Oscillations are an ineluctable attribute of financial markets. Market turmoil is the unpredicted growing and plummeting of the stock market. Adjustments in market reaction, economic tendencies, and liquidity restrictions are constituents that trigger stock markets to be unstable. Market chaos is unavoidably supplemented by a stream of economic figures and evaluations of government economic strategies and how they may influence markets. The global integration of stock markets entails a confluence of market risk and price. However, the occurrence of networks in financial markets can be a valuable component, but it also can behave as an impetus of contagion throughout the system.

Financial returns show volatility clustering rather than a steady volatility. Discrete time models are optimum for the quantitative and accurate assessment of the patterns of regular price variations. There are two leading types of discrete-time models towards exploring stock price volatility: autoregressive random variance (ARV) or stochastic variance (SV) model and autoregressive conditional heteroskedastic (ARCH) model. Moreover, there are the successors of these models such as the Glosten-Jagannathan-Runkle GARCH (GJR-GARCH), threshold GARCH (TGARCH), or integrated generalized autoregressive conditional heteroskedasticity (IGARCH). SV models explore the volatility process itself individually, whereas GARCH specifications exhibit the characteristic that the volatility process is set out as a function of the earlier observations. Both SV and GARCH models can be regarded as cases of state-space models (SSMs), which are commonly employed for the analysis of time series data and dynamical systems.

The aim of this Special Issue is to bring together original research and review articles discussing stock market modelling and forecasting based on the most recent and advanced models in discrete time.

Potential topics include but are not limited to the following:

• Oil shock and stock market volatility
• Volatility spillovers between worldwide financial markets
• Time-varying volatility spill over based on the DCC-GARCH model
• Volatility transmission across commodity futures and stock markets
• Returns and volatility of future energy markets
• Forecasting energy market volatility using GARCH model
• Analyzing stock market uncertainty
• Volatility persistence in cryptocurrency markets
• Stock market efficiency
• Stock market anomalies
• Value-at-risk methodology for efficient portfolio risk administration