Relationships between Australian real estate and stock market prices—a case of market inefficiency

This paper explores the relationship between the Australian real estate and equity market between 1980 and 1999. The results from this study show three specific outcomes that extend the current literature on real estate finance. First, it is shown that structural shifts in stock and property markets can lead to the emergence of an unstable linear relationship between these markets. That is, full‐sample results support bi‐directional Granger causality between equity and real estate returns, whereas when sub‐samples are chosen that account for structural shifts the results generally show that changes within stock market prices influence real estate market returns, but not vice versa. Second, the results also indicate that non‐linear causality tests show a strong unidirectional relationship running from the stock market to the real estate market. Finally, from this empirical evidence a trading strategy is developed which offers superior performance when compared to adopting a passive strategy for investing in Australian securitized property. These results appear to have important implications for managing property assets in the funds management industry and also for the pricing efficiency within the Australian property market. Copyright © 2002 John Wiley & Sons, Ltd.     

The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

In this study, we explore the effect of cojumps within the agricultural futures market, and cojumps between the agricultural futures market and the stock market, on stock volatility forecasting. Also, we take into account large and small components of cojumps. We have several noteworthy findings. First, large jumps may lead to more substantial fluctuations and are more powerful than small jumps. The effect of cojumps and their decompositions on future volatility are mixed. Second, a model including large and small cojumps between the agricultural futures market and the stock market can achieve a higher forecasting accuracy, implying that large and small cojumps contain more useful predictive information than cojumps themselves. Third, our conclusions are robust based on various robustness tests such as the realized kernel, expanding forecasts, different forecasting windows, different jump tests, and different threshold values.     

Stock market volatility and the forecasting performance of stock index futures

This study attempts to apply the general equilibrium model of stock index futures with both stochastic market volatility and stochastic interest rates to the TAIFEX and the SGX Taiwan stock index futures data, and compares the predictive power of the cost of carry and the general equilibrium models. This study also represents the first attempt to investigate which of the five volatility estimators can enhance the forecasting performance of the general equilibrium model. Additionally, the impact of the up‐tick rule and other various explanatory factors on mispricing is also tested using a regression framework. Overall, the general equilibrium model outperforms the cost of carry model in forecasting prices of the TAIFEX and the SGX futures. This finding indicates that in the higher volatility of the Taiwan stock market incorporating stochastic market volatility into the pricing model helps in predicting the prices of these two futures. Furthermore, the comparison results of different volatility estimators support the conclusion that the power EWMA and the GARCH(1,1) estimators can enhance the forecasting performance of the general equilibrium model compared to the other estimators. Additionally, the relaxation of the up‐tick rule helps reduce the degree of mispricing. Copyright © 2008 John Wiley & Sons, Ltd.     

Forecasting realized volatility of oil futures market: A new insight

In this study we propose several new variables, such as continuous realized semi‐variance and signed jump variations including jump tests, and construct a new heterogeneous autoregressive model for realized volatility models to investigate the impacts that those new variables have on forecasting oil price volatility. In‐sample results indicate that past negative returns have greater effects on future volatility than that of positive returns, and our new signed jump variations have a significantly negative influence on the future volatility. Out‐of‐sample empirical results with several robust checks demonstrate that our proposed models can not only obtain better performance in forecasting volatility but also garner larger economic values than can the existing models discussed in this paper.     

A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information

This study is the first to examine the impacts of overnight and intraday oil futures cross-market information on predicting the US stock market volatility the high-frequency data. In-sample estimations present that high overnight oil futures RV can lead to high RV of the S&P 500. Moreover, negative overnight returns are more powerful than positive components, implying the existence of the leverage effect. From statistical and economic perspectives, out-of-sample results indicate that the decompositions of overnight oil futures and intraday RVs, based on signed intraday returns, can significantly increase the models' predictive ability. Finally, when considering the US stock market overnight effect, the decompositions are still useful to predict volatility, especially during high US stock market fluctuations and high and low EPU states.     

Regression‐based modeling of market option prices: with application to S&P500 options

This paper presents a simple empirical approach to modeling and forecasting market option prices using localized option regressions (LOR). LOR projects market option prices over localized regions of their state space and is robust to assumptions regarding the underlying asset dynamics (e.g. log‐normality) and volatility structure. Our empirical study using 3 years of daily S&P500 options shows that LOR yields smaller out‐of‐sample pricing errors (e.g. 32% 1‐day‐out) relative to an efficient benchmark from the literature and produces option prices free of the volatility smile. In addition to being an efficient and robust option‐modeling and valuation tool for large option books, LOR provides a simple‐to‐implement empirical benchmark for evaluating more complex risk‐neutral models. Copyright © 2007 John Wiley & Sons, Ltd.     

Forecasting for the LCD monitor market

The TFT‐LCD (thin‐film transistor–liquid crystal display) industry is one of the key global industries with products that have high clock speed. In this research, the LCD monitor market is considered for an empirical study on hierarchical forecasting (HF). The proposed HF methodology consists of five steps. First, the three hierarchical levels of the LCD monitor market are identified. Second, several exogenously driven factors that significantly affect the demand for LCD monitors are identified at each level of product hierarchy. Third, the three forecasting techniques—regression analysis, transfer function, and simultaneous equations model—are combined to forecast future demand at each hierarchical level. Fourth, various forecasting approaches and disaggregating proportion methods are adopted to obtain consistent demand forecasts at each hierarchical level. Finally, the forecast errors with different forecasting approaches are assessed in order to determine the best forecasting level and the best forecasting approach. The findings show that the best forecast results can be obtained by using the middle‐out forecasting approach. These results could guide LCD manufacturers and brand owners on ways to forecast future market demands. Copyright 2008 John Wiley & Sons, Ltd.     

