Climate change legislation is financially and politically costly. Financial markets have the capacity to encourage companies to do more than what is required by law (i.e. overcomply), and this could lead to socially optimal outcomes without the costs. First, I examine how the responses of Canadian companies to a voluntary survey regarding carbon emission levels affect those companies' valuations. I employ a signaling framework where companies choose between two signals - disclosure and nondisclosure - and where investors are uncertain about the likelihood of legislation in addition to company type. I test the prediction of the model that disclosure increases company value only when investors believe legislation is likely. I find that withholding emissions information resulted in average daily abnormal returns of 3 basis points, and that disclosure resulted in average daily abnormal returns of -11 basis points in the days surrounding the submission of survey responses. The level of emissions disclosed is found to be irrelevant. Second, I examine the credibility of green legislative threat. The economic climate impacts the government's ability to credibly threaten new environmental law, and so I model a company's pollution decision as a function of the economic climate. In times of recession, companies may choose to pollute heavily since they believe that the likelihood of legislation is low. As a first step in evaluating the model empirically, I use differences-in-differences regressions to estimate the effect of legislative threat during recession on company value. Although the value of carbon-intensive companies decreased initially in reaction to legislative threat, the relative value of these companies increased as the depth of the recession becomes more apparent. I find that on average the legislative threat of an emission trading scheme reduced Tobin's Q by 18% in the initial stages of the recession, but as the recession deepened the legislative threat effect was eliminated. My results suggest that financial markets combined with a credible threat of legislation could provide encouragement to companies to overcomply with current regulations, possibly to the extent that is socially optimal. More research on factors affecting company carbon emissions levels and intensity is required.

Climate change legislation is financially and politically costly. Financial markets have the capacity to encourage companies to do more than what is required by law (i.e. overcomply), and this could lead to socially optimal outcomes without the costs. First, I examine how the responses of Canadian companies to a voluntary survey regarding carbon emission levels affect those companies' valuations. I employ a signaling framework where companies choose between two signals - disclosure and nondisclosure - and where investors are uncertain about the likelihood of legislation in addition to company type. I test the prediction of the model that disclosure increases company value only when investors believe legislation is likely. I find that withholding emissions information resulted in average daily abnormal returns of 3 basis points, and that disclosure resulted in average daily abnormal returns of -11 basis points in the days surrounding the submission of survey responses. The level of emissions disclosed is found to be irrelevant. Second, I examine the credibility of green legislative threat. The economic climate impacts the government's ability to credibly threaten new environmental law, and so I model a company's pollution decision as a function of the economic climate. In times of recession, companies may choose to pollute heavily since they believe that the likelihood of legislation is low. As a first step in evaluating the model empirically, I use differences-in-differences regressions to estimate the effect of legislative threat during recession on company value. Although the value of carbon-intensive companies decreased initially in reaction to legislative threat, the relative value of these companies increased as the depth of the recession becomes more apparent. I find that on average the legislative threat of an emission trading scheme reduced Tobin's Q by 18% in the initial stages of the recession, but as the recession deepened the legislative threat effect was eliminated. My results suggest that financial markets combined with a credible threat of legislation could provide encouragement to companies to overcomply with current regulations, possibly to the extent that is socially optimal. More research on factors affecting company carbon emissions levels and intensity is required.

