| 1. |
- Chang, K. -C, et al.
(författare)
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Psychometric Testing of Three COVID-19-Related Scales Among People with Mental Illness
- 2022
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Ingår i: International Journal of Mental Health and Addiction. - : Springer. - 1557-1874 .- 1557-1882. ; 20, s. 324-336
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Tidskriftsartikel (refereegranskat)abstract
- Fear of novel coronavirus 2019 (COVID-19) may result in psychological health problems among different populations. Moreover, believing COVID-19 information and preventive COVID-19 infection behaviors are relevant constructs associated with fear of COVID-19. Therefore, the present study validated three instruments assessing fear, beliefs, and preventive behaviors related to COVID-19 among individuals with mental illness. Moreover, relationships between the three constructs were examined. Individuals with mental illness (N = 400; 178 females; mean age = 46.91 years) completed the Fear of COVID-19 Scale (FCV-19S), Believing COVID-19 Information Scale (BCIS), Preventive COVID-19 Infection Behaviors Scale (PCIBS), and Depression Anxiety Stress Scale-21 (DASS-21). The FCV-19S, BCIS, and PCIBS demonstrated a single-factor structure with satisfactory fit indices. Moreover, believing COVID-19 information positively and significantly associated with fear of COVID-19, and fear of COVID-19 negatively and significantly associated with preventive behaviors and positively and significantly associated with psychological distress. The FCV-19S, BCIS, and PCIBS may assist healthcare providers in assessing COVID-19-related information among individuals with mental illness. Consequently, relevant programs may be designed to help individuals with mental illness going through the period of crisis.
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| 2. |
- Cross, Jamie L., et al.
(författare)
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Large stochastic volatility in mean VARs
- 2023
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Ingår i: Journal of Econometrics. - : Elsevier. - 0304-4076 .- 1872-6895. ; 236:1
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Tidskriftsartikel (refereegranskat)abstract
- Bayesian vector autoregressions with stochastic volatility in both the conditional mean and variance (SVMVARs) are widely used for studying the macroeconomic effects of uncertainty. Despite their popularity, intensive computational demands when estimating such models has constrained researchers to specifying a small number of latent volatilities, and made out-of-sample forecasting exercises impractical. In this paper, we propose an efficient Markov chain Monte Carlo (MCMC) algorithm that facilitates timely posterior and predictive inference with large SVMVARs. In a simulation exercise, we show that the new algorithm is significantly faster than the state-of-the-art particle Gibbs with ancestor sampling algorithm, and exhibits superior mixing properties. In two applications, we show that large SVMVARs are generally useful for structural analysis and out-of-sample forecasting, and are especially useful in periods of high uncertainty such as the Great Recession and the COVID-19 pandemic.
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| 3. |
- Cross, Jamie L., et al.
(författare)
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Macroeconomic forecasting with large Bayesian VARs : Global-local priors and the illusion of sparsity
- 2020
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 36:3, s. 899-915
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Tidskriftsartikel (refereegranskat)abstract
- A class of global-local hierarchical shrinkage priors for estimating large Bayesian vector autoregressions (BVARs) has recently been proposed. We question whether three such priors: Dirichlet-Laplace, Horseshoe, and Normal-Gamma, can systematically improve the forecast accuracy of two commonly used benchmarks (the hierarchical Minnesota prior and the stochastic search variable selection (SSVS) prior), when predicting key macroeconomic variables. Using small and large data sets, both point and density forecasts suggest that the answer is no. Instead, our results indicate that a hierarchical Minnesota prior remains a solid practical choice when forecasting macroeconomic variables. In light of existing optimality results, a possible explanation for our finding is that macroeconomic data is not sparse, but instead dense.
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| 4. |
- Hou, Ai Jun, et al.
(författare)
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VIX Futures Calendar Spreads
- 2018
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Ingår i: Journal of futures markets. - : Wiley. - 0270-7314 .- 1096-9934. ; 38:7, s. 822-838
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Tidskriftsartikel (refereegranskat)abstract
- A VIX futures calendar spread involves buying a futures contract maturing in 1 month and selling another one maturing in a different month. VIX futures calendar spreads represent a daily turnover above 500 million dollars, or roughly 20% of the total VIX futures trading volume. Speculation, rather than information about changes in the slope of the volatility term structure, is the main driving force behind calendar spread trades. On average, a calendar spread costs a little less than $100 (about 15 basis points). If settled at the end of the trading day, 43% of the calendar spreads are profitable.
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