Business

Publication Search Results

Now showing 1 - 10 of 27
  • (2023) Rojasavachai, Ravipa
    Thesis
    This thesis consists of three stand-alone studies in the field of energy finance market and household finance. The first study investigates the demographics and socio-economics factors influencing individuals’ decision to choose working from home before and after the coronavirus outbreak. I find that all factors impact the working from home choice before the coronavirus outbreak. However, the number of children becomes an uninfluenced factor, and female employees are more likely to work from home after the outbreak. Thus, I further report that female employees who work from home are less likely to have been promoted and received lower wages before the coronavirus outbreak. However, after the outbreak, they have a higher probability of job promotion and higher wage premium of working from home. This points to higher bargaining power with employers for females after the outbreak due to the changing in cultural norms and advanced working from home technologies. In the second study, I aim to analyse the impact of financial literacy on energy poverty in Australia and to explore important mechanisms of influence. The result shows that financial literate individual is less likely to face energy poverty. I also address the potential endogeneity by employing the mathematically skills as instrument variable. Additionally, I examine the channels that contribute to the relationship between financial literacy and energy poverty by focusing on the role of wealth and consumption. The result shows that wealth is the important channel through which financial literacy influences energy poverty while consumption could not classify as important channel. The third study investigates the macroeconomic effects of extreme weather in Australia by employing the index, Australian Actuaries Climate Index (AACI), to measure the extreme weather conditions. I find that the extreme weather shocks have a negatively impact on gross domestic product (GDP) and consumer price index (CPI). To provide better understanding of CPI, I analyse the CPI component and find that energy and food price are increased after extreme weather shock because higher energy demand for cooling and heating and lower agriculture output, respectively. In addition, the extreme weather shocks have the negative effect on interest rate while the positive impact on unemployment rate and energy consumption.

  • (2023) Arno, Akashlina
    Thesis
    The thesis consists of three chapters. The first two chapters explore the impact of environmental regulations on economic and environmental factors in emerging markets. The main challenge policymakers face in emerging markets is balancing economic growth while ensuring environmental sustainability. Chapter 1 examines the impact of environmental policies in India, focusing on the effect of the Corporate Responsibility for Environmental Protection (CREP) charter and Supreme Court Action Plans (SCAP). CREP targeted plants in a pre-determined set of industries known as the highly polluting industries (HPI). The SCAP was implemented in specific cities rather than industries. While CREP significantly improved investment in pollution control stock and productivity at the target plants, the effects of SCAP are mixed. Chapter 2 expands on the empirical analysis of chapter 1 by providing a theoretical model to capture the empirical findings from the Indian manufacturing sector. I present a theoretical model that describes how plants of different sizes invest in pollution control equipment when there could be a penalty associated with excess pollutant emissions. The model's findings show that the most productive plants assumed to be the largest, have the highest expenditure on clean capital. Chapter 3 is self-contained and concentrates on effectively tracking the changes in the emerging economies' output gap and growth. I employ a mixed-frequency Bayesian VAR approach to nowcast India's output gap and growth before and after the 2020-2021 recession caused by the Covid-19 pandemic. The model uses monthly indicators, which allows forecasting the effects of any significant macroeconomic shocks before the release of real GDP data for the quarter.

