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Abstract:
Value investing was first identified by Graham and Dodd in the mid-30's as an effective approach to investing. Under this approach stocks are rated as being cheap or expensive largely based on some valuation multiple such as the stock's price-to-earnings or book-to-market ratio. Numerous studies have found that value investing does perform well across most equity markets but it is also true that over most reasonable time horizons, the majority of value stocks underperform the market. The reason for this is that the poor valuation ratios for many companies are reflective of poor fundamentals that are only worsening. The typical value measures do not provide any insights into those stocks whose performance is likely to mean-revert and those that will continue along their recent downhill path.

The hypothesis in this paper is that the value stocks most likely to mean-revert are those that are financially sound. Further, it is proposed that we should be able to gain some insights into the financial strength of the value companies using fundamental accounting data. We apply a Bayesian model averaging approach to a set of fundamental accounting variables to forecast, the probability of each value stock outperforming the market. These probability estimates are then used as the basis for enhancing a value portfolio that has been formed using some valuation multiple. The positive note from our study of the US, UK and Australian equity markets is that it appears that fundamental accounting data can be used to enhance the performance of a value investment strategy. The bad news is that the sources of accounting data that play the greatest role in providing such insights would seem to vary both across time and across markets.

Abstract:
A multiple-regime threshold generalized autoregressive conditionally heteroskedastic capital asset pricing model is introduced. The model captures asymmetric risk through allowing market beta to change discretely between regimes that are driven by market information. Asymmetric volatility and mean equation dynamics are also captured. We confirm the time-varying nature of market risk, in response to changes in the market, and that this discrete time variation can differ across assets. These findings could have important implications for optimising investment decisions: e.g. in risk assessment, portfolio selection and hedging decisions.

Asymmetry, Bayesian, CAPM, Time-varying market beta, Markov chain Monte Carlo method, posterior model probability

Abstract:
The contribution of this paper is twofold. First, we exploit copula methodology, with two threshold GARCH models as marginals, to construct a bivariate copula threshold GARCH model, simultaneously capturing asymmetric nonlinear behaviour in univariate stock returns of spot and futures markets and bivariate dependency, in a flexible manner. Two elliptical copulas (Gaussian and Student-t) and three Archimedean copulas (Clayton, Gumbel and the Mixture of Clayton and Gumbel) are utilized. Secondly, we employ the presenting models to investigate the hedging performance for five East Asian spot and futures stock markets: Hong Kong, Japan, Korea, Singapore and Taiwan. Compared with conventional hedging strategies, including Engle’s dynamic conditional correlation GARCH model, the results show that hedge ratios constructed by a Gaussian or Mixture copula are the best-performed in variance reduction for all markets except Japan and Singapore, and provide close to the best returns on a hedging portfolio over the sample period.

hedge ratio, threshold GARCH, copula, spot and futures market, stock return

Abstract:
It is well known that volatility asymmetry exists in financial markets. This paper reviews and investigates recently developed techniques for Bayesian estimation and model selection applied to a large group of modern asymmetric heteroskedastic models. These include the GJR-GARCH, threshold autoregression with GARCH errors, threshold GARCH and Double threshold heteroskedastic model with auxiliary threshold variables. Further we briefly review recent methods for Bayesian model selection, such as: reversible jump Markov chain Monte Carlo, Monte Carlo estimation via independent sampling from each model and importance sampling methods. Seven heteroskedastic models are then compared, for three long series of daily Asian market returns, in a model selection study illustrating the preferred model selection method. Major evidence of nonlinearity in mean and volatility is found, with the preferred model having a weighted threshold variable of local and international market news.

asymmetric volatility model, Markov chain Monte Carlo, posterior model probability, parallel

Abstract:
A multiple-regime threshold nonlinear financial time series model, with a fat-tailed error distribution, is discussed and Bayesian estimation and inference is considered. Further, approximate Bayesian posterior model comparison among competing models with different numbers of regimes is considered: effectively a test for the number of required regimes. An adaptive MCMC sampling scheme is designed, while importance sampling is employed to estimate Bayesian residuals for model diagnostic testing. Our modeling framework provides a parsimonious representation of well-known stylized features of financial time series and facilitates statistical inference in the presence of high or explosive persistence and dynamic conditional volatility. We focus on the three-regime case: the main feature of the model is the capturing of mean and volatility asymmetries in financial markets, while allowing an explosive volatility regime. A simulation study highlights the properties of our MCMC estimators and the accuracy and favourable performance as a model selection tool, compared to a deviance criterion, of the posterior model probability approximation method. An empirical study of eight international oil & gas markets illustrates strong support for the three-regime model over its competitors, in most markets, in terms of model posterior probability and in showing three distinct regime behaviours: falling/explosive, dormant and rising markets.

