COVID-19 pandemic is an extreme event that created a turmoil in stock markets around the world. This unexpected circumstance poses a critical question whether the prevailing models can help predict the plummets of indices, hence the returns. In this study, we model the stock returns using...

This paper aims to study the forecasting capabilities of several models under the Markov regime-switching (MRS) and the extreme value theory (EVT) frameworks applied to daily electricity prices in the New Zealand electricity market. The MRS models in this study include up to five regimes, with...

Macroeconomic data are an important piece of information in decision making for both the public and private sectors in Thailand. However, the release of key macroeconomic data, usually in a lower frequency such as quarterly, is not always in a timely manner. Using the higher frequency data such...

Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact on electricity price and volatility. In this...

In this study, we present an empirical comparison of statistical models and machine learning models for daily electricity price forecasting in the New Zealand electricity market. We demonstrate the effectiveness of GARCH and SV models and their t-distribution variants when paired with feature...