  • (2023) Wu, Yu
    Thesis
    The aim of this thesis is to review and statistically synthesize the state of research on the relationship between customer mistreatment and service employees’ affective and behavioral outcomes and to examine the spillover and spiraling mechanisms of resource losses. In study 1, I included 93 effect sizes of 80 independent samples from 70 primary studies (N = 24,708). I used a meta-analytic approach to conduct a quantitative review of the relationship between customer mistreatment and service employees’ affective and behavioral outcomes. Meta-regression was applied to explore the impact of contextual- level moderators (i.e., service provider type, mean sample age, percentage of female employees) on these relationships. Furthermore, I compared the effects of customer mistreatment with the effects of other work-related stressors (i.e., challenge-related stressors and hindrance-related stressors). The results show that customer mistreatment has a significant negative impact on service employees’ affective outcomes (i.e., reduced job satisfaction, reduced organizational commitment, and increased stress) and behavioral outcomes (i.e., increased emotional labor, increased surface acting, increased turnover intention, and increased work withdrawal). Additionally, the relationship between customer mistreatment and service employees’ organizational commitment is influenced by a contextual-level moderator (i.e., service provider type). Furthermore, the meta-analysis results show that the effect sizes between customer mistreatment and employee outcomes ranged from moderately small to moderately large. In study 2, adopting a dynamic perspective of resource loss, I examined the spillover mechanism between employees’ emotional exhaustion in the evening and their negative emotions the next morning. Moreover, I tested the spiraling mechanism from service employees’ emotional exhaustion the previous evening to their emotional exhaustion the next evening. The results show that the impact of customer mistreatment on employees’ evening emotional exhaustion spills over to the next day, which leads them to feel negative emotions in the morning. Furthermore, the impact of customer mistreatment on employees’ evening emotional exhaustion triggers their emotional exhaustion spirals, and their evening emotional exhaustion leads to more emotional exhaustion the next evening. The theoretical and practical implications of these findings are discussed.

  • (2023) Li, Yulong
    Thesis
    Mortality modelling and projection is important for actuarial science and significant amount of research has been done in this regard. In addition to the systematic uncertainty over time, mortality risk can also vary across individuals of the same age, a phenomenon known as mortality heterogeneity. In this thesis, I propose and establish a mortality model incorporating health heterogeneity, based on which I assess the positive impact of considering health effects on both government and individual retirement finances. I also incorporate systematic mortality by health state and extend the estimation of cohort mortality models to incomplete cohort data. In cohort mortality modelling, the traditional Kalman Filter Algorithm (KFA) uses the complete older cohort data while ignoring the recent incomplete cohort data. The calibrated mortality model can lead to unreliable mortality projections especially for long-term projections. In the first part of the thesis, I extend the traditional KFA by incorporating recent incomplete cohort data in the model fitting process, ensuring the mortality model projection is more accurate and better captures cohort mortality developments. Then in the second part, I propose and estimate a finite-state Markov Ageing Model (MAM) incorporating health heterogeneity, which is calibrated based on Australian cohort mortality and health condition data. This model better captures both the mortality and health developments for individuals with different initial health status. We capture the feature that healthier individuals are more likely to survive, while less healthy individuals are more likely to die based on the calibrated model. Allowing for health heterogeneity when make retirement planning is important because of these differences. In the last part, I provide an impact analysis of the MyRetirement system (CIPRs) from two perspectives: first, I use the multi-state health mortality model with which we consider both systematic mortality and health heterogeneity. Second, I add health-linked components (deferred health annuities) to the CIPRs' portfolio framework, aiming to provide health-linked income streams for retirees when their health costs increase significantly. I show how this extension is beneficial both to the government, in terms of Age Pension payments, and to retirees, in terms of retirement incomes.

  • (2023) Nguyen, Thi Minh Hang
    Thesis
    Variable annuities (VAs) are increasingly becoming popular insurance products in many developed countries which provide guaranteed forms of income depending on the performance of the equity market. Insurance companies often hold large VA portfolios and the associate valuation of such portfolios is a very time-consuming task. There have been several studies focusing on inventing techniques aimed at reducing the computational time including the selection of representative VA contracts and the use of a metamodel to estimate the values of all contracts in the portfolio. In this thesis, LASSO regression is used to select a set of representative scenarios after the representative contracts are chosen, which in turn allows for the set of representative contracts to expand without significant increase in computational load. The proposed approach leads to a remarkable improvement in the computational efficiency and accuracy of the metamodel. Stochastic reserving and calculation of capital requirement require VA providers to calculate risk measures such as Value at Risk and Conditional Tail Expectation. An emulation framework is proposed to calculate these risk measures by building a neural network to model the net liability of a VA contract at some given scenario. The surrogate model is faster at estimating net liability than the exact calculation. Efficiency is improved thanks to faster computing of net liability for any contract at any scenario in the Monte Carlo simulation. This approach can also be used to select scenarios where the estimated portfolio liabilities are in the top quantile. The true liabilities of the portfolio at these top-quantile scenarios can be computed which can then be used to compute the risk measures. This results in a reduction in computational time because the Monte Carlo method is performed on only a fraction of the original scenarios. As an equity-linked insurance products, VA is exposed to significant market risks due to the underlying assets in the mutual funds that its contributions are invested in. To hedge against these market risks, insurers need to construct a hedging portfolio consisting of the underlying assets whose hedge positions can be determined by the Greeks of the portfolio such as the partial dollar Deltas. For a large portfolio, the calculation of the Greeks using Monte Carlo simulation is very slow, so a metamodeling approach can be used to estimate the Greeks. Assuming that the mutual funds of the VA insurers is a mixture of major market indices, there is likely a dependence between the partial dollar Deltas of the portfolio on the market indices. This dependent relationship can be incorporated into the model using multi-output regression approaches and the resulting improvement in the effectiveness of the metamodel or the lack thereof will be studied in the thesis.

