Nuttanan Wichitaksorn

This paper examines how operational-exposure measures observed in 2020 disclosures and digital adaptation are associated with performance during and after the COVID-19 pandemic. Using firm disclosures, financial statements and interview evidence from Thai firms over 2018–2023, we construct measures of task-level physical proximity and digital adaptation intensity and estimate fixed-effects interaction models. Higher Physical Closeness scores are associated with larger profitability declines during the pandemic shock, under business-sector-by-quarter fixed effects, the association remains statistically significant only for return on assets (ROA). We find no statistically significant association for Site-dependent Work under these fixed effects. The positive baseline associations between digital adaptation and later performance are not statistically significant after industry-specific quarterly shocks are absorbed. The joint analysis provides no consistent evidence that this association varies with firms’ operational exposure. We use the annual estimates as the primary results and treat the quarterly estimates as supplementary evidence because their confidence intervals are wide.

This study applies intermarket analysis to forecast the New Zealand NZX 50 stock returns using a hybrid framework that combines econometric models with machine learning (ML) algorithms. Daily return data from 3 January 2001 to 31 December 2024 are employed to examine the interconnectedness between the New Zealand equity market and major global financial markets. This study follows a three-stage methodology: ML-based feature selection using LASSO, ridge, and elastic net regressions; econometric validation through a Global Vector Autoregressive (GVAR) model; and forecasting implementation using Support Vector Regression (SVR), Random Forest (RF), Long Short-Term Memory, and Artificial Neural Networks. Feature selection consistently identifies the Australian ASX 200, Japanese Nikkei 225, U.S. S&P 500, and U.S. 10-year Treasury yield as the most influential predictors, with Australia exerting the strongest impact. GVAR results reveal significant short-term spillover effects, but no long-term co-integrating relationships, indicating independent market trends. The U.S. market emerges as the dominant transmitter of shocks, while New Zealand acts as a net receiver. Forecasting results show RF and SVR outperform alternative models, with optimal performance achieved using a fifth-lag structure. The findings support short-term, spillover-based investment strategies and contribute to a transparent, replicable framework for forecasting equity markets in small open economies.

This study develops a comparative forecasting framework that integrates daily weather information with quarterly electricity generation, used here as a proxy for electricity demand in New Zealand, through mixed-frequency modelling approaches. The analysis progresses from baseline univariate time-series models to classical mixed data sampling regressions, advanced regularised and autoregressive mixed-frequency models, and machine learning-based mixed-frequency methods. The forecasting results show that mixed-frequency models can improve upon traditional univariate benchmarks by incorporating higher-frequency weather information. Among the advanced approaches, autoregressive mixed-frequency models deliver strong forecasting performance, particularly over shorter recent evaluation windows, while seasonal time-series benchmarks such as SARIMA remain highly competitive and achieve the lowest RMSE in the main eight-quarter evaluation period. Machine learning-based mixed-frequency models show mixed performance, likely reflecting the challenges posed by data dimensionality and limited sample size. The proposed framework provides interpretable forecasts and offers practical insights for electricity system planning in renewable-dominated energy systems.

In this study, we propose a general mathematical modelling framework based on the characteristics of elite athletes’ movements in the touch rugby matches to investigate the dynamic relationship between physical workload and injury risk over time. Our framework extends the Cox-based model in the context of touch rugby by incorporating a time-scaling component and cluster-specific heterogeneity simultaneously. In addition, we allow for the inclusion of covariates (e.g., velocity variation) to capture their effects. We applied our model to high-frequency wearable sensor data collected from 27 elite athletes (15 men and 12 women). The empirical study results show that our model, time-scaled frailty model (TSFM), demonstrates better goodness-of-fit than traditional frailty and Andersen–Gill models. The results reveal that higher velocity variation, particularly during high-intensity phases, and longer time of continuous exposure to the workload spike state significantly increased overload risk, ultimately resulting in injury. It also highlights the importance of individual differences, even under the same exercise intensity. These insights provide coaches with an evidence-based framework for athlete monitoring, allowing for more personalized training loads, tactical deployment, and injury prevention strategies in elite touch rugby environments.

