http://hdl.handle.net/2123/8118 2013-05-24T10:07:43Z 2013-05-24T10:07:43Z Anderson, Edward Qian, Cheng http://hdl.handle.net/2123/9071 2013-05-09T16:52:20Z 2013-05-09T00:00:00Z

Title: Competing for contracts with buyer uncertainty: Choosing price and quality variables Authors: Anderson, Edward; Qian, Cheng Abstract: We model a situation in which a single firm evaluates competing suppliers and selects just one. Suppliers submit bids involving both price and quality variables. The buyer makes a choice which from the supplier's perspective appears to contain a stochastic element - for example the buyer may have information, which is not shared with the suppliers, and that gives one supplier an advantage in the final choice. We use a discrete choice model of buyer choice (e.g. multinomial logit). Our main result is that the supplier's choice of the quality variables is not affected by the competitive environment. Thus the suppliers compete only on price. We compare this with a second model in which the buyer's weighting on different quality variables is uncertain at the time bids are made.

2013-05-09T00:00:00Z Vasnev, Andrey Pauwels, Laurent http://hdl.handle.net/2123/8965 2013-03-08T17:52:32Z 2013-03-01T00:00:00Z

Title: Forecast combination for U.S. recessions with real-time data Authors: Vasnev, Andrey; Pauwels, Laurent Abstract: This paper proposes the use of forecast combination to improve predictive accuracy in forecasting the U.S. business cycle index, as published by the Business Cycle Dating Committee of the NBER. It focuses on one-step ahead out-of-sample monthly forecast utilising the well-established coincident indicators and yield curve models, allowing for dynamics and real-time data revisions. Forecast combinations use logscore and quadratic-score based weights, which change over time. This paper finds that forecast accuracy improves when combining the probability forecasts of both the coincident indicators model and the yield curve model, compared to each model's own forecasting performance.

2013-03-01T00:00:00Z Vasnev, Andrey Magnus, Jan R http://hdl.handle.net/2123/8964 2013-03-08T17:52:31Z 2013-03-01T00:00:00Z

Title: Practical use of sensitivity in econometrics with an illustration to forecast combinations Authors: Vasnev, Andrey; Magnus, Jan R Abstract: Sensitivity analysis is important for its own sake and also in combination with diagnostic testing. We consider the question how to use sensitivity statistics in practice, in particular how to judge whether sensitivity is large or small. For this purpose we distinguish between absolute and relative sensitivity and highlight the context-dependent nature of any sensitivity analysis. Relative sensitivity is then applied in the context of forecast combination and sensitivity-based weights are introduced. All concepts are illustrated through the European yield curve. In this context it is natural to look at sensitivity to autocorrelation and normality assumptions. Different forecasting models are combined with equal, fit-based and sensitivity-based weights, and compared with the multivariate and random walk benchmarks. We show that the fit-based weights and the sensitivity-based weights are complementary. For long-term maturities the sensitivity-based weights perform better than other weights.

2013-03-01T00:00:00Z Vasnev, Andrey Gerlach, Richard Watkins, John http://hdl.handle.net/2123/8963 2013-03-08T17:52:29Z 2012-12-01T00:00:00Z

Title: Multiple Event Incidence and Duration Analysis for Credit Data Incorporating Non-Stochastic Loan Maturity Authors: Vasnev, Andrey; Gerlach, Richard; Watkins, John Abstract: Applications of duration analysis in Economics and Finance exclusively employ methods for events of stochastic duration. In application to credit data, previous research incorrectly treats the time to pre-determined maturity events as censored stochastic event times. The medical literature has binary parametric ‘cure rate’ models that deal with populations that never experienced the modelled event. We propose and develop a Multinomial parametric incidence and duration model, incorporating such populations. In the class of cure rate models, this is the first fully parametric multinomial model and is the first framework to accommodate an event with pre-determined duration. The methodology is applied to unsecured personal loan credit data provided by one of Australia’s largest financial services organizations. This framework is shown to be more flexible and predictive through a simulation and empirical study that reveals: simulation results of estimated parameters with a large reduction in bias; superior forecasting of duration; explanatory variables can act in different directions upon incidence and duration; and, variables exist that are statistically significant in explaining only incidence or duration.