  • (2023) Keller, Elena
    Thesis
    Infertility affects 1 in 6 couples and >180 million people worldwide. It represents an increasingly important public health problem, amplified by the continuing global trend to later childbearing. Fertility treatment including in vitro fertilization (IVF) is not suited to traditional health technology assessment (HTA) methods, because its value is derived by its ability to create life, rather than extend, improve, or save existing lives. Consequently, there is a lack of guidance, and satisfactory HTA methods to determine whether fertility treatment provides good value for money. Moreover, the ever-increasing demand for elective egg freezing (EEF) to preserve female fertility poses additional challenges for economic assessments. This thesis describes 5 studies that move the research agenda forward for guiding the economic evaluation of fertility treatment. Study 1, a systematic review, identified and quantified 5 methodological categories for value-of-statistical-life elicitation. Based on these categories, Study 2 investigated methods for eliciting the value of a statistical baby (VSB) and concluded that discrete choice experiments (DCEs) are the most appropriate method in a fertility treatment context. Study 3 applied DCE outputs to derive a VSB estimate, which was used to assess value for money of publicly funded IVF in a cost-benefit analysis, finding that at least 5 IVF cycles likely provide good value for women <42 years. Study 4 elicited patient preferences for fertility treatment based on a DCE and Study 5 performed an incentive-compatible lab experiment to assess the impact of patient and treatment characteristics on the demand for IVF and EEF. Both experiments indicate that the demand for fertility treatment is price-inelastic and unresponsive to income level, which might explain why women continue fertility treatment once they have commenced despite their financial capacity. This research makes several methodological contributions and provides an evidence base to assess the public investment in fertility treatment. Overall, patients and society were found to value fertility treatment highly. New knowledge generated includes: (1) identifying the number of cost-beneficial IVF cycles by female age; (2) quantifying price and income elasticities for IVF and EEF; (3) bridging the gap between the proliferation of DCEs and policy by applying DCE outputs to HTA; and (4) demonstrating that government funding decisions can be explored in a lab experiment.