Knowledge of the investment risk of investment-grade commercial real estate (‘ICRE’) is important because it determines the approaches which should be taken to portfolio management. However, relatively little is known about this risk. This research expands the body of knowledge of ICRE investment risk by producing conclusions about the information content of prices and the distribution of returns in the ICRE context. It is broken into three main parts. First, the ICRE returns-generating process is characterised to form a basis for deducing theoretical conclusions about the information content of prices and the stochastic attributes of returns. The rationale for this approach lies in capital markets literature, which demonstrates that the characteristics of the information structure of markets, the decision-making processes of investors and the market trading mechanism determine the main attributes of the process of price evolution (which is assumed to be the main driver of returns). The analysis concludes that ICRE prices are partially informed, and changes in prices are described by a ‘jump’ process. Second, analysis of a database of ‘large’ price changes supplied by the Property Council of Australia is undertaken to empirically test the jump process hypothesis. This analysis provides evidence that natural events associated with changes in the leasing structure of properties are a primary driver of relatively large, infrequent dislocations in valuation-based prices. With parts one and two as a backdrop, the third part of this research empirically tests a discrete mixture of normals (‘DMON’) model of investment risk. Capital markets research shows that a DMON model flows naturally from jump price processes. DMON models fitted to cross-sectional returns on individual properties supplied by the PCA are found to be superior to the normal and stable Paretian models previously proposed by other researchers. In aggregate these conclusions have serious implications for the management of ICRE portfolios, and suggest a need for additional research. Some implications include: (1) Mean-lower partial variance is superior to mean-variance optimisation. (2) Forecasting the distribution of ICRE returns forms a new tool for active management. (3) Passive portfolio management is inappropriate. (4) Comparables-based valuations may be unreliable for investment decisions. / Subscription resource available via Digital Dissertations only.

Knowledge of the investment risk of investment-grade commercial real estate (‘ICRE’) is important because it determines the approaches which should be taken to portfolio management. However, relatively little is known about this risk. This research expands the body of knowledge of ICRE investment risk by producing conclusions about the information content of prices and the distribution of returns in the ICRE context. It is broken into three main parts. First, the ICRE returns-generating process is characterised to form a basis for deducing theoretical conclusions about the information content of prices and the stochastic attributes of returns. The rationale for this approach lies in capital markets literature, which demonstrates that the characteristics of the information structure of markets, the decision-making processes of investors and the market trading mechanism determine the main attributes of the process of price evolution (which is assumed to be the main driver of returns). The analysis concludes that ICRE prices are partially informed, and changes in prices are described by a ‘jump’ process. Second, analysis of a database of ‘large’ price changes supplied by the Property Council of Australia is undertaken to empirically test the jump process hypothesis. This analysis provides evidence that natural events associated with changes in the leasing structure of properties are a primary driver of relatively large, infrequent dislocations in valuation-based prices. With parts one and two as a backdrop, the third part of this research empirically tests a discrete mixture of normals (‘DMON’) model of investment risk. Capital markets research shows that a DMON model flows naturally from jump price processes. DMON models fitted to cross-sectional returns on individual properties supplied by the PCA are found to be superior to the normal and stable Paretian models previously proposed by other researchers. In aggregate these conclusions have serious implications for the management of ICRE portfolios, and suggest a need for additional research. Some implications include: (1) Mean-lower partial variance is superior to mean-variance optimisation. (2) Forecasting the distribution of ICRE returns forms a new tool for active management. (3) Passive portfolio management is inappropriate. (4) Comparables-based valuations may be unreliable for investment decisions. / Subscription resource available via Digital Dissertations only.

Knowledge of the investment risk of investment-grade commercial real estate (‘ICRE’) is important because it determines the approaches which should be taken to portfolio management. However, relatively little is known about this risk. This research expands the body of knowledge of ICRE investment risk by producing conclusions about the information content of prices and the distribution of returns in the ICRE context. It is broken into three main parts. First, the ICRE returns-generating process is characterised to form a basis for deducing theoretical conclusions about the information content of prices and the stochastic attributes of returns. The rationale for this approach lies in capital markets literature, which demonstrates that the characteristics of the information structure of markets, the decision-making processes of investors and the market trading mechanism determine the main attributes of the process of price evolution (which is assumed to be the main driver of returns). The analysis concludes that ICRE prices are partially informed, and changes in prices are described by a ‘jump’ process. Second, analysis of a database of ‘large’ price changes supplied by the Property Council of Australia is undertaken to empirically test the jump process hypothesis. This analysis provides evidence that natural events associated with changes in the leasing structure of properties are a primary driver of relatively large, infrequent dislocations in valuation-based prices. With parts one and two as a backdrop, the third part of this research empirically tests a discrete mixture of normals (‘DMON’) model of investment risk. Capital markets research shows that a DMON model flows naturally from jump price processes. DMON models fitted to cross-sectional returns on individual properties supplied by the PCA are found to be superior to the normal and stable Paretian models previously proposed by other researchers. In aggregate these conclusions have serious implications for the management of ICRE portfolios, and suggest a need for additional research. Some implications include: (1) Mean-lower partial variance is superior to mean-variance optimisation. (2) Forecasting the distribution of ICRE returns forms a new tool for active management. (3) Passive portfolio management is inappropriate. (4) Comparables-based valuations may be unreliable for investment decisions. / Subscription resource available via Digital Dissertations only.