2012-12-01T00:00:00Z Vasnev, Andrey Pauwels, Laurent http://hdl.handle.net/2123/8933 2013-02-12T15:52:42Z 2013-01-01T00:00:00Z

Title: Forecast combination for U.S. recessions with real-time data Authors: Vasnev, Andrey; Pauwels, Laurent Abstract: This paper proposes the use of forecast combination to improve predictive accuracy in forecasting the U.S. business cycle index as published by the Business Cycle Dating Committee of the NBER. It focuses on one-step ahead out-of-sample monthly forecast utilising the well-established coincident indicators and yield curve models, allowing for dynamics and real-time data revisions. Forecast combinations use logscore and quadratic-score based weights, which change over time. This paper finds that forecast accuracy improves when combining the probability forecasts of both the coincident indicators model and the yield curve model, compared to each model's own forecasting performance.

2013-01-01T00:00:00Z Vasnev, Andrey Pauwels, Laurent http://hdl.handle.net/2123/8932 2013-02-12T15:52:41Z 2013-01-01T00:00:00Z

Title: Practical considerations for optimal weights in density forecast combination Authors: Vasnev, Andrey; Pauwels, Laurent Abstract: The problem of finding appropriate weights to combine several density forecasts is an important issue currently debated in the forecast combination literature. Recently, a paper by Hall and Mitchell (IJF, 2007) proposes to combine density forecasts with optimal weights obtained from solving an optimization problem. This paper studies the properties of this optimization problem when the number of forecasting periods is relatively small and finds that it often produces corner solutions by allocating all the weight to one density forecast only. This paper’s practical recommendation is to have an additional training sample period for the optimal weights. While reserving a portion of the data for parameter estimation and making pseudo-out-of-sample forecasts are common practices in the empirical literature, employing a separate training sample for the optimal weights is novel, and it is suggested because it decreases the chances of corner solutions. Alternative log-score or quadratic-score weighting schemes do not have this training sample requirement. January

2013-01-01T00:00:00Z Proietti, Tommaso Luati, Alessandra http://hdl.handle.net/2123/8337 2012-05-09T16:52:31Z 2012-05-01T00:00:00Z

Title: Maximum likelihood estimation of time series models: the Kalman filter and beyond Authors: Proietti, Tommaso; Luati, Alessandra Abstract: The purpose of this chapter is to provide a comprehensive treatment of likelihood inference for state space models. These are a class of time series models relating an observable time series to quantities called states, which are characterized by a simple temporal dependence structure, typically a first order Markov process. The states have sometimes substantial interpretation. Key estimation problems in economics concern latent variables, such as the output gap, potential output, the non-accelerating-inflation rate of unemployment, or NAIRU, core inflation, and so forth. Time-varying volatility, which is quintessential to finance, is an important feature also in macroeconomics. In the multivariate framework relevant features can be common to different series, meaning that the driving forces of a particular feature and/or the transmission mechanism are the same. The objective of this chapter is reviewing this algorithm and discussing maximum likelihood inference, starting from the linear Gaussian case and discussing the extensions to a nonlinear and non Gaussian framework.

2012-05-01T00:00:00Z Matsypura, D. Timkovsky, V.G. http://hdl.handle.net/2123/8173 2012-05-01T17:11:04Z 2010-09-01T00:00:00Z

Title: Margining Option Portfolios by Network Flows Authors: Matsypura, D.; Timkovsky, V.G. Abstract: As shown in [Rudd and Schroeder, 1982], the problem of margining option portfolios where option spreads with two legs are used for offsetting can be solved in polynomial time by network flow algorithms. However, spreads with only two legs do not provide sufficient accuracy in measuring risk. Therefore, margining practice also employs spreads with three and four legs. A polynomial time solution to the extension of the problem where option spreads with three and four legs are also used for offsetting is not known. In this paper we propose a heuristic network flow algorithm for this extension and present a computational study that proves high efficiency of this algorithm in margining practice.

