In this work, we introduce an innovative approach to managing electricity costs within Germany's evolving energy market, where dynamic tariffs are becoming increasingly normal. In line with recent German governmental policies, particularly the Energiewende (Energy Transition) and European Union...

The combination of stochastic derivative pricing models and downside risk measures often leads to the paradox (risk, return) = (−infinity, +infinity) in a portfolio choice problem. The construction of a portfolio of derivatives with high expected returns and very negative downside risk...

As life expectancy increases, pension plans face growing longevity risk. Standardized longevity-linked securities such as survivor contracts allow pension plans to transfer this risk to capital markets. However, more consensus is needed on the appropriate mortality model and premium principle to...

This study examined how uncertainty and global risk affect financial markets in emerging economies, focusing on foreign investment, CDS spreads, exchange rates, and stock return volatility. Using over 8.6 million ticker transaction observations and structural vector autoregression (VAR) models,...

This study investigates systemic risk, return patterns, and diversification within the Johannesburg Stock Exchange (JSE) during the COVID-19 pandemic, utilizing data-centric approaches and the ARMA-GARCH vine copula-based conditional value-at-risk (CoVaR) model. By comparing three investment...

This paper studies the volatility of electricity spot prices in the Nordic market (Sweden, Finland, Denmark, and Norway) under regime switching. Utilizing Markov-switching GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, we provide strong evidence of nonlinear regime...

Insurance companies need to calculate solvency capital requirements in order to ensure that they can meet their future obligations to policyholders and beneficiaries. The solvency capital requirement is a risk management tool essential for addressing extreme catastrophic events that result in a...

Recent rises in macroeconomic volatility have prompted the introduction of quantile vector autoregression (QVAR) models to forecast macroeconomic risk. This paper provides an extensive evaluation of the predictive performance of QVAR models in a pseudo-out-of-sample experiment spanning 112...

This paper compares the renowned GARCH model with a novel one, the Generalized Autoregressive Score (GAS) model in terms of forecasting performance. Considering the gap in the literature, this study focuses on the Turkish stock and FX markets. The analysis covers 25 years (1999-2023), of which...

We introduce a new coherent risk measure, the minimal-entropy risk measure, which is built on the minimal-entropy 𝜎-martingale measure - a concept inspired by the well-known minimal-entropy martingale measure used in option pricing. While the minimal-entropy martingale measure is commonly...

This study proposes a hybrid forecasting model that integrates the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a Long Short-Term Memory (LSTM) neural network to estimate Value at Risk (VaR) in the Rwandan foreign exchange market. The model is designed to capture...

Macroeconomic research often relies on structural vector autoregressions, (S)VARs, to uncover empirical regularities. Critics argue the method goes awry due to lag truncation: short lag-lengths imply a poor approximation to important data-generating processes (e.g. DSGE-models). Empirically,...

As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study...

This study explores the presence of hidden market regimes in a sector-specific stock index within the Thai equity market. The behavior of such indices often deviates from broader macroeconomic trends, making it difficult for conventional models to detect regime changes. To overcome this...

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...