SelectedWorks @ Melbourne Business School (The University of Melbourne)
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713 research outputs found
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Inversion Copulas from Nonlinear State Space Models
No full textWhile copulas constructed from inverting latent elliptical, or skew-elliptical, distributions are popular, they can be inadequate models of serial dependence in time series. As an alternative, we propose an approach to construct copulas from the inversion of latent nonlinear state space models. This allows for new time series copula models that have the same serial dependence structure as a state space model, yet have an arbitrary marginal distribution - something that is difficult to achieve using other time series models. We examine the time series properties of the copula models, outline measures of serial dependence, and show how to use likelihood-based methods to estimate the models. To illustrate the breadth of new copulas that can be constructed using our approach, we consider three example latent state space models: a stochastic volatility model with an unobserved component, a Markov-switching autoregression, and a Gaussian linear unobserved component model. We use all three inversion copulas to model and forecast quarterly U.S. inflation data. We show how combining the serial dependence structure of the state space models, with flexible asymmetric and heavy-tailed margins, improves the accuracy of the fit and density forecasts in every case
Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence
No full textMost existing reduced-form macroeconomic multivariate time series models employ elliptical disturbances, so that the forecast densities produced are symmetric. In this paper, we use a copula model with asymmetric margins to produce forecast densities with the scope for severe departures from symmetry. Empirical and skew t distributions are employed for the margins, and a high-dimensional Gaussian copula is used to jointly capture cross-sectional and (multivariate) serial dependence. The copula parameter matrix is given by the correlation matrix of a latent stationary and Markov vector autoregression (VAR). We show that the likelihood can be evaluated efficiently using the unique partial correlations, and estimate the copula using Bayesian methods. We examine the forecasting performance of the model for four U.S. macroeconomic variables between 1975:Q1 and 2011:Q2 using quarterly real-time data. We find that the point and density forecasts from the copula model are competitive with those from a Bayesian VAR. During the recent recession the forecast densities exhibit substantial asymmetry, avoiding some of the pitfalls of the symmetric forecast densities from the Bayesian VAR. We show that the asymmetries in the predictive distributions of GDP growth and inflation are similar to those found in the probabilistic forecasts from the Survey of Professional Forecasters. Last, we find that unlike the linear VAR model, our fitted Gaussian copula models exhibit nonlinear dependencies between some macroeconomic variables
AMP Bridging Finance and Behavioral Scholarship on Agent Risk Sharing and Risk Taking
No full textA large volume of research has examined agent risk taking and the contracting problem of risk sharing – the sharing of performance risk across agent and principal – to advance our knowledge of mechanisms that can align the assumed divergent interests and risk preferences of the managerial-agent and shareholder-principal. This research has been undertaken in two research streams that appear to have operated in silos, utilizing different theoretical frameworks and methodological approaches: financial economics and behavioral science. We review the theoretical paradigms and empirical findings deriving from both fields in order to identify opportunities for cross-fertilization and to advance future research in both streams. We also make an assessment of how the combined research efforts of finance and behavioral scholars has progressed in developing our understanding of agent risk taking and mechanisms for achieving agent-principal incentive alignment. Finally, we discuss how this research has influenced the corporate world for better or for worse
Leading Mindfully: How to focus on what matters, influence for good and enjoy leadership more
No full textInference of Self-Exciting Jumps in Prices and Volatility Using High-Frequency Measures
No full textDynamic jumps in the price and volatility of an asset are modelled using a joint Hawkes process in conjunction with a bivariate jump diffusion. A state space representation is used to link observed returns, plus nonparametric measures of integrated volatility and price jumps, to the specified model components; with Bayesian inference conducted using a Markov chain Monte Carlo algorithm. An evaluation of marginal likelihoods for the proposed model relative to a large number of alternative models, including some that have featured in the literature, is provided. An extensive empirical investigation is undertaken using data on the S&P500 market index over the 1996 to 2014 period, with substantial support for dynamic jump intensities - including in terms of predictive accuracy - documented
Conflict Between Controlling Family Owners and Minority Shareholders: Much Ado About Nothing
No full textWe examine the unique nature of conflict between controlling family owners and minority shareholders (principal-principal conflict) in publicly traded family controlled firms through examining shareholder proposals. Implicit in prior governance and family business research has been that non-family shareholders are likely to be in conflict with the dominant family owners. In general, we find that much of this fear may be unwarranted except under specific circumstances. Our findings elucidate sources of heterogeneity in family firm principal-principal conflict and add greater nuance to our understanding of this type of agency problem within family firms
Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures
No full textLeading Change authentically: How authentic leaders influence follower responses to complex change
No full textOrganizational change has become complex and challenging, and employee attitudes and beliefs toward change are even more important. This article proposes a theoretical framework on how authentic leadership may influence followers’ change-oriented attitudes, beliefs, and behaviors through follower psychological resources including hope, trust, optimism, self-efficacy, and resilience, which influence readiness for change and change implementation. We maintain that an authentic leader’s behavior can influence his or her followers’ change-oriented behaviors in participation in decision-making processes and change initiatives, organization citizenship behavior, organizational learning processes, and forming coalition for change. We developed propositions for further empirical investigation
Going Short-Term or Long-Term? CEO Stock Options and Temporal Orientation in the Presence of Slack
No full textWe draw on behavioral agency theory to explain how decision heuristics associated with CEO stock options interact with firm slack to shape the CEO’s preference for short or long-term strategies (temporal orientation). Our findings suggest CEO current option wealth substitutes for the influence of slack resources in encouraging a long-term orientation, while prospective option wealth enhances the positive effect of slack on temporal orientation. Our theory offers explanations for non-findings in previous analysis of the relationship between CEO equity based pay and temporal orientation and provides the insights that CEO incentives created by stock options: (1) enhance the effect of available slack upon temporal orientation; and (2) can both incentivize and de-incentivize destructive short-termism, depending upon the values of current and prospective option wealth
An investigation of authentic leadership\u27s individual and group influences on follower responses
No full textIn this study, we investigated and clarified aspects of the multilevel nature of Authentic Leadership (AL) and its effects on followers. Specifically, we hypothesized that AL would have distinct effects through both personalized AL (P_AL), which is a leader’s direct effect on a follower, and through generalized AL (G_AL), which is a leader’s indirect or group-based effect on a follower as a result of leadership effects among the follower’s coworkers. These hypotheses were consistent with a complete review of the empirical literature on AL’s effects and the results from a sample of leaders and followers working in a large multinational company. The data showed that the two paths of AL’s influence had distinct relationships with follower responses. We discuss the implications of these results, particularly those concerning how to study the multilevel effects of AL