2010-09-01T00:00:00Z Matsypura, D. Timkovsky, V.G. http://hdl.handle.net/2123/8172 2012-03-09T18:02:24Z 2010-07-01T00:00:00Z

Title: Combinatorics of Option Spreads: The Margining Aspect Authors: Matsypura, D.; Timkovsky, V.G. Abstract: In December 2005, the U.S. Securities and Exchange Commission approved margin rules for complex option spreads with 5, 6, 7, 8, 9, 10 and 12 legs. Only option spreads with 2, 3 or 4 legs were recognized before. Taking advantage of option spreads with a large number of legs substantially reduces margin requirements and, at the same time, adequately estimates risk for margin accounts with positions in options. In this paper we present combinatorial models for known and newly discovered option spreads with up to 134 legs. We propose their full characterization in terms of matchings, alternating cycles and chains in graphs with bicolored edges. We show that the combinatorial analysis of option spreads reveals powerful hedging mechanisms in the structure of margin accounts, and that the problem of minimizing the margin requirement for a portfolio of option spreads can be solved in polynomial time using network flow algorithms. We also give recommendations on how to create more efficient margin rules for options.

2010-07-01T00:00:00Z Coffman, E.G. Jr Matsypura, D. Timkovsky, V.G. http://hdl.handle.net/2123/8171 2012-05-01T17:11:09Z 2010-03-01T00:00:00Z

Title: Portfolio Margining: Strategy vs Risk Authors: Coffman, E.G. Jr; Matsypura, D.; Timkovsky, V.G. Abstract: This paper presents the results of a novel mathematical and experimental analysis of two approaches to margining customer accounts, strategy-based and risk-based. Building combinatorial models of hedging mechanisms of these approaches, we show that the strategy-based approach is, at this point, the most appropriate one for margining security portfolios in customer margin accounts, while the risk-based approach can work efficiently for margining only index portfolios in customer mar-gin accounts and inventory portfolios of brokers. We also show that the application of the risk-based approach to security portfolios in customer margin accounts is very risky and can result in the pyramid of debt in the bullish market and the pyramid of loss in the bearish market. The results of this paper support the thesis that the use of the risk-based approach to margining customer accounts with positions in stocks and stock options since April 2007 influenced and triggered the U.S. stock market crash in October 2008. We also provide recommendations on ways to set appropriate margin requirements to help avoid such failures in the future.

2010-03-01T00:00:00Z Lu, Zudi Huang, Hai Gerlach, Richard http://hdl.handle.net/2123/8170 2012-03-11T16:02:01Z 2010-01-01T00:00:00Z

Title: Estimating Value At Risk Authors: Lu, Zudi; Huang, Hai; Gerlach, Richard Abstract: Significantly driven by JP Morgan's RiskMetrics system with EWMA (exponentially weighted moving average) forecasting technique, value-at-risk (VaR) has turned to be a popular measure of the degree of various risks in financial risk management. In this paper we propose a new approach termed skewed-EWMA to forecast the changing volatility and formulate an adaptively efficient procedure to estimate the VaR. Differently from the JP Morgan's standard-EWMA, which is derived from a Gaussian distribution, and the Guermat and Harris (2001)'s robust-EWMA, from a Laplace distribution, we motivate and derive our skewed-EWMA procedure from an asymmetric Laplace distribution, where both skewness and heavy tails in return distribution and the time-varying nature of them in practice are taken into account. An EWMA-based procedure that adaptively adjusts the shape parameter controlling the skewness and kurtosis in the distribution is suggested. Backtesting results show that our proposed skewed-EWMA method offers a viable improvement in forecasting VaR.

