Economics - Research Publications
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ItemReplication: Belief elicitation with quadratic and binarized scoring rules(Elsevier, 2020-12-01)Researchers increasingly elicit beliefs to understand the underlying motivations of decision makers. Two commonly used methods are the quadratic scoring rule (QSR) and the binarized scoring rule (BSR). Hossain and Okui (2013) use a within-subject design to evaluate the performance of these two methods in an environment where subjects report probabilistic beliefs over binary outcomes with objective probabilities. In a near replication of their study, we show that their results continue to hold with a between-subject design. This is an important validation of the BSR given that researchers typically implement only one method to elicit beliefs. In favor of the BSR, reported beliefs are less accurate under the QSR than the BSR. Consistent with theoretical predictions, risk-averse subjects distort their reported beliefs under the QSR.
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ItemImpacts of graduated driver licensing regulations(Elsevier, 2020-05-01)We evaluate the impact of the Graduated Driver Licensing (GDL) system introduced in Victoria, Australia as they influence both injury and fatality rates. Since 1990, the Victorian GDL scheme has undergone several modifications including the introduction of new requirements and the stricter enforcement of existing regulations. Our evaluation of the GDL is based on monthly mortality and morbidity data for drivers 18–25 for the period January 2000 to June 2017. We estimate the immediate and long-term impacts of each policy change to the GDL system. Our results indicate that several initiatives in the GDL system have had impacts on both fatalities and injuries requiring hospitalisation when differentiated by gender. In a number of cases we observe that reactions to these measures are common to both genders. These include: the signalling of the proposed GDL changes in the media, the introduction of an extra probationary year for those under 21, the total alcohol ban for the entire probationary period, and limits on peer passengers for the first year. Stricter mobile phone restrictions appear to have had no impact on injuries for either males or females although they were associated with lower fatality rates for both. In addition, we found an indication that in the period prior to the introduction of the mandatory requirement of 120 h supervised driving, there was a rise in male driver injuries possibly caused by a rush of more inexperienced learners to obtain their probationary licence.
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ItemOptimal consumption and investment strategies with liquidity risk and lifetime uncertainty for Markov regime-switching jump diffusion models(Elsevier, 2020-02-01)In this paper, we consider the optimal consumption and investment strategies for households throughout their lifetime. Risks such as the illiquidity of assets, abrupt changes of market states, and lifetime uncertainty are considered. Taking the effects of heritage into account, investors are willing to limit their current consumption in exchange for greater wealth at their death, because they can take advantage of the higher expected returns of illiquid assets. Further, we model the liquidity risks in an illiquid market state by introducing frozen periods with uncertain lengths, during which investors cannot continuously rebalance their portfolios between different types of assets. In liquid market, investors can continuously remix their investment portfolios. In addition, a Markov regime-switching process is introduced to describe the changes in the market's states. Jumps, classified as either moderate or severe, are jointly investigated with liquidity risks. Explicit forms of the optimal consumption and investment strategies are developed using the dynamic programming principle. Markov chain approximation methods are adopted to obtain the value function. Numerical examples demonstrate that the liquidity of assets and market states have significant effects on optimal consumption and investment strategies in various scenarios.
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ItemAssociation of maternal depression and home adversities with infant hypothalamic-pituitary-adrenal (HPA) axis biomarkers in rural Pakistan(Elsevier, 2020-11-01)Background: Each year, almost 35% of children are exposed to maternal depression and more grow up in persistent poverty, increasing the risk for stress-related disease and other socio-developmental deficits later in life. These impacts are likely related to chronic stress via the hypothalamic-pituitary-adrenal (HPA) axis. However, there is little evidence relating early windows of child HPA axis activity to multiple exposures. Methods: We investigated chronic measures of hair-derived HPA axis hormones (cortisol and dehydroepiandrosterone (DHEA)) in 104 one-year old infants from rural Pakistan and longitudinal measures of maternal depression, intimate partner violence (IPV), socio-economic status (SES), and the home environment. Results: Estimates from adjusted linear mixed effects models did not reveal consistent significant associations between infant cortisol and maternal depression or home adversities. By contrast, infants exposed to maternal depression during pregnancy had lower DHEA levels (ß= -0.18 95% confidence interval [CI]: -0.34, -0.02) as did those whose mothers experienced multiple types of IPV (ß=-4.14 95% CI: -7.42, -0.79) within one year postpartum. Higher SES had a significant positive association with infant DHEA levels (ß= 0.77 95% CI: 0.08, 1.47). Depression severity and chronicity at one year postpartum had near significant associations with infant DHEA. Measures of home environment had no observable impacts on infant HPA axis activity. Limitations: Limitations include the modest sample size and aggregation of hair samples for analysis. Conclusion: Results point to possible early HPA axis dysregulation driven by changes in DHEA activity, but not cortisol at one year of age. Findings contribute to growing research examining intergenerational transmissions of maternal depression, IPV, and household environment on infant stress-response systems.