Knowledge of the investment risk of investment-grade commercial real estate (‘ICRE’) is important because it determines the approaches which should be taken to portfolio management. However, relatively little is known about this risk. This research expands the body of knowledge of ICRE investment risk by producing conclusions about the information content of prices and the distribution of returns in the ICRE context. It is broken into three main parts. First, the ICRE returns-generating process is characterised to form a basis for deducing theoretical conclusions about the information content of prices and the stochastic attributes of returns. The rationale for this approach lies in capital markets literature, which demonstrates that the characteristics of the information structure of markets, the decision-making processes of investors and the market trading mechanism determine the main attributes of the process of price evolution (which is assumed to be the main driver of returns). The analysis concludes that ICRE prices are partially informed, and changes in prices are described by a ‘jump’ process. Second, analysis of a database of ‘large’ price changes supplied by the Property Council of Australia is undertaken to empirically test the jump process hypothesis. This analysis provides evidence that natural events associated with changes in the leasing structure of properties are a primary driver of relatively large, infrequent dislocations in valuation-based prices. With parts one and two as a backdrop, the third part of this research empirically tests a discrete mixture of normals (‘DMON’) model of investment risk. Capital markets research shows that a DMON model flows naturally from jump price processes. DMON models fitted to cross-sectional returns on individual properties supplied by the PCA are found to be superior to the normal and stable Paretian models previously proposed by other researchers. In aggregate these conclusions have serious implications for the management of ICRE portfolios, and suggest a need for additional research. Some implications include: (1) Mean-lower partial variance is superior to mean-variance optimisation. (2) Forecasting the distribution of ICRE returns forms a new tool for active management. (3) Passive portfolio management is inappropriate. (4) Comparables-based valuations may be unreliable for investment decisions. / Subscription resource available via Digital Dissertations only.

Knowledge of the investment risk of investment-grade commercial real estate (‘ICRE’) is important because it determines the approaches which should be taken to portfolio management. However, relatively little is known about this risk. This research expands the body of knowledge of ICRE investment risk by producing conclusions about the information content of prices and the distribution of returns in the ICRE context. It is broken into three main parts. First, the ICRE returns-generating process is characterised to form a basis for deducing theoretical conclusions about the information content of prices and the stochastic attributes of returns. The rationale for this approach lies in capital markets literature, which demonstrates that the characteristics of the information structure of markets, the decision-making processes of investors and the market trading mechanism determine the main attributes of the process of price evolution (which is assumed to be the main driver of returns). The analysis concludes that ICRE prices are partially informed, and changes in prices are described by a ‘jump’ process. Second, analysis of a database of ‘large’ price changes supplied by the Property Council of Australia is undertaken to empirically test the jump process hypothesis. This analysis provides evidence that natural events associated with changes in the leasing structure of properties are a primary driver of relatively large, infrequent dislocations in valuation-based prices. With parts one and two as a backdrop, the third part of this research empirically tests a discrete mixture of normals (‘DMON’) model of investment risk. Capital markets research shows that a DMON model flows naturally from jump price processes. DMON models fitted to cross-sectional returns on individual properties supplied by the PCA are found to be superior to the normal and stable Paretian models previously proposed by other researchers. In aggregate these conclusions have serious implications for the management of ICRE portfolios, and suggest a need for additional research. Some implications include: (1) Mean-lower partial variance is superior to mean-variance optimisation. (2) Forecasting the distribution of ICRE returns forms a new tool for active management. (3) Passive portfolio management is inappropriate. (4) Comparables-based valuations may be unreliable for investment decisions. / Subscription resource available via Digital Dissertations only.