2010-01-01T00:00:00Z Gerlach, Richard Chen, Cathy W.S Lin, Liou-Yan http://hdl.handle.net/2123/8169 2012-05-01T17:11:09Z 2012-01-01T00:00:00Z

Title: Bayesian Semi-parametric Expected Shortfall Forecasting in Financial Markets Authors: Gerlach, Richard; Chen, Cathy W.S; Lin, Liou-Yan Abstract: Bayesian semi-parametric estimation has proven effective for quantile estimation in general and specifically in financial Value at Risk forecasting. Expected short-fall is a competing tail risk measure, involving a conditional expectation beyond a quantile, that has recently been semi-parametrically estimated via asymmetric least squares and so-called expectiles. An asymmetric Gaussian density is proposed allowing a likelihood to be developed that leads to Bayesian semi-parametric estimation and forecasts of expectiles and expected shortfall. Further, the conditional autoregressive expectile class of model is generalised to two fully nonlinear families. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation in these families. The proposed models are clearly favoured in an empirical study forecasting eleven financial return series: clear evidence of more accurate expected shortfall forecasting, compared to a range of competing methods is found. Further, the most favoured models are those estimated by Bayesian methods.

2012-01-01T00:00:00Z Proietti, Tommaso http://hdl.handle.net/2123/8168 2012-05-01T17:11:09Z 2011-10-01T00:00:00Z

Title: The Multistep Beveridge-Nelson Decomposition Authors: Proietti, Tommaso Abstract: The Beveridge-Nelson decomposition defines the trend component in terms of the eventual forecast function, as the value the series would take if it were on its long-run path. The paper introduces the multistep Beveridge-Nelson decomposition, which arises when the forecast function is obtained by the direct autoregressive approach, which optimizes the predictive ability of the AR model at forecast horizons greater than one. We compare our proposal with the standard Beveridge-Nelson decomposition, for which the forecast function is obtained by iterating the one-stepahead predictions via the chain rule. We illustrate that the multistep Beveridge-Nelson trend is more efficient than the standard one in the presence of model misspecification and we subsequently assess the predictive validity of the extracted transitory component with respect to future growth.

2011-10-01T00:00:00Z Proietti, Tommaso Lütkepohl, Helmut http://hdl.handle.net/2123/8167 2012-05-02T16:53:00Z 2011-10-01T00:00:00Z

Title: Does the Box-Cox transformation help in forecasting macroeconomic time series? Authors: Proietti, Tommaso; Lütkepohl, Helmut Abstract: The paper investigates whether transforming a time series leads to an improvement in forecasting accuracy. The class of transformations that is considered is the Box-Cox power transformation, which applies to series measured on a ratio scale. We propose a nonparametric approach for estimating the optimal transformation parameter based on the frequency domain estimation of the prediction error variance, and also conduct an extensive recursive forecast experiment on a large set of seasonal monthly macroeconomic time series related to industrial production and retail turnover. In about one fifth of the series considered the Box-Cox transformation produces forecasts significantly better than the untransformed data at one-step-ahead horizon; in most of the cases the logarithmic transformation is the relevant one. As the forecast horizon increases, the evidence in favour of a transformation becomes less strong. Typically, the naïve predictor that just reverses the transformation leads to a lower mean square error than the optimal predictor at short forecast leads. We also discuss whether the preliminary in-sample frequency domain assessment conducted provides a reliable guidance which series should be transformed for improving significantly the predictive performance.

2011-10-01T00:00:00Z Proietti, Tommaso Grassi, Stefano http://hdl.handle.net/2123/8166 2012-05-01T17:11:07Z 2011-09-01T00:00:00Z

Title: Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search Authors: Proietti, Tommaso; Grassi, Stefano Abstract: An important issue in modelling economic time series is whether key unobserved components representing trends, seasonality and calendar components, are deterministic or evolutive. We address it by applying a recently proposed Bayesian variable selection methodology to an encompassing linear mixed model that features, along with deterministic effects, additional random explanatory variables that account for the evolution of the underlying level, slope, seasonality and trading days. Variable selection is performed by estimating the posterior model probabilities using a suitable Gibbs sampling scheme. The paper conducts an extensive empirical application on a large and representative set of monthly time series concerning industrial production and retail turnover. We find strong support for the presence of stochastic trends in the series, either in the form of a time-varying level, or, less frequently, of a stochastic slope, or both. Seasonality is a more stable component: only in 70% of the cases we were able to select at least one stochastic trigonometric cycle out of the six possible cycles. Most frequently the time variation is found in correspondence with the fundamental and the first harmonic cycles. An interesting and intuitively plausible finding is that the probability of estimating time-varying components increases with the sample size available. However, even for very large sample sizes we were unable to find stochastically varying calendar effects.

