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...

In this paper, we modify Duan’s (1995) local risk-neutral valuation relationship (mLRNVR) for the GARCH option-pricing models. In our mLRNVR, the conditional variances under two measures are designed to be different and the variance process is more persistent in the risk-neutral measure than...

Using data from Thailand's Labour Force Survey from 2012 to 2021, the differences over time were investigated for the likelihood of youths moving between employment states, such as unemployment, employment and out of the labour force. Additional transitions were calculated for groups moving...

This paper extends the analysis of bivariate seemingly unrelated (SUR) Tobit model by modeling its nonlinear dependence structure through copulas. The capability in coupling together the different marginal distributions allows the flexible modeling for the SUR Tobit. The ability in capturing...

This paper proposes a generalized class of univariate skew distributions that are constructed through mixture of two scaled normal distributions. The proposed skew distributions with the skewness parameter defined in the (0,1) interval allow us to have an application on parametric quantile...

Parallel computation is a fast growing computing environment in many areas including computational Bayesian statistics. However, most of the Bayesian parallel computing have been implemented through the sequential Monte Carlo method where model parameters are updated sequentially and it is...

This paper proposes a new approach to analyze multiple vector autoregressive (VAR) models that render us a newly constructed matrix autoregressive (MtAR) model based on a matrix-variate normal distribution with two covariance matrices. The MtAR is a generalization of VAR models where the two...