This study investigates the volatility spillover dynamics between the VIX fear index and the Magnificent Seven technology stocks - namely Microsoft, Apple, Nvidia, Amazon, Alphabet, Meta Platforms, and Tesla - over the period of June 2012 to March 2024. To achieve this objective, the variance...

This study explores how the volatility spillover mechanism and dynamic dependence among the founding BRICS equity markets, namely IBOVESPA, MICEX, Nifty 50, SSE, and JSE, have evolved over time using a multivariate DCC-GARCH model. The analysis is conducted across three distinct regimes: the...

This study examines the role of volatility mean reversion in option pricing and evaluates the performance of commonly used volatility estimators within a broad market context. Using a comprehensive dataset of end-of-day option chains for the 100 most actively traded U.S. equities from 2018 to...

This paper examines the asymmetric impact of economic policy uncertainty (EPU) on exchange rate volatility across a sample of developed and emerging economies. Using an asymmetric GARCH-MIDAS model, volatility is decomposed into short-term and long-term components, with the latter associated...

This paper compares the daily return volatility by four leading international indices: JSE Top 40, FTSE 100, Nikkei 225 and S&P/ASX 200. The return series are modelled in ARMA process, where ARMA(1,3) values are taken for JSE Top 40 and S&P/ASX 200, ARMA(0,0) for FTSE 100, and ARMA(1,2) for...

This study examines the asymmetric responses of stock market indices in Türkiye and Kazakhstan to oil price shocks during the 2010-2025 period. Using the Nonlinear Autoregressive Distributed Lag (NARDL) model, the study decomposes the nonlinear effects of oil price fluctuations on financial...

This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017-2024. Particular attention is given to the structural shift associated with the 2024 U.S....

This paper evaluates an Adaptive LASSO-MGARCH model for multivariate volatility forecasting, with an application to green and conventional bonds, equities, energy commodities, and EU carbon allowances. By introducing coefficient-specific adaptive penalisation directly into the multivariate GARCH...

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...

Conventional VAR-based volatility spillover measures rely on homoskedasticity and single-Gaussian assumptions, limiting their ability to capture structural breaks and heterogeneous shocks during crises. This study develops a flexible framework to analyze volatility transmission in U.S. commodity...

Exogenous shocks can affect equity markets by changing volatility and cross-market co-movement. This study examines how two U.S.-centred events, treated as different shock types, influence time-varying conditional correlations between the U.S. stock market and other G7 markets. The uncertainty...

The association between cryptocurrency and sustainability is a complex and growing topic. Given that such linkage requires a continuous investigation, this empirical research, unlike the existing literature, explores if the volatility dynamics of digital assets are driven by the changes in...

This study examines the interdependencies between European Union Allowances (EUAs) on carbon emissions and traditional cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Ripple and Bitcoin Cash) and green cryptocurrencies (Cardano, Stellar, EOS, TRON and IOTA) from January 2018 to February 2022....

This study investigates the dynamics of Bitcoin's realized volatility by extending the Heterogeneous Autoregressive (HAR) framework to incorporate external shocks from major financial and commodity markets, namely the NASDAQ-100, Brent crude oil, and gold. To capture potential asymmetries,...

This study provides a comprehensive evaluation of six volatility forecasting models applied to twelve dominant and less dominant cryptocurrencies across multiple time horizons using high-frequency intraday data. The exponential generalized autoregressive conditional heteroskedastic (EGARCH),...

We apply rolling-window econometric methods, including GARCH(1,1) estimation, Bai-Perron structural break detection, CUSUM stability testing, and Granger causality analysis in bivariate VAR frameworks, to analyze the temporal dynamics of market integration in cryptocurrency perpetual futures,...

Examining the economies of the European Union from 2000 to 2023, we have found no strong evidence that the inflation rate has a negative impact on economic growth. In contrast, in line with conventional economic theory, higher interest rates are associated with lower economic growth. The results...

European electricity markets are in the midst of unprecedented changes-caused by Russia's invasion of Ukraine and the rise of renewable sources of energy. Using high-frequency data, this paper investigates volatility spillovers across 24 countries in the European Union (EU) during the period...

In the analysis of multivariate stochastic volatility models, many estimation procedures begin by transforming the data, taking the logarithm of the squared returns to obtain a linear state space model. A well-known series representation links the correlations between elements of the observation...

In this paper, we carry out a comprehensive comparison of Gaussian generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive stochastic volatility (ARSV) models for volatility forecasting using the S&P 500 Index. In particular, we investigate their performance using...