8th Feb 2013  11:00 am  

Speaker: 
Associate Professor Richard Gerlach, 
Affiliation: 
Discipline of Business Analytics, University of Sydney 
Venue: 
Room 498, Merewether Building (H04) 
Title: 
Bayesian Semiparametric Expected Shortfall Forecasting in Financial Markets. 
Abstract: 
Bayesian methods have proven effective for quantile estimation, including for financial Value at Risk forecasting. Expected shortfall is a competing tail risk measure, now favoured by the Basel Committee, involving a conditional expectation, that has recently been semiparametrically estimated via asymmetric least squares. An asymmetric Gaussian density is proposed, allowing a likelihood to be developed that leads to Bayesian semiparametric estimation and forecasts of expectiles and expected shortfall. Further, the conditional autoregressive expectile (CARE) class of model is generalised to two fully nonlinear families. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation. The proposed models are favoured in an empirical study forecasting eight financial return series: evidence of more accurate expected shortfall forecasting, compared to a range of competing methods, is found, while Bayesian estimated models tend to be more accurate. However, during the recent financial crisis period, most models perform badly, while two existing methods perform best. 
27th Feb 2013  11:00 am  

Speaker: 
Professor Helmut Lutkepohl, 
Affiliation: 
Department of Economics, Freie University Berlin and DIW Berlin 
Venue: 
Room 498, Merewether Building (H04) 
Title: 
Identifying Structural Vector Autoregressions via Changes in Volatility. 
Abstract: 
Identification of shocks of interest is a central problem in structural vector autoregressive (SVAR) modelling. Identification is often achieved by imposing restrictions on the impact or longrun effects of shocks or by considering sign restrictions for the impulse responses. In a number of articles changes in the volatility of the shocks have also been used for identification. The present study focusses on the latter device. Some possible setups for identification via heteroskedasticity are reviewed and their potential and limitations are discussed. Two detailed examples are considered to illustrate the approach. 
1st Mar 2013  11:00 am  

Speaker: 
Professor Masayuki Hirukawa, 
Affiliation: 
Faculty of Economics; Setsunan University 
Venue: 
Room 498 
Title: 
Family of Generalized Gamma Kernels: A Unified Approach to the Asymptotics on Asymmetric Kernels 
Abstract: 
Unlike symmetric kernels, exploiting the asymptotics on asymmetric kernels has relied on kernelspecific arguments. Toward a unified approach to their asymptotics, this paper proposes a generic form of asymmetric kernels that consists of a set of common conditions. The generic kernel, called a family of Generalized Gamma kernels, is built on the Generalized Gamma density function, and incorporates the Modified Gamma kernel as a special case. As other special cases, two new kernels, namely, the Weibull and Nakagamim kernels, are also proposed. The density estimator using Generalized Gamma kernels is shown to preserve the appealing properties that the Gamma and Modified Gamma kernels possess. Furthermore, this paper investigates three extensions of the density estimation including multiplicative bias correction. 
4th Mar 2013  10:30 am  

Speaker: 
Professor Vinod Singhal, 
Affiliation: 
Brady Family Professor of Operations Management; Georgia Institute of Technology; Scheller College of Business 
Venue: 
Room 498 Merewether Building H04 
Title: 
Supply Chain Risks and Financial performance: Evidence from DemandSupply Mismatches 
Abstract: 
This talk will present empirical evidence on the effect of supply chain risks on financial performance. Financial performance is measured using measures related to shareholder value, share price volatility, and profitability. It will compare and contrast the corporate performance effects of three different types of supply chain risks; supply chain disruptions, product introduction delays, and excess inventory. The implications of these results on making the business case for supply chain initiatives and justifying investments in technologies and solutions that mitigate supply chain risks will be discussed. 
8th Mar 2013  11:00 am  

Speaker: 
Assistant Professor Saraswata Chaudhuri; Department of Economics, 
Affiliation: 
Authors: Saraswata Chaudhuri and David Guilkey; University of North Carolina at Chapel Hill 
Venue: 
Room 498 
Title: 
GMM with Multiple Missing Variables 
Abstract: 
Abstract: We consider estimation of a finite dimensional unknown parameter value defined by a set of overidentifying unconditional moment restrictions. Random variables forming the different elements of the moment vector can be missing at random  jointly or individually  for some sample units, thus rendering the corresponding elements of the moment vector infeasible. We obtain the semiparametric efficiency bound under this setup. We recommend semiparametric estimators that utilize the available information optimally and hence have asymptotic variances equal to the efficiency bound. A small scale MonteCarlo experiment provides evidence that these semiparametric estimators perform better than the existing estimators even in relatively small samples. An empirical example studying the relationship between a child's years of schooling and number of siblings based on data from Indonesia is provided for the purpose of illustration. 
15th Mar 2013  11:00 am  