Abstract:
Testing for Granger non-causality over varying quantile levels could be used to measure and infer dynamic linkages, enabling the identification of quantiles for which causality is relevant, or not. However, dynamic quantiles in financial application settings are clearly affected by heteroscedasticity, as well as the exogenous and endogenous variables under consideration. GARCH-type dynamics are added to the standard quantile regression model, so as to more robustly examine quantile causal relations between dynamic variables. An adaptive Bayesian Markov chain Monte Carlo scheme, exploiting the link between quantile regression and the skewed-Laplace distribution, is designed for estimation and inference of the quantile causal relations, simultaneously estimating and accounting for heteroscedasticity. Dynamic quantile linkages for the international stock markets in Taiwan and Hong Kong are considered over a range of quantile levels. Specifically, the hypothesis that these stock returns are Granger-caused by the US market and/or the Japanese market is examined. The US market is found to significantly and positively Granger-cause both markets at all quantile levels, while the Japanese market effect was also significant at most quantile levels, but with weaker effects.

Bayesian, Granger non-causality in quantiles, Skewed-Laplace distribution, GARCH, Markov chain Monte Carlo

Abstract:
Barrier options traded in the Australian market vary considerably in terms of the extent to which the barrier is monitored and in terms of the location of the barrier level relative to the exercise price. This paper examines the impact of these differences on prices and also on deltas and gammas. We find that it is not possible to generalize results concerning hedge parameter values to all barrier options. We find that options examined by Easton et al. (2004) do not display discontinuity of deltas at the barrier levels and that their apparent overpricing cannot be attributed to hedging difficulties.

Abstract:
This paper examines the ASEAN-5 countries and explores the impact of structural breaks on the level of financial integration in that region. An extended cointegration procedure allowing for three types of structural break, is employed and compared with the standard Johansen procedure, for daily and weekly returns. The empirical results suggest a higher level of integration within the ASEAN-5 markets than previously found, suggesting that financial risk reduction benefits from diversifying investments across the region are less than previously thought. Further, Singapore and Thailand are the main long-term drivers in the region; Malaysia and Indonesia are more short-term drivers. Structural breaks are found to correspond with the Asian financial crisis in 1997/98 and a possible Y2K effect in late 1999. Results are verified using another structural break model and method, where break dates are treated as known.

cointegration, rank, structural break, Asian financial crisis, stock market.

Abstract:
It is reasonable to suggest that a portfolio manager with direct property diversified by sector or region is more interested in strategic than in tactical asset allocation. However, even with strategic allocations of property the portfolio manager needs a regular monitoring of the inter-relationships amongst assets comprising the portfolio to ensure that unexpected events do not 'permanently' alter such relationships. One procedure for ascertaining whether assets are inter-related over the long run (and therefore offer few diversification benefits) is through cointegration analysis. A difficulty with conventional cointegration analysis, however, is that it is unable to accommodate changes in equilibrium relationships that might occur due to unexpected structural changes. In this paper we apply the Gregory and Hansen cointegration procedure to consider how unexpected structural changes might affect the potential long run diversification benefits of assets held in an Australian property portfolio.

Abstract:
An effective approach for forecasting return volatility via threshold nonlinear heteroskedastic models of the daily asset price range is provided. The return is defined as the difference between the highest and lowest log intra-day asset price. A general model specification is proposed, allowing the intra-day high-low price range to depend nonlinearly on past information, or an exogenous variable such as US market information. The model captures aspects such as sign or size asymmetry and heteroskedasticity, which are commonly observed in financial markets. The focus is on parameter estimation, inference and volatility forecasting in a Bayesian framework. An MCMC sampling scheme is employed for estimation and shown to work well in simulation experiments. Finally, competing range-based and return-based heteroskedastic models are compared via out-of-sample forecast performance. Applied to six international financial market indices, the range-based threshold heteroskedastic models are well supported by the data in terms of finding significant threshold nonlinearity, diagnostic checking and volatility forecast performance under various volatility proxies.

size and sign asymmetry, volatility model, conditional autoregressive range (CARR) model, threshold variable, Bayes

Abstract:
A family of threshold nonlinear generalised autoregressive conditionally heteroscedastic models is considered, that allows smooth transitions between regimes, capturing size asymmetry via an exponential smooth transition function. A Bayesian approach is taken and an efficient adaptive sampling scheme is employed for inference, including a novel extension to a recently proposed prior for the smoothing parameter that solves a likelihood identification problem. A simulation study illustrates that the sampling scheme performs well, with the chosen prior kept close to uninformative, while successfully ensuring identification of model parameters and accurate inference for the smoothing parameter. An empirical study confirms the potential suitability of the model, highlighting the presence of both mean and volatility (size) asymmetry; while the model is favoured over modern, popular model competitors, including those with sign asymmetry, via the deviance information criterion.

Asymmetric, Bayesian inference, Heteroskedastic, Markov chain Monte Carlo (MCMC), Normal scale mixtures distribution

Abstract:
BAYSTAR provides Bayesian MCMC methods for iteratively sampling to provide parameter estimates and inference for the two-regime SETAR model. A convenient user interface for importing data from a file or specifying true values for simulated data is easy to apply for analysis. Parameter inferences are summarized to an easily readable format. Simultaneously, the checking of convergence can be done by monitoring the MCMC trace plots and autocorrelograms.

Asymmetry; MCMC method; two-regime SETAR model; BAYSTAR package