  • (2023) Boglioni Beaulieu, Guillaume
    Thesis
    Accurately capturing the dependence between risks, if it exists, is an increasingly relevant topic of actuarial research. In recent years, several authors have started to relax the traditional 'independence assumption', in a variety of actuarial settings. While it is known that 'mutual independence' between random variables is not equivalent to their 'pairwise independence', this thesis aims to provide a better understanding of the materiality of this difference. The distinction between mutual and pairwise independence matters because, in practice, dependence is often assessed via pairs only, e.g., through correlation matrices, rank-based measures of association, scatterplot matrices, heat-maps, etc. Using such pairwise methods, it is possible to miss some forms of dependence. In this thesis, we explore how material the difference between pairwise and mutual independence is, and from several angles. We provide relevant background and motivation for this thesis in Chapter 1, then conduct a literature review in Chapter 2. In Chapter 3, we focus on visualising the difference between pairwise and mutual independence. To do so, we propose a series of theoretical examples (some of them new) where random variables are pairwise independent but (mutually) dependent, in short, PIBD. We then develop new visualisation tools and use them to illustrate what PIBD variables can look like. We showcase that the dependence involved is possibly very strong. We also use our visualisation tools to identify subtle forms of dependence, which would otherwise be hard to detect. In Chapter 4, we review common dependence models (such has elliptical distributions and Archimedean copulas) used in actuarial science and show that they do not allow for the possibility of PIBD data. We also investigate concrete consequences of the 'nonequivalence' between pairwise and mutual independence. We establish that many results which hold for mutually independent variables do not hold under sole pairwise independent. Those include results about finite sums of random variables, extreme value theory and bootstrap methods. This part thus illustrates what can potentially 'go wrong' if one assumes mutual independence where only pairwise independence holds. Lastly, in Chapters 5 and 6, we investigate the question of what happens for PIBD variables 'in the limit', i.e., when the sample size goes to infi nity. We want to see if the 'problems' caused by dependence vanish for sufficiently large samples. This is a broad question, and we concentrate on the important classical Central Limit Theorem (CLT), for which we fi nd that the answer is largely negative. In particular, we construct new sequences of PIBD variables (with arbitrary margins) for which a CLT does not hold. We derive explicitly the asymptotic distribution of the standardised mean of our sequences, which allows us to illustrate the extent of the 'failure' of a CLT for PIBD variables. We also propose a general methodology to construct dependent K-tuplewise independent (K an arbitrary integer) sequences of random variables with arbitrary margins. In the case K = 3, we use this methodology to derive explicit examples of triplewise independent sequences for which no CLT hold. Those results illustrate that mutual independence is a crucial assumption within CLTs, and that having larger samples is not always a viable solution to the problem of non-independent data.

  • (2023) Wang, Haoxu
    Thesis
    My thesis consists of three essays. My first essay is on relative strength anomalies. After long being one of the main puzzles in asset pricing, momentum has ironically become a case of observational equivalence. It can now be explained both by behavioral factors capturing mispricing and by the neoclassical-inspired investment q-factors. Besides, q-factors explain the related 52-week-high anomaly. We note that recent tests subsuming both anomalies are unconditional exercises while the bulk of momentum profits are predictable and occur in bull markets and after periods of low volatility. Comparing asset pricing models conditionally, we find the unconditional fit is misleading. The models fit well most of the time but not when the profits are produced. Noticeably, q-theory implies time-varying loadings that are not consistent with the data. On the other hand, consistent with an underreaction channel, earnings announcement returns and analyst forecast errors both decrease steeply with lagged volatility. My second essay is on portfolio optimization. We comprehensively examine whether advances in the asset-pricing and covariance matrix literatures can jointly improve the out-of-sample (OOS) performance of mean-variance efficient (MVE) portfolios. Focusing on the 500 largest stocks, we find that, after accounting for transaction costs, MVE portfolios formed using improved inputs do not outperform the passive strategy. However, their after-cost performance can be substantially improved by combining several ideas available in the literature. Portfolios that simultaneously target risk, manage transaction costs, correct the covariance matrix for OOS errors, and use simple linear Fama-MacBeth return forecasts attain net Sharpe ratios greater than one, significantly outperforming the passive portfolio. My third essay is on factor momentum. Factor momentum recently joined the ongoing debate over the causes of stock momentum. We find that neither momentum in “off-the-shelf” factors nor momentum in high-eigenvalue principal component factors can explain any previously proposed momentum driver. Also, compared to previous drivers, factor momentum does not exhibit superior performance in capturing momentum-like anomalies. Like the competing models, it cannot explain stock momentum conditionally. Moreover, it cannot explain stock momentum after accounting for transaction costs while these can explain the persistence of factor momentum, especially in less systematic factors.