Speaker: 
Associate Professor William McCausland, 
Affiliation: 
Universite de Montreal 
Venue: 
Room 498 Merewether Building H04 
Title: 
PRIOR DISTRIBUTIONS FOR RANDOM CHOICE STRUCTURES  WILLIAM J. MCCAUSLAND AND A. A. J. MARLEY. 
Abstract: 
We study various axioms of discrete probabilistic choice,measuring how restrictive they are, both alone and in the presence of other axioms. We do this by formulating a class of prior distributions over the set of random choice structures and using Monte Carlo simulation to compute, for a range of prior distributions, probabilities that various simple and compound axioms hold. For example, the probability of the triangle inequality is usually many orders of magnitude higher than the probability of random utility. For most pairs of axioms we study, the probability that both hold is consistently greater than the product of their marginal probabilities. When one of the two axioms is Sattath and Tversky's (1976) multiplicative inequality and the other is the triangle inequality or one of the variants of stochastic transitivity, the joint probability is consistently less than the product of the marginals. In this sense, the multiplicative inequality is complementary to these other axioms. The reciprocal of the prior probability that an axiom holds is an upper bound on the Bayes factor in favour of a restricted model, in which the axiom holds, against an unrestricted model. The high prior probability of the triangle inequality limits the degree of support data from a single decision maker can provide in its favour. 
12th Apr 2013  11:00 am  

Speaker: 
Professor George Steiner, 
Affiliation: 
McMaster University 
Venue: 
Room 498 Merewether Building H04 
Title: 
Revised deliverytime quotation in scheduling with outsourcing and tardiness penalties 
Abstract: 
There are many situations in supply chain scheduling when the supplier finds it impossible to meet the promised due dates for some orders. We present a model for the rescheduling of orders with simultaneous assignment of attainable revised due dates to minimize due date escalation and tardiness penalties for the supplier. The model can also be used to determine which orders should be outsourced if this option is available. We show that the problem is equivalent to minimizing the total tardiness with rejection with respect to the original due dates. We prove that the problem is NPhard and present a pseudopolynomial algorithm for it. We also present a fully polynomial time approximation scheme for the problem. 
19th Apr 2013  11:00 am  

Speaker: 
Prof. John Geweke, 
Affiliation: 
UTS Business School 
Venue: 
Room 498 Merewether Building H04 
Title: 
Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments 
Abstract: 
Massively parallel desktop computing capabilities now well within the reach of individual academics modify the environment for posterior simulation in fundamental and potentially quite advantageous ways. But to fully exploit these benefits algorithms that conform to parallel computing environments are needed. Sequential Monte Carlo comes very close to this ideal whereas other approaches like Markov chain Monte Carlo do not. This paper presents a sequential posterior simulator well suited to this computing environment. The simulator makes fewer analytical and programming demands on investigators, and is faster, more reliable and more complete than conventional posterior simulators. The paper extends existing sequential Monte Carlo methods and theory to provide a thorough and practical foundation for sequential posterior simulation that is well suited to massively parallel computing environments. It provides detailed recommendations on implementation, yielding an algorithm that requires only code for simulation from the prior and evaluation of prior and data densities and works well in a variety of applications representative of serious empirical work in economics and finance. The algorithm is robust to pathological posterior distributions, generates accurate marginal likelihood approximations, and provides estimates of numerical standard error and relative numerical efficiency intrinsically. The paper concludes with an application that illustrates the potential of these simulators for applied Bayesian inference. 
3rd May 2013  03:00 pm  

Speaker: 
Professor Rodney Strachan, 
Affiliation: 
Australian National University 
Venue: 
Room 498 Merewether Building H04 
Title: 
Invariant Inference and Efficient Computation in the Static Factor Model 
Abstract: 
Factor models are used in a wide range of areas. Two issues with Bayesian versions of these models are a lack of invariance to ordering of the variables and computational inefficiency. This paper develops invariant and efficient Bayesian methods for estimating static factor models. This approach leads to inference on the number of factors that does not depend upon the ordering of the variables, and we provide arguments to explain this invariance. Beginning from identified parameters in which no ordering is imposed, we use parameter expansions to obtain a specification with standard conditional posteriors. Identifying restrictions that are commonly employed result in interpretable factors or loadings and, using our approach, these can be imposed expost. This allows us to investigate several alternative identifying schemes without the need to respecify and resample the model. We show significant gains in computational efficiency. We apply our methods to a simple example using a macroeconomic 
17th May 2013  11:00 am  

