This study complements the current literature, providing a thorough investigation of the lead–lag connection between stock indices and sovereign credit default swap (CDS) returns for 14 European countries and the US over the period 2004–2...
We investigated the connectedness of the returns and volatility of clean energy stock, technology stock, crude oil, natural gas, and investor sentiment based on the time-varying parameter vector autoregressive (TVP-VAR) connectedness approach. The em...
In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structu...
Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model cali...
We propose the use of wavelet-based semiparametric models for forecasting the value-at-risk (VaR) and expected shortfall (ES) in the crude oil market. We compared the forecast outcomes across different time scales for three semiparametric models, thr...
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 critica...
This paper examined the causal relationship between economic growth and carbon dioxide emissions (CO2) in Rwanda using annual data from 1960–2014. The study was conducted within the framework of the environmental Kuznets curve (EKC) hypothesis...
Accurate forecasts of the U.S. renewable energy consumption mix are essential for planning transmission upgrades, sizing storage, and setting balancing market rules. We introduce a Bayesian Dirichlet ARMA model (BDARMA) tailored to monthly shares of...
A conditional Extreme Value Theory (GARCH-EVT) approach is a two-stage hybrid method that combines a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) filter with the Extreme Value Theory (EVT). The approach requires pre-specification...
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 spanni...
In this study, we aim to investigate the impacts of credit default swaps (CDS) premium as a risk financial indicator on the fluctuations of value of the Turkish lira against the Euro. We try to answer the following questions: Is the CDS premium chang...
The carbon emission allowance (CEA) market is a complex socio-technical and environmental-management system in which regulatory design, trading activity, liquidity conditions, and price volatility interact dynamically. Accurate systems-level tail-ris...
In this study, we analyzed the risk faced by the reverse mortgage provider in the case of the lump-sum solution, which is increasingly becoming one of the most popular types of reverse mortgages. The risk faced by the mortgage provider was estimated...
Rising political and economic uncertainty over the world affects all participants on different markets, including stock markets. Recent research has shown that these effects are significant and should not be ignored. This paper estimates the spillove...
This paper develops a queueing-organized framework for within-venue monitoring of BTC/USDT liquidity, signed-flow pressure, and resiliency on Binance. The model treats latent buy and sell pressure as occupancy processes and organizes three empirical...
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...
This study investigates the impact of cyber threats on the clean energy (CE) market, which is increasingly dependent on digital technologies and interconnected infrastructure. The sector’s growing digitalization makes it more susceptible to cyb...
This paper proposes a fuzzy copula-based optimization framework for modeling dependence structures and financial risk under parameter uncertainty. The parameters of selected copula families are represented as trapezoidal fuzzy numbers, and their &alp...
After high-penetration distributed photovoltaics (DPVs) are integrated into AC/DC hybrid distribution networks, stochastic source-load fluctuations, AC-DC coupling, and heterogeneous response characteristics of voltage-regulation devices jointly aggr...
Despite the growing importance of the Saudi capital market, sectoral-level volatility connectedness within Tadawul remains largely unexplored. This study contributes to the literature by applying the Diebold–Yılmaz framework to examine vol...
Many studies report forecasting gains for sophisticated machine-learning and deep-learning models without testing whether those gains survive comparison against simple, reproducible benchmarks. This study tests that question for one-step-ahead monthl...
This study investigates whether U.S. macro-financial indicators improve one-step-ahead forecasts of return connectedness among gold, Brent crude oil, and the U.S. Dollar Index beyond autoregressive persistence. We estimate a time-varying Total Connec...
Policymakers in small open economies need reliable signals of incipient private consumption downturns, yet traditional indicators are revised, noisy, and often arrive too late. This study develops a Romanian-specific early warning system that combine...
As global discourse increasingly centers on environmental, social, and governance considerations, ESG investment has become a major trend in financial markets. Artificial intelligence (AI), through its rapid evolution, has exerted a transformative in...