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.     

Predicting stock index volatility: can market volume help?

This paper explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the NYSE. An application of linear and non-linear Granger causality tests highlights evidence of bidirectional causality, although the relationship is stronger from volatility to volume than the other way around. The out-of-sample forecasting performance of various linear, GARCH, EGARCH, GJR and neural network models of volatility are evaluated and compared. The models are also augmented by the addition of a measure of lagged volume to form more general ex-ante forecasting models. The results indicate that augmenting models of volatility with measures of lagged volume leads only to very modest improvements, if any, in forecasting performance. © 1998 John Wiley & Sons, Ltd.     

Testing for Granger (non‐)causality in a time‐varying coefficient VAR model

In this paper we propose Granger (non‐)causality tests based on a VAR model allowing for time‐varying coefficients. The functional form of the time‐varying coefficients is a logistic smooth transition autoregressive (LSTAR) model using time as the transition variable. The model allows for testing Granger non‐causality when the VAR is subject to a smooth break in the coefficients of the Granger causal variables. The proposed test then is applied to the money–output relationship using quarterly US data for the period 1952:2–2002:4. We find that causality from money to output becomes stronger after 1978:4 and the model is shown to have a good out‐of‐sample forecasting performance for output relative to a linear VAR model. Copyright © 2008 John Wiley & Sons, Ltd.     

Bias in the estimation of non‐linear transformations of the integrated variance of returns

Volatility models such as GARCH, although misspecified with respect to the data‐generating process, may well generate volatility forecasts that are unconditionally unbiased. In other words, they generate variance forecasts that, on average, are equal to the integrated variance. However, many applications in finance require a measure of return volatility that is a non‐linear function of the variance of returns, rather than of the variance itself. Even if a volatility model generates forecasts of the integrated variance that are unbiased, non‐linear transformations of these forecasts will be biased estimators of the same non‐linear transformations of the integrated variance because of Jensen's inequality. In this paper, we derive an analytical approximation for the unconditional bias