This study proposes Bayesian estimation of multivariate regular vine (R-vine) copula models with generalized autoregressive conditional heteroskedasticity (GARCH) margins modeled by Gaussian-mixture distributions. The Bayesian estimation approach includes Markov chain Monte Carlo and variational Bayes with data augmentation. Although R-vines typically involve computationally intensive procedures limiting their practical use, we address this challenge through parallel computing techniques. To demonstrate our approach, we employ thirteen bivariate copula families within an R-vine pair-copula construction, applied to a large number of marginal distributions. The margins are modeled as exponential-type GARCH processes with intertemporal capital asset pricing specifications, using a mixture of Gaussian and generalized Pareto distributions. Results from an empirical study involving 100 financial returns confirm the effectiveness of our approach.

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 paper, we employ a mixed-frequency vector autoregression (MF-VAR) framework where we propose a VAR specification to the reverse unrestricted mixed-data sampling (RU-MIDAS) model, called RU-MIDAS-VAR, to provide point forecasts of half-hourly electricity prices using several weather variables and electricity demand. A key focus of this study is the use of variational Bayes as an estimation technique and its comparison with other well-known Bayesian estimation methods. We separate forecasts for peak and off-peak periods in a day since we are primarily concerned with forecasts for peak periods. Our forecasts, which include peak and off-peak data, show that weather variables and demand as regressors can replicate some key characteristics of electricity prices. We also find the MF-VAR and RU-MIDAS-VAR models achieve similar forecast results. Using the LASSO, adaptive LASSO, and random subspace regression as dimension-reduction and variable selection methods helps to improve forecasts where random subspace methods perform well for large parameter sets while the LASSO significantly improves our forecasting results in all scenarios.

Background and aims: Child maltreatment is an important component of alcohol’s harm to others. No previous studies have assessed the association between multiple parental alcohol events and child maltreatment. This study aimed to measure the impact of parental alcohol‐attributable events on the incidence rate of child maltreatment events over the span of childhood and adolescence in New Zealand.Design, setting and participants: Longitudinal analysis using the birth cohort data of all live births (n = 58 359) in New Zealand in 2000, with the cohort followed until age 17 years using linked data from the Statistics New Zealand Integrated Data Infrastructure (IDI) from 1995 to 2017.Measurements: Zero‐inflated negative binomial and count regression models were used to measure the association between the number of child maltreatment events [collected from publicly funded hospital discharges, Programme for the Integration of Mental Health Data (PRIMHD), Oranga Tamariki, Children’s Action Plan (CAP) and police data sets] and parental exposure to alcohol‐attributable hospitalization or service use for mental health/addiction. Potential confounders were included for parents and children.Findings: Of 58 359 children in the cohort, 86% (50 319) had not experienced any documented maltreatment events. Among those at risk of being maltreated children, the maltreatment incidence rate ratio increased by 35.2% (95% confidence interval = 18.1–55.6%) if a child was exposed to parental alcohol‐attributable hospitalization or service use for mental health addiction events. For each additional parental alcohol‐attributable hospitalization or service use for mental health or addiction, the risk of being maltreated children increased by 12.8%.Conclusions: In New Zealand, hazardous alcohol consumption by parents appears to be associated with child maltreatment alongside other contributing factors, in particular parental lower educational attainment.

With the world moving forward, leaders in the university sector must proactively innovate in the delivery of teaching programs. The way students learn is changing, such as reduced lecture attendance and a growing demand for digital learning. This impacts staff workload and can negatively affect both the in-class and online student experience. We need to find ways to enhance student learning and engagement in an attendance-agnostic manner. In this work-in-progress paper, we describe the process of re-designing three courses from the Mathematical Sciences Department at Auckland University of Technology using the flipped classroom and active learning pedagogies. We reflect on the implementation of these courses, which were flipped for one semester (12 weeks) with students who had no previous experience with flipped learning. Despite challenges in involving students in pre-class tasks due to their unfamiliarity, positive feedback on lecture recordings and scaffolding of activities was received from motivated students. Future enhancements of each course will be incorporating students' feedback and the lessons learnt from the course’s first run.