Speaker: 
Dr Laurent Pauwels, 
Affiliation: 
Discipline of Business Analytics, University of Sydney Business School 
Venue: 
Room 498 Merewether Building H04 
Title: 
Stock market margin requirements and the 2008 financial crisis 
Abstract: 
This paper presents a case study on how a substantial decrease of margin requirements for equities, equity options, unlisted derivatives and NBI futures made it easier for investors to borrow and take a leveraged position in equities. From 2005 until 2007, the Securities and Exchange Commission had embarked on a policy change, which may have triggered a speculative bubble in the process. This paper hypothesises that the change in margin requirements led to the formation of a speculative bubble in margin debt across U.S. stock market in the advent of the 2008 financial crisis. In order to investigate such a bubble, this paper employs recursive test procedures for testing explosive behaviour. The recursive methodology relies on modifications of unitroot and structural change tests commonly used in the empirical economics and finance literature. Furthermore, the tests can also identify the origination and collapse dates of the bubble. There is evidence of a bubble in margin debt forming in the second half of 2006 and collapsing after September 2008 with the global financial meltdown. Our findings imply that the new margin requirements led to the formation of a speculative bubble and will continue doing so unless adjusted appropriately. 
24th May 2013  11:00 am  

Speaker: 
Associate Professor Oleg Prokopyev, 
Affiliation: 
University of Pittsburgh 
Venue: 
Room 498 Merewether Building H04 
Title: 
Optimal Implantable Cardioverter Defibrillator (ICD) Generator Replacement 
Abstract: 
Implantable Cardioverter Defibrillators (ICDs) include small, battery powered generators, the longevity of which depends on a patient's rate of consumption. Generator replacement, however, involves risks. Hence, a tradeoff exists between prematurely exposing the patient to these risks and allowing for the possibility that the device is unable to deliver therapy when needed. Currently, replacements are performed using a onesizefitsall approach. Here, we develop a Markov decision process model to determine patientspecific optimal replacement policies as a function of patient age and the remaining battery capacity. We analytically establish that the optimal policy is of thresholdtype in the remaining capacity, but not necessarily in patient age. We conduct a large computational study that suggests that under the optimal policy, patients see a considerable decrease in the total expected number of replacements, while achieving the same or greater expected lifetime. 
7th Jun 2013  03:30 pm  

Speaker: 
Professor Eric Renault, 
Affiliation: 
Brown University 
Venue: 
Room 498 Merewether Building H04 
Title: 
Affine Option Pricing Model in Discrete Time 
Abstract: 
We propose an extension with leverage effect of the discrete time stochastic volatility model of Darolles et al. (2006). This extension is shown to be the natural discrete time analog of the Heston (1993) option pricing model. It shares with Heston (1993) the advantage of structure preserving change of measure: with an exponentially affine stochastic discount factor, the historical and the risk neutral models belong to the same family of joint probability distributions for return and volatility processes. This allows computing option prices in semiclosed form through Fourier transform. The discrete time approach has several advantages. First, it allows relaxing the constraints on higher order moments implied by the specification of a diffusion process. Second, it makes more transparent the role of various parameters: leverage versus volatility feedback effect, connection with daily realized volatility measure on highfrequency intraday returns, closedform formulas for affine dynamics of the first two moments of return and volatility that are robust to temporal aggregation, impact of leverage on the volatility smile, etc. This sheds some new light on the identification issue of the various risk premium parameters. An empirical illustration is provided. Keywords: stochastic volatility; leverage; option pricing; equity risk premium; volatility risk premium 
2nd Aug 2013  11:00 am  

Speaker: 
Professor Jiti Gao, 
Affiliation: 
Department of Econometrics and Business Statistics; Monash University 
Venue: 
Room 498 Merewether Bldg H04 
Title: 
Estimation and Specification in Nonstationary Time Series with Endogeneity 
Abstract: 
This presentation gives a survey of some recent developments on estimation and model specification for nonlinear and nonstationary time series. This talk focuses on the discussion of using nonparametric and semiparametric models to deal with a class of time series models that allow for nonlinearity, nonstationarity and endogeneity. Applications in economics and finance are discussed.