2011-09-01T00:00:00Z Pauwels, Laurent Liu, Li-Gang http://hdl.handle.net/2123/8165 2012-05-01T17:11:05Z 2011-09-01T00:00:00Z

Title: Do External Political Pressures Affect the Renminbi Exchange Rate? Authors: Pauwels, Laurent; Liu, Li-Gang Abstract: This paper investigates whether external political pressure for faster renminbi (RMB) appreciation affect both the daily returns and the conditional volatility of the RMB central parity rate. We construct several political pressure indicators pertaining to the RMB exchange rate, with a special emphasis on the US pressure, to test the hypothesis. After controlling for Chinese macroeconomic surprise news, we find that US and non-US political pressure does not have a significant influence on RMB's daily returns. However, evidence suggests that political pressures, and especially those from the US, have statistically significant impacts on the conditional volatility of the RMB. Furthermore, we conduct the same exercise on the 12-month RMB nondeliverable forward rate (NDF). We find that the NDF market is highly responsive to macroeconomic surprise news and there is some evidence that Sino-US bilateral meetings affect the conditional volatility of the RMB NDF.

2011-09-01T00:00:00Z Anderson, E.J. http://hdl.handle.net/2123/8164 2012-05-01T17:11:04Z 2011-08-01T00:00:00Z

Title: Ranking games and gambling: When to quit when you're ahead Authors: Anderson, E.J. Abstract: It is common for rewards to be given on the basis of a rank ordering, so that relative performance amongst a cohort is the criterion. In this paper we formulate an equilibrium model in which an agent makes successive decisions on whether or not to gamble and is rewarded on the basis of a rank ordering of final wealth. This is a model of the behaviour of mutual fund managers who are paid depending on funds under management which in turn are largely determined by annual or quarterly rank orderings. In this model fund managers can elect either to pick stocks or to use a market tracking strategy. In equilibrium the final distribution of rewards will have a negative skew. We explore how this distribution depends on the number of players, the probability of success when gambling, the structure of the rewards, and on information regarding the other player's performance.

2011-08-01T00:00:00Z Gerlach, Richard Chen, Qian http://hdl.handle.net/2123/8163 2012-05-01T17:11:04Z 2011-01-01T00:00:00Z

Title: The Two-sided Weibull Distribution and Forecasting Financial Tail Risk Authors: Gerlach, Richard; Chen, Qian Abstract: A two-sided Weibull is developed to model the conditional financial return distribution, for the purpose of forecasting Value at Risk (VaR) and conditional VaR. A range of conditional return distributions are combined with four volatility specifications to forecast tail risk in four international markets, two exchange rates and one individual asset series, over a four year forecast period that includes the recent global financial crisis. The two-sided Weibull performs at least as well as other distributions for VaR forecasting, but performs most favourably for conditional Value at Risk forecasting, prior to as well as during and after the recent crisis.

2011-01-01T00:00:00Z Anderson, E.J. Holmberg, P. Philpott, A.B. http://hdl.handle.net/2123/8162 2012-03-09T18:02:21Z 2009-11-01T00:00:00Z

Title: Mixed strategies in discriminatory divisible-good auctions Authors: Anderson, E.J.; Holmberg, P.; Philpott, A.B. Abstract: Using the concept of market-distribution functions, we derive general optimality conditions for discriminatory divisible-good auctions, which are also applicable to Bertrand games and non-linear pricing. We introduce the concept of offer distribution function to analyze randomized offer curves, and characterize mixed-strategy Nash equilibria for pay-as-bid auctions where demand is uncertain and costs are common knowledge; a setting for which pure-strategy supply function equilibria typically do not exist. We generalize previous results on mixtures over horizontal offers as in Bertrand-Edgeworth games, but more importantly we characterize novel mixtures over partly increasing supply functions.