In this thesis, we try to provide a broad econometric analysis of a class of risk measures, distortion risk measures (DRM). With carefully selected functional form, the Value-at-Risk (VaR) and Tail-VaR (TVaR) are special cases of DRMs. Besides, the DRM also admits interpretation in the sense of non-expected utility type of preferences. We first provide a unified statistical framework for the nonparametric estimators of the DRMs in a univariate case. The asymptotic properties of both the DRMs and their sensitivities with respect to the parameters representing risk aversion and/or pessimism are derived. Moreover, the relationships between the VaR and TVaR are also investigated in detail, which, we hope, can shed new lights on the way passing one risk measure to another. Then, the analysis of DRMs are extended to a multi-dimensional framework, where the DRM is computed for a portfolio consisting of many primitive assets. Analogous to the mean-variance frontier analysis, we study the efficient portfolio frontier when both objective and constraint are replaced by the DRMs. We call this the DRM-DRM framework. Under a nonparametric setting, we propose three asymptotic test statistics for evaluating the efficiency of a given portfolio. Finally, we discuss the criteria used for evaluating models used to forecast the VaRs. More precisely, we propose a criterion which takes into account the loss levels beyond the VaRs.

The essays empirically show the impact of investors speculation and disagreements on the returns and trading volume of securities. The results also shed light on the central issues of price formation and investors’ trading motives in security markets. The first essay investigates whether the trading activities of retail investors affect option prices through volatility speculation. This essay empirically shows that higher retail trading proportions are related to lower delta-hedged option returns. The phenomenon is more pronounced before earnings announcements and among stocks with more time-varying and positively skewed volatility. The results suggest that retail investors speculate and pay a lottery premium on the expected future volatility, resulting in more expensive options in terms of higher implied volatilities. This systematic deviation of option-implied volatility from realized volatility suggests retail investor clientele as a behavioral-based driving force of volatility risk premium. The second essay investigates the motive of option trading. It is shown that option trading is mostly driven by differences of opinion, a finding different from the current literature that attempts to attribute option trading to information asymmetry. First, option trading around earnings announcements is speculative in nature and mostly dominated by small, retail investors. Second, around earnings announcements, option turnovers do not predict stock returns, once prior stock returns are controlled for. Third, regression results reveal that option trading is also significantly explained by differences of opinion at ordinary times. While informed trading is present in stocks, it is not detected in options. The third essay provides strong evidence of reduction in informational efficiency when there are short-sale constraints and disagreements. Post earnings announcement returns are found to be significantly lower for stocks with more dispersed opinions and stocks that are exogenously short-sale prohibited by the Hong Kong Stock Exchange, supporting Miller’s (1977) overvaluation hypothesis. The results also suggest short-sale constraint as an explanation to negative post earnings announcement drift.

In this thesis, we try to provide a broad econometric analysis of a class of risk measures, distortion risk measures (DRM). With carefully selected functional form, the Value-at-Risk (VaR) and Tail-VaR (TVaR) are special cases of DRMs. Besides, the DRM also admits interpretation in the sense of non-expected utility type of preferences. We first provide a unified statistical framework for the nonparametric estimators of the DRMs in a univariate case. The asymptotic properties of both the DRMs and their sensitivities with respect to the parameters representing risk aversion and/or pessimism are derived. Moreover, the relationships between the VaR and TVaR are also investigated in detail, which, we hope, can shed new lights on the way passing one risk measure to another. Then, the analysis of DRMs are extended to a multi-dimensional framework, where the DRM is computed for a portfolio consisting of many primitive assets. Analogous to the mean-variance frontier analysis, we study the efficient portfolio frontier when both objective and constraint are replaced by the DRMs. We call this the DRM-DRM framework. Under a nonparametric setting, we propose three asymptotic test statistics for evaluating the efficiency of a given portfolio. Finally, we discuss the criteria used for evaluating models used to forecast the VaRs. More precisely, we propose a criterion which takes into account the loss levels beyond the VaRs.