Discussion of “Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue”

This paper, "Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue," proposes a novel Bayesian approach based on dynamic linear models to share information from different sectors, LSG (Local Store Group), and item category, through the use of auxiliary information (the discount information). The authors demonstrate the feasibility of parallel computing with multiple item categories, making the Bayesian method highly scalable. The proposed method in the paper should have wide applicability in inventory and revenue management. We suggest in this discussion potential areas for further development.

Tail risk forecasting of realized volatility CAViaR models

This research proposes a new class of RES-CAViaR (conditional autoregressive value-at-risk) models, that incorporate daily realized volatility and expected shortfall (ES) to forecast VaR and ES simultaneously. We further consider weekly and monthly realized volatilities in the proposed model to approximate a long-memory process. We employ the Bayesian adaptive Markov chain Monte Carlo approach to estimate all unknown parameters and to jointly predict daily VaR and ES over a 4-year out-of-sample period including the COVID-19 pandemic. Our results show that the realized CAViaR-type models outperform in terms of three backtests, four loss-function criteria, and ES measurement at the 1% level.

Assessing government policies' impact on the COVID-19 pandemic and elderly deaths in East Asia.

This study assesses governments' long-term non-pharmaceutical interventions upon the coronavirus disease 2019 (COVID-19) pandemic in East Asia. It advances the literature towards a better understanding of when and which control measures are effective. We (1) provide time-varying case fatality ratios and focus on the elderly's mortality and case fatality ratios, (2) measure the correlations between daily new cases (daily new deaths) and each index based on multiple domestic pandemic waves and (3) examine the lead-lag relationship between daily new cases (daily new deaths) and each index via the cross-correlation functions on the pre-whitened series. Our results show that the interventions reduce COVID-19 infections for some periods before the period of the Omicron variant. Moreover, there is no COVID-19 policy lag in Taiwan between daily new confirmed cases and each index. As of March 2022, the case fatality ratios of the elderly group in Japan, Hong Kong and South Korea are 4.69%, 4.72% and 1.48%, respectively, while the case fatality ratio of the elderly group in Taiwan is 25.01%. A government's COVID-19 vaccination distribution and prioritisation policies are pivotal for the elderly group to reduce the number of deaths. Immunising this specific group as best as possible should undoubtedly be a top priority.

Bayesian quantile forecasting via the realized hysteretic GARCH model

This research introduces a new model, a realized hysteretic GARCH, that is similar to a three-regime nonlinear framework combined with daily returns and realized volatility. The setup allows the mean and volatility switching in a regime to be delayed when the hysteresis variable lies in a hysteresis zone. This nonlinear model presents explosive persistence and high volatility in Regime 1 in order to capture extreme cases. We employ the Bayesian Markov chain Monte Carlo (MCMC) procedure to estimate model parameters and to forecast volatility, value at risk (VaR), and expected shortfall (ES). A simulation study highlights the properties of the proposed MCMC methods, as well as their accuracy and satisfactory performance as quantile forecasting tools. We also consider two competing models, the realized GARCH and the realized threshold GARCH, for comparison and carry out Bayesian risk forecasting via predictive distributions on four stock markets. The out-of-sample period covers the recent 4 years by a rolling window approach and includes the COVID-19 pandemic period. Among the realized models, the realized hysteretic GARCH model outperforms at the 1% level in terms of violation rates and backtests.

Public opinion concerning governments' response to the COVID-19 pandemic.

OBJECTIVES: Governments around the world have implemented numerous policies in response to the COVID-19 pandemic. This research examines the political issues resulting in public opinion concerning their responses to the pandemic via an international perspective. The objectives of this study are to: (1) measure the association and determine whether differences in political support can be attributed to the presence of approval ratings during the pandemic, and to (2) identify exceptional cases based on statistical predictions. METHODS: We collect information from several open-sourced surveys conducted between June and September 2020 of public sentiment concerning governments' response toward COVID-19. The 11 countries in our sample account for over 50% of the world's Gross Domestic Product (GDP). The study includes country-specific random effects to take into account the data's clustered structure. We consider "political partisanship" and "pre-pandemic approval ratings in 2019" as two potential explanatory variables and employ a mix-effect regression for bounded responses via variable transformation and the wild bootstrap resampling method. RESULTS: According to the wild bootstrap method, the mixed-effect regression explains 98% of the variation in approval ratings during the pandemic in September 2020. The findings reveal partisan polarization on COVID-19 policies in the U.S., with opposing supporters most likely to express negative sentiments toward the governing party. CONCLUSIONS: The evidence suggests that approval ratings during the pandemic correlate to differences in political support and pre-pandemic approval ratings, as measured by approval ratings from the views between governing coalition supporters and opponents.

Bayesian estimation of realized GARCH-type models with application to financial tail risk management

Advances in the various realized GARCH models have proven effective in taking account of the bias in realized volatility (RV) introduced by microstructure noise and non-trading hours. They have been extended into nonlinear or long-memory patterns, including the realized exponential GARCH (EGARCH), realized heterogeneous autoregressive GARCH (HAR-GARCH), and realized threshold GARCH (TGARCH) models. These models with skew Student’s t-distribution are applied to quantile forecasts such as Value-at-Risk and expected shortfall of financial returns as well as volatility forecasting. Parameter estimation and quantile forecasting are built on Bayesian Markov chain Monte Carlo sampling methods. Backtesting measures are presented for both Value-at-Risk and expected shortfall forecasts and employ two loss functions to assess volatility forecasts. Results taken from the S&P500 in the U.S. market with approximately 5-year out-of-sample periods covering the COVID-19 pandemic period are reported as follows: (1) The realized HAR-GARCH model performs best in respect of violation rates and expected shortfall at the 1% and 5% significance levels. (2) The realized EGARCH model performs best with regard to volatility forecasts.

What factors drive the satisfaction of citizens with governments' responses to COVID-19?

OBJECTIVES: This research scrutinizes the important factors influencing the satisfaction of citizens concerning their governments' responses to the COVID-19 pandemic based on an open-sourced survey of 14 countries. METHODS: To collect information on public sentiment regarding governments' reactions to COVID-19, we consider five factors for analysis: number of confirmed cases per million population, number of deaths per million population, and governments' containment and health policies, stringency policies, and economic support policies. We examine the Kendall correlations of variables in the 14 countries and use the wild bootstrap method for regression models to find important regressors. RESULTS: Our results show that people pay stronger attention to the results of their governments' battle against COVID-19 (number of confirmed cases and deaths per million population) rather than to what policies they initiate. Health policy and economic support do influence the approval of any national response to COVID-19. We also find that public satisfaction in Japan and South Korea toward the two governments' responses to the pandemic varies greatly compared to that of other countries' citizens to their governments' responses. CONCLUSIONS: The results herein offer some suggestions to governments when initiating policies to balance public health, livelihoods, and economic support.