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ItemA Survey of the Individual Claim Size and Other Risk Factors Using Credibility Bonus-Malus Premiums(MDPI AG, 2020-02-01)In this paper, a flexible count regression model based on a bivariate compound Poisson distribution is introduced in order to distinguish between different types of claims according to the claim size. Furthermore, it allows us to analyse the factors that affect the number of claims above and below a given claim size threshold in an automobile insurance portfolio. Relevant properties of this model are given. Next, a mixed regression model is derived to compute credibility bonus-malus premiums based on the individual claim size and other risk factors such as gender, type of vehicle, driving area, or age of the vehicle. Results are illustrated by using a well-known automobile insurance portfolio dataset.
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ItemA portfolio policy package to reduce greenhouse gas emissions(MDPI AG, 2020-04-01)Arguments for a portfolio of price, regulation and subsidy policy interventions to reduce the production and consumption of greenhouse gas emissions are presented. The operation and effects of each intervention are described and compared. A combination of different sets of market failures across the many potential decision changes available to producers and consumers to reduce emissions and different properties of the mitigation instruments support a portfolio approach to reduce emissions at a low cost.
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ItemContinuous-time mean-variance asset-liability management with stochastic interest rates and inflation risks(American Institute of Mathematical Sciences, 2020-03-01)This paper investigates a continuous-time Markowitz mean-variance asset-liability management (ALM) problem under stochastic interest rates and inflation risks. We assume that the company can invest in n +1assets: one risk-free bond and n risky stocks. The risky stock's price is governed by a geometric Brownian motion (GBM), and the uncontrollable liability follows a Brownian motion with drift, respectively. The correlation between the risky assets and the liability is considered. The objective is to minimize the risk (measured by variance) of the terminal wealth subject to a given expected terminal wealth level. By applying the Lagrange multiplier method and stochastic control approach, we derive the associated Hamilton-Jacobi-Bellman (HJB) equation, which can be converted into six partial differential equations (PDEs). The closed-form solutions for these six PDEs are derived by using the homogenization approach and the variable transformation technique. Then the closed-form expressions for the efficient strategy and efficient frontier are obtained. In addition, a numerical example is presented to illustrate the results.
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ItemContinuous-time mean-variance portfolio selection with no-shorting constraints and regime-switching(American Institute of Mathematical Sciences, 2020-03-01)The present article investigates a continuous-time mean-variance portfolio selection problem with regime-switching under the constraint of no-shorting. The literature along this line is essentially dominated by the Hamilton-Jacobi-Bellman (HJB) equation approach. However, in the presence of switching regimes, a system of HJB equations rather than a single equation need to be tackled concurrently, which might not be solvable in terms of classical solutions, or even not in the weaker viscosity sense as well. Instead, we first introduce a general result on the sign of geometric Brownian motion with jumps, then derive the efficient portfolio and frontier via the maximum principle approach; in particular, we observe, under a mild technical assumption on the initial conditions, that the no-shorting constraint will consistently be satisfied over the whole finite time horizon. Further numerical illustrations will be provided.
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ItemNo Preview AvailableThe global effects of Covid-19-induced uncertainty(Elsevier, 2020-09-01)We estimate a VAR with world-level variables to simulate the effects of the Covid-19 outbreak-related uncertainty shock. We find a peak (cumulative over one year) negative response of world output of 1.6% (14%).
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ItemBayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity(Elsevier, 2020-04-01)In this study, Bayesian inference is developed for structural vector autoregressive models in which the structural parameters are identified via Markov-switching heteroskedasticity. In such a model, restrictions that are just-identifying in the homoskedastic case, become over-identifying and can be tested. A set of parametric restrictions is derived under which the structural matrix is globally or partially identified and a Savage–Dickey density ratio is used to assess the validity of the identification conditions. The latter is facilitated by analytical derivations that make the computations feasible and numerical standard errors small. As an empirical example, monetary models are compared using heteroskedasticity as an additional device for identification. The empirical results support an identified interest rate reaction function with money.