December 2023, thereby...

This paper presents the first formal comparison of Value at risk (VaR) forecasting performance across various high-frequency volatility models and conventional benchmarks using daily data in the crude oil futures market. Our analysis reveals the following key findings:(1) High-frequency data...

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Leveraging ARMA-GJR-GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas, the framework...

This study examines the use of Filtered Historical Simulation (FHS) to estimate tail risk in cryptocurrency markets for the optimization of robustness in this area under model misspecification. An ARMA-GARCH model is employed on the daily returns on Binance Coin and Litecoin in order to compare...

This paper estimates the daily market risk of Italian bank securities portfolios under different model assumptions, using granular data on all banks and exposures from 2008 to 2023. Market risk is measured via value-at-risk and expected shortfall, estimated with three approaches: (1)...

This work looks for the optimal allocation of different assets, namely, the G7 stock indices, commodities (gold and WTI crude oil), cryptocurrencies (Bitcoin and Ripple), and S&P Green Bond, over four periods: before the COVID-19 crisis, during the COVID-19 crisis and before the Russia-Ukraine...

Drawdown is an important risk measure in both theory and practice. Most drawdown measures use the running peak as the reference point from which to calculate the drawdown. Instead, the start-to-low drawdown (SLD), which references the start of the period, is firstly proposed as a relevant...

This paper assesses the exchange rate risk exposure of Tunisia's external public debt portfolio using the delta-normal Value at Risk (VaR) approach. Based on daily data from 2004 to 2019, focusing on the main borrowing currencies (the euro, US dollar, and Japanese yen), the study identifies the...

This study investigates whether publicly listed cannabis shares provide enough risk-adjusted returns to warrant their incorporation into diversified portfolios. An equally weighted portfolio of cannabis companies is constructed using monthly data from January 2015 to December 2024. Risk-adjusted...

This study investigates the heterogeneous responses of Bitcoin (BTC), gold (GOLD), and green bonds (GBOND) to geopolitical risk (GPR) shocks across different market regimes and investment horizons. Using a triadic empirical framework that encompasses wavelet quantile-on-quantile regression...