2009-11-01T00:00:00Z Watkins, John Vasnev, Andrey Gerlach, Richard http://hdl.handle.net/2123/8161 2012-03-11T16:01:59Z 2009-11-01T00:00:00Z

Title: Survival Analysis for Credit Scoring: Incidence and Latency Authors: Watkins, John; Vasnev, Andrey; Gerlach, Richard Abstract: Duration analysis is an analytical tool for time-to-event data that has been borrowed from medicine and engineering to be applied by econometricians to investigate typical economic and finance problems. In applications to credit data, time to the pre-determined maturity events have been treated as censored observations for the events with stochastic latency. A methodology, motivated by the cure rate model framework, is developed in this paper to appropriately analyse a set of mutually exclusive terminal events where at least one event may have a predetermined latency. The methodology is applied to a set of personal loan data provided by one of Australia's largest financial services institutions. This is the first framework to simultaneously model prepayment, write off and maturity events for loans. Furthermore, in the class of cure rate models it is the first fully parametric multinomial model and the first to accommodate for an event with pre-determined latency. The simulation study found this model performed better than the two most common applications of survival analysis to credit data. In addition, the result of the application to personal loans data reveals particular explanatory variables can act in different directions upon incidence and latency of an event and variables exist that may be statistically significant in explaining only incidence or latency.

2009-11-01T00:00:00Z Chapman, Archie C. Leslie, David S. Rogers, Alex Jennings, Nicholas R. http://hdl.handle.net/2123/8160 2012-05-01T17:11:08Z 2011-08-01T00:00:00Z

Title: Convergent learning algorithms for potential games with unknown noisy rewards Authors: Chapman, Archie C.; Leslie, David S.; Rogers, Alex; Jennings, Nicholas R. Abstract: In this paper, we address the problem of convergence to Nash equilibria in games with rewards that are initially unknown and which must be estimated over time from noisy observations. These games arise in many real-world applications, whenever rewards for actions cannot be prespecified and must be learned on-line. Standard results in game theory, however, do not consider such settings. Specifically, using results from stochastic approximation and differential inclusions, we prove the convergence of variants of fictitious play and adaptive play to Nash equilibria in potential games and weakly acyclic games, respectively. These variants all use a multi-agent version of Q-learning to estimate the reward functions and a novel form of the e-greedy decision rule to select an action. Furthermore, we derive e-greedy decision rules that exploit the sparse interaction structure encoded in two compact graphical representations of games, known as graphical and hypergraphical normal form, to improve the convergence rate of the learning algorithms. The structure captured in these representations naturally occurs in many distributed optimisation and control applications. Finally, we demonstrate the efficacy of the algorithms in a simulated ad hoc wireless sensor network management problem.

2011-08-01T00:00:00Z Gerlach, Richard Chen, Cathy W.S. Chan, Nancy Y. C. http://hdl.handle.net/2123/8159 2012-03-09T18:02:23Z 2009-08-01T00:00:00Z

Title: Bayesian time-varying quantile forecasting for Value-at-Risk in financial markets Authors: Gerlach, Richard; Chen, Cathy W.S.; Chan, Nancy Y. C. Abstract: Recently, Bayesian solutions to the quantile regression problem, via the likelihood of a Skewed-Laplace distribution, have been proposed. These approaches are extended and applied to a family of dynamic conditional autoregressive quantile models. Popular Value at Risk models, used for risk management in finance, are extended to this fully nonlinear family. An adaptive Markov chain Monte Carlo sampling scheme is adapted for estimation and inference. Simulation studies illustrate favourable performance, compared to the standard numerical optimization of the usual nonparametric quantile criterion function, in finite samples. An empirical study generating Value at Risk forecasts for ten major financial stock indices finds significant nonlinearity in dynamic quantiles and evidence favoring the proposed model family, for lower level quantiles, compared to a range of standard parametric volatility models, a semi-parametric smoothly mixing regression and some nonparametric risk measures, in the literature.