  • (2023) Wang, Jingyi
    Thesis
    This dissertation extends knowledge of heterogeneous reference groups for organizational social comparisons. Addressing research opportunities regarding improving our understanding towards organizational reference groups in the contemporary behavioral theory of the firm literature (the behavioral literature for short), this dissertation explores two research questions regarding both the behavioral (internal) and structural (external) antecedents of organizational reference group configurations – defined by the performance range and industry diversity of the organizations in reference groups. Drawing upon the behavioral literature, the social network literature, and the social comparison theory literature, I explore how organizational historical performance feedback and interorganizational networks interact to activate different types of social comparison motives and influence reference group configurations. First, I theorize the effects from recent historical performance feedback on reference group configurations. Second, I examine how the fluctuation of historical performance feedback over time interacts with recent historical performance feedback to influence reference group configurations. Third, I examine how interorganizational network features interact with recent performance feedback to influence reference group configurations. Network diversity and network status are examined to capture both the information flow and the social hierarchy aspects of interorganizational networks. Main effects of network diversity and network status on organizational reference group configurations are also investigated in this dissertation to provide a more complete understanding about the antecedents of heterogeneous organizational reference groups. To test the hypotheses, I construct two panel datasets of 1,157 and 332 US public firms from 2006 to 2015 based on the ISS Incentive Lab, BoardEx, and Compustat North America databases. Fixed effects models are applied as analytic methods. The results support the hypotheses that the fluctuation of historical performance feedback weakens the influence of positive historical performance feedback on reference group configurations (both the performance range and industry diversity), and network status weakens the influence of negative historical performance feedback on the industry diversity of reference groups. The results also demonstrate an unexpected weakening effect of network diversity on the relationship between negative historical performance feedback and the performance range of reference groups, which is opposite to my hypothesis. By investigating the antecedents of reference group configurations, my dissertation contributes to the behavioral literature in four primary ways. First, this dissertation takes initial steps to examine specific organizational reference group configurations and contributes to knowledge on the heterogeneity of reference groups across organizations. Second, this dissertation demonstrates the role played by history/time and positive historical performance feedback in the process of reference group selection, and provides a more complete model of how historical performance feedback influences reference groups used for social comparisons. Third, this dissertation investigates interorganizational networks as contingent external antecedents of reference group configurations and enriches our understanding of organizational social comparisons. Fourth, this dissertation makes an empirical contribution through using panel datasets with information of reference groups actually used by organizations to test hypotheses.

  • (2023) Han, Miaodi
    Thesis
    This thesis examines whether non-client companies’ online disclosures serve as an external information source for auditors and whether auditors’ searches for such information facilitate their audit process and influence the outcomes in various important corporate event settings, including regulatory reviews by the U.S. Securities and Exchange Commission (SEC) and mergers and acquisitions (M&A). Specifically, I exploit the clickstream data from the SEC’s EDGAR system, which allows me to track audit firms’ access to public firms’ electronic filings to investigate the effects of auditors’ information searches on the outcomes of regulatory reviews and M&A. The first study examines the usefulness of non-client industry peer companies’ financial filings for auditors to resolve the SEC’s review process on their clients. I hypothesize and find that audit firms can facilitate the resolution of the SEC’s comment letters for their clients through the disclosure benchmarking strategy against their client’s industry peers. Further analyses suggest that this benchmarking effect on the resolution of the regulatory process operates through two economic channels related to regulatory risk and information endowment. In addition, I find a positive association between the auditor’s disclosure benchmarking of peer firms in the regulatory process and their client’s future financial reporting quality. These findings highlight the usefulness of industry peer firms’ filing information to auditors in the SEC review process and provide significant implications for managers, regulators, and auditors with regard to the review process. The second study investigates the effect of information search effort by the acquirer’s auditor on post-M&A audit quality. I predict and find greater M&A-related information search effort by the acquirer’s auditor through accessing the target firm’s filings is associated with better post-acquisition audit quality. This effect is more pronounced when the acquirer’s auditor has less M&A experience, when the target firm has a lower analyst following, and when the deal value is greater. Further evidence also shows the auditor information search effort is associated with better audit efficiency. These findings inform managers, audit firms, and other stakeholders by showing that the information search effort on the target firm’s filings by the acquirer’s auditor improves post-M&A audit quality, which has been an issue of concern to these stakeholders.