9th Aug 2013  11:00 am  

Speaker: 
Dr. Quan Gan, 
Affiliation: 
Discipline of Finance; The University of Sydney 
Venue: 
Room 498, Merewether Bldg H04 
Title: 
Portfolio Selection with Skew Normal Asset Returns 
Abstract: 
This paper examines the portfolio selection problem with skew normal asset returns. By exploring an alternative parameterization of Azzalini & Dalla Valle (1996)'s multivariate skew normal distribution I show that the multivariate skew normal distribution is a special case of Simaan (1993)'s threeparameter model. All Simaan (1993)'s results are applicable to the skew normal asset returns. The threeparameter efficient frontier is spanned by three funds which include two funds from the meanvariance portfolio selection. Combining the skew normal asset returns with the CARA utility, I obtain the closedform certainty equivalent and skewness premium. I show that the skewness premium is positive (negative) when asset returns have negative (positive) skewness. The magnitude of the skewness premium is increasing in market risk aversion. I use the skew normal certainty equivalent to evaluate the economic value of incorporating higher moments in portfolio selection. I find that when investors face broad investment opportunities, the economic value of considering higher moments is negligible under realistic margin requirements.

16th Aug 2013  11:00 am  

Speaker: 
Dr Vasilis Sarafidis, 
Affiliation: 
Department of Econometrics; Monash University 
Venue: 
Room 498 Merewether Building H04 
Title: 
Estimation of Correlated Random Coefficient Models for Short Panels with a MultiFactor Structure 
Abstract: 
In this paper we develop a methodology that provides a consistent estimator of the average effect in a correlated random coefficient panel data model with crosssectional dependence when the time dimension is fixed. The problem of identification and estimation is studied without imposing the restriction that T is larger than the number of regressors. We put forward a pooled GMM estimation approach, which allows certain forms of weak exogeneity or endogeneity. Finite sample evidence shows that the estimator performs well. 
23rd Aug 2013  11:00 am  

Speaker: 
Professor Dvir Shabtay, 
Affiliation: 
Ben Gurion University of Negev, Israel 
Venue: 
Room 498 Merewether Building H04 
Title: 
The Resource Dependent Assignment Problem and Its Applications in Scheduling 
Abstract: 
We extend the classical linear assignment problem to the case where the cost of assigning agent j to task i is a multiplication of task i's cost parameter by a cost function of agent j. The cost function of agent j is a either a linear or a convex function of the amount of resource allocated to the agent. A solution for our assignment problem is defined by the assignment of agents to tasks and by a resource allocation to each agent. The quality of a solution is measured by two criteria. The first criterion is the total assignment cost and the second is the total weighted resource consumption. We address these criteria via four different problem variations. For both assignment cost function (linear and convex) we obtained similar results (also the analysis is completely different). For both functions, we prove that (i) our assignment problem is NPhard for three of the four variations even if all the resource consumption weights are equal and that (ii) the fourth variation is solvable in polynomial time. For the linear assignment cost function, we also provide a pseudo polynomial time algorithm to solve the NPhard variations. In addition, we find that our assignment problem is equivalent to a large set of important scheduling problems whose complexity has heretofore been an open question for three of the four variations. 
30th Aug 2013  11:00 am  

Speaker: 
Dr Yong Song, 
Affiliation: 
Business School; University of Technology Sydney 
Venue: 
Room 498 Merewether Building H04 
Title: 
Infinite Hidden Markov Models with Application to Speculative Bubble Detection 
Abstract: 
This paper proposes an infinite hidden Markov model (iHMM) to detect, date stamp, and estimate speculative bubbles. Three features make this new approach attractive to practitioners. First, the iHMM is capable of capturing the nonlinear dynamics of heterogeneous bubble behaviors as it allows an infinite number of regimes. Second, the implementation of this procedure is straightforward as the detection, dating, and estimation of bubbles are done simultaneously in a coherent Bayesian framework. Third, the iHMM, by assuming hierarchical structures, is parsimonious and superior in outofsample forecast. This model and its extensions are applied to the pricedividend ratio of NASDAQ Composite Index from 1973M02 to 2013M01. The insample posterior analysis and outofsample prediction find evidence of explosive dynamics during the dotcom bubble period. Model comparison shows that the iHMM is strongly supported by the data against the finite hidden Markov model. 
11th Sep 2013  11:30 am  

Speaker: 
Professor Robert Kohn, 
Affiliation: 
Australian School of Business; The University of New South Wales 
Venue: 
Room 498 Merewether Bldg H04 
Title: 
Bayesian Inference Using an Unbiased Estimate of the Likelihood 
Abstract: 
We consider Bayesian inference by importance sampling or by Markov chain Monte Carlo when the likelihood is analytically intractable but can be unbiasedly estimated. When the inference is by importance sampling we refer to this procedure importance sampling squared (ISsquared), as we can often estimate the likelihood itself by importance sampling. We provide a formal justification for such inference when working with an estimate of the likelihood and study its convergence properties. We analyse the effect of estimating the likelihood on the resulting inference and provide guidelines on how to set up the precision of the likelihood estimate in order to obtain an optimal tradeoff between the computational cost of estimating the likelihood and accuracy for posterior inference on the model parameters. We illustrate the methodology in empirical applications to stochastic volatility models, nonlinear DSGE models and multinomial panel data models. 
20th Sep 2013  11:00 am  

Speaker: 
Associate Professor Nektarios Aslanidis, 
Affiliation: 
Departament d' Economia; Universitat Rovira i Virgili, CREIP ; Spain 
Venue: 
Room 498 Merewether Bldg 
Title: 
Quantiles of the Realized StockBond Correlation and Links to the Macroeconomy 
Abstract: 
This paper adopts quantile regressions to scrutinize the realized stockbond correlation based upon high frequency returns. The paper provides insample and outofsample analysis and considers a large number of macrofinance predictors wellknow from the return predictability literature. Strong insample predictability is obtained from quantile models with factoraugmented predictors, particularly at the lower to median quantiles. Outofsample the quantile factor model works best at the median to upper quantiles. Investor sentiment generally does not significantly affect the quantiles of the realized stock bond corrrelation. Keywords: Realized stockbond correlation; Quantile regressions; Macrofinance variables; Factor analysis; Investor sentiment. JEL Classifications: C22; G11; G12 
10th Oct 2013  11:30 am  

Speaker: 
Professor Rob Hyndman, 
Affiliation: 
Department of Econometrics and Business Statistics; Monash University 
Venue: 
Room 498 Merewether Bldg H04 
Title: 
Forecasting Hierarchical Time Series 
Abstract: 
Hierarchical time series occur when there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on dimensions such as product, geography, or some other features. A common application occurs in manufacturing where forecasts of sales need to be made for a range of different products in different locations. The forecasts need to add up appropriately across the levels of the hierarchy. Historically, forecasting of hierarchical time series has been done using either the "bottomup" method, various "topdown" methods, or some combination of the two known as "middleout" approaches. I will describe a framework for studying such methods which leads naturally to an optimal combination approach based on a large illconditioned regression model. While the model leads to optimal forecasts, the illconditioning and size of the model make computation difficult or impossible. I will describe a solution to this problem that make the forecasts fast to compute even for problems involving hundreds of thousands of time series.

18th Oct 2013  11:00 am  

Speaker: 
Dr Nuttanan Wichitaksorn, 
Affiliation: 
Department of Mathematics and Statistics; University of Canterbury, NZ 
Venue: 
Room 498 Merewether Bldg H04 
Title: 
The Bayesian Parallel Computation for Intractable Likelihood 
Abstract: 
Parallel computation is a fast growing computing environment in many areas including computational Bayesian statistics. However, most of the Bayesian parallel computing have been implemented through the sequential Monte Carlo method where model parameters are updated sequentially and it is suitable for some largescale problems. This talk is the first to revive the use of adaptive griddy Gibbs (AGG) algorithm under the Markov chain Monte Carlo framework and show how to implement the AGG using the parallel computation. The parallel AGG is suitable for (i) small to mediumscale problems where the dimension of model parameter space is not very high, (ii) some or all model parameters are defined on a specific interval, and (iii) model likelihood is intractable. In addition, the parallel AGG is relatively easy to implement and code. Since the AGG is a Gibbs algorithm where each of model parameters is directly drawn from the conditional posterior density, the model marginal likelihood can be conveniently computed and immediately provided after the end of posterior simulation. Three examples including a linear regression model with Studentt error, a nonlinear regression model, and a financial time series model (GARCH), will be illustrated to show the applicability of the AGG to the parallel computing environment.

1st Nov 2013  11:00 am  

Speaker: 
Dr Vitali Alexeev, 
Affiliation: 
University of Tasmania 
Venue: 
Room 498 Merewether Building H04 
Title: 
Equity portfolio diversification with high frequency data 
Abstract: 
Investors wishing to achieve a particular level of diversification may be misled on how many 
5th Dec 2013  11:00 am  

Speaker: 
Dr Georgios Tsiotas, 
Affiliation: 
University of Crete; Greece 
Venue: 
Rm 498 Merewether Bldg H04 
Title: 
Loss Functions in ValueatRisk Estimation 
Abstract: 
The Value at Risk (VaR) is a risk measure that is widely used by financial institutions to allocate risk. VaR forecast estimation involves the evaluation of conditional quantiles based on the currently available information. Recent advances in VaR evaluation incorporate conditional variance into the quantile estimation, which yields the Conditional Autoregressive VaR (CAViaR) models. Optimal VaR estimates are typically generated using the socalled ``check'' loss function. 
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