2009-08-01T00:00:00Z Pauwels, Laurent Vasnev, Andrey http://hdl.handle.net/2123/8158 2012-05-01T17:11:07Z 2011-06-01T00:00:00Z

Title: Forecast combination for discrete choice models: predicting FOMC monetary policy decisions Authors: Pauwels, Laurent; Vasnev, Andrey Abstract: This paper provides a methodology for combining forecasts based on several discrete choice models. This is achieved primarily by combining one-step-ahead probability forecast associated with each model. The paper applies well-established scoring rules for qualitative response models in the context of forecast combination. Log-scores and quadratic-scores are both used to evaluate the forecasting accuracy of each model and to combine the probability forecasts. In addition to producing point forecasts, the effect of sampling variation is also assessed. This methodology is applied to forecast the US Federal Open Market Committee (FOMC) decisions in changing the federal funds target rate. Several of the economic fundamentals influencing the FOMC decisions are nonstationary over time and are modelled in a similar fashion to Hu and Phillips (2004a, JoE). The empirical results show that combining forecasted probabilities using scores mostly outperforms both equal weight combination and forecasts based on multivariate models.

2011-06-01T00:00:00Z Anderson, Edward http://hdl.handle.net/2123/8157 2012-05-01T17:11:07Z 2011-04-01T00:00:00Z

Title: Supply Function Equilibria Always Exist Authors: Anderson, Edward Abstract: Supply function equilibria are used in the analysis of divisible good auctions with a large number of identical objects to be sold or bought. An important example occurs in wholesale electricity markets. Despite the substantial literature on supply function equilibria the existence of a pure strategy Nash equilibria for a uniform price auction in asymmetric cases has not been established in a general setting. In this paper we prove the existence of a supply function equilibrium for a duopoly with asymmetric firms having convex costs, with decreasing concave demand subject to an additive demand shock, provided the second derivative of the demand function is small enough. The proof is constructive and also gives insight into the structure of the equilibrium solutions.

2011-04-01T00:00:00Z Gerlach, Richard Chen, Cathy W.S Lin, Edward M.H. Lee, Wcw http://hdl.handle.net/2123/8156 2012-05-01T17:11:06Z 2011-03-01T00:00:00Z

Title: Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis Authors: Gerlach, Richard; Chen, Cathy W.S; Lin, Edward M.H.; Lee, Wcw Abstract: Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four error probability distributions: Gaussian, Student-t, skewed-t and generalized error distribution. Adaptive Markov chain Monte Carlo methods are employed in estimation and forecasting. A portfolio of four Asia-Pacific stock markets is considered. Two forecasting periods are evaluated in light of the recent global financial crisis. Results reveal that: (i) GARCH models out-performed stochastic volatility models in almost all cases; (ii) asymmetric volatility models were clearly favoured pre-crisis; while at the 1% level during and post-crisis, for a 1 day horizon, models with skewed-t errors ranked best, while IGARCH models were favoured at the 5% level; (iii) all models forecasted VaR less accurately and anti-conservatively post-crisis

2011-03-01T00:00:00Z Knight, Eva Cottet, Remy http://hdl.handle.net/2123/8155 2012-05-01T17:11:06Z 2011-02-01T00:00:00Z

Title: Australian Residential Housing Market & Hedonic Construction of House Price Indices for Metropolitan Authors: Knight, Eva; Cottet, Remy Abstract: A Semiparametric spatial model is used as it allows nonlinear estimation of both mean and variance. A Bayesian approach is used for inference via a Markov Chain Monte Carlo sampling scheme. A distinct advantage of using the Bayesian approach is the incorporation of prior information in the inferential process. The prior is updated with arrival of information. In the real world, the modeller should have some idea of the outcome before the modelling process begins. Finite sample inference can be obtained and is more accurate than asymptotic approximation. In the case of the real estate market, transaction data are finite due to infrequent trading. Estimation is done via posterior distributions which factor in the variability of estimators and therefore have improved confidence intervals. Spatial variables such as longitude and latitude are modelled via the construction of a bivariate thin plate spline. These two variables provide powerful lens for capturing the effect of demographic factors and for borrowing and lending information in neighbouring suburbs. Demographic factors and 1 trends are just as important as economic factors in determining demand for residential housing and they are also included in the model.

2011-02-01T00:00:00Z