Lead-lag relationship between spot index and futures price of the nikkei stock average

This paper examines the lead-lag relationship between the spot index and futures price of the Nikkei Stock Average. Using daily data in the post-crash period we investigate the interaction between the spot and futures series through the error correction model. Two versions of error correction models are considered, depending on the postulated long-run equilibrium relationship. It is found that lagged changes in the futures price affect the short-term adjustment in the spot index, but not vice versa. Forecasting models for the spot index are also constructed using the univariate time series approach and the vector autoregressive method. For the post-sample forecast comparison the error correction models produce the best results. The vector autoregressive method performs better than the martingale model, while the univariate time series method gives the poorest forecasts.     

Note the use of growth curves in forecasting market development

In this note the four-parameter generalized logistic curve is introduced and some of its properties are discussed. The curve is then fitted to certain data sets. The results indicate that the generalized logistic can be a worthwhile alternative to the more familiar logistic and Gompertz curves.     

Assessing the historical accuracy of regional economic forecasts

This study addresses for the first time systematic evaluation of a widely used class of forecasts, regional economic forecasts. Ex ante regional structural equation model forecasts are analysed for 19 metropolitan areas. One- to ten-quarter-ahead forecasts are considered and the seven-year sample spans a complete business cycle. Counter to previous speculation in the literature, (1) dependency on macroeconomic forecasting model inputs does not substantially erode accuracy relative to univariate extrapolative methodologies and (2) stochastic time series models do not on average, yield more accurate regional economic predictions than structural models. Similar to findings in other studies, clear preferences among extrapolative methodologies do not emerge. Most general conclusions, however, are subject to caveats based on step-length effects and region-specific effects.     

Assessing the value of Hermite densities for predictive distributions

The empirical behaviour of two Hermitian densities—the Edgeworth asymptotic expansion, and the Gallant and Nychka ( 1987 ) squared transformation—are assessed and compared to Hansen's (1994) asymmetric Student t. The comparison focuses on VaR measurements and overall probability fits. In and out of sample, Berkowitz's ( 2001 ) extended procedures are also implemented. Results are moderately favourable to the Hermitians, supporting the semi‐nonparametric interpretation, and multivariate developments. Copyright © 2009 John Wiley & Sons, Ltd.     

High‐frequency Markov switching models in the foreign exchange market

This paper estimates two‐state Markov models for three daily exchange rate series, and investigates the profitability of following the generated forecasts using the performance of simple chartist trading rules as benchmarks. It is shown that (1) the data are well approximated by Markov models, (2) the performance of previously profitable trading rules has dramatically declined in the 1990s, and (3) the Markov models are unstable and not suitable for forecasting in their current form. Copyright © 2000 John Wiley & Sons, Ltd.     

Performance of GARCH models in forecasting stock market volatility

This paper studies the performance of GARCH model and its modifications, using the rate of returns from the daily stock market indices of the Kuala Lumpur Stock Exchange (KLSE) including Composite Index, Tins Index, Plantations Index, Properties Index, and Finance Index. The models are stationary GARCH, unconstrained GARCH, non‐negative GARCH, GARCH‐M, exponential GARCH and integrated GARCH. The parameters of these models and variance processes are estimated jointly using the maximum likelihood method. The performance of the within‐sample estimation is diagnosed using several goodness‐of‐fit statistics. We observed that, among the models, even though exponential GARCH is not the best model in the goodness‐of‐fit statistics, it performs best in describing the often‐observed skewness in stock market indices and in out‐of‐sample (one‐step‐ahead) forecasting. The integrated GARCH, on the other hand, is the poorest model in both respects. Copyright © 1999 John Wiley & Sons, Ltd.     

Economic factors and the stock market: a new perspective

It has been widely accepted that many financial and economic variables are non‐linear, and neural networks can model flexible linear or non‐linear relationships among variables. The present paper deals with an important issue: Can the many studies in the finance literature evidencing predictability of stock returns by means of linear regression be improved by a neural network? We show that the predictive accuracy can be improved by a neural network, and the results largely hold out‐of‐sample. Both the neural network and linear forecasts show significant market timing ability. While the switching portfolio based on the linear forecasts outperforms the buy‐and‐hold market portfolio under all three transaction cost scenarios, the switching portfolio based on the neural network forecasts beats the market only if there is no transaction cost. Copyright © 1999 John Wiley & Sons, Ltd.     

Forecast accuracy after pretesting with an application to the stock market

In econometrics, as a rule, the same data set is used to select the model and, conditional on the selected model, to forecast. However, one typically reports the properties of the (conditional) forecast, ignoring the fact that its properties are affected by the model selection (pretesting). This is wrong, and in this paper we show that the error can be substantial. We obtain explicit expressions for this error. To illustrate the theory we consider a regression approach to stock market forecasting, and show that the standard predictions ignoring pretesting are much less robust than naive econometrics might suggest. We also propose a forecast procedure based on the ‘neutral Laplace estimator’, which leads to an improvement over standard model selection procedures. Copyright © 2004 John Wiley & Sons, Ltd.     

Forecasting new product trial in a controlled test market environment

A number of researchers have developed models that use test market data to generate forecasts of a new product's performance. However, most of these models have ignored the effects of marketing covariates. In this paper we examine what impact these covariates have on a model's forecasting performance and explore whether their presence enables us to reduce the length of the model calibration period (i.e. shorten the duration of the test market). We develop from first principles a set of models that enable us to systematically explore the impact of various model ‘components’ on forecasting performance. Furthermore, we also explore the impact of the length of the test market on forecasting performance. We find that it is critically important to capture consumer heterogeneity, and that the inclusion of covariate effects can improve forecast accuracy, especially for models calibrated on fewer than 20 weeks of data. Copyright © 2003 John Wiley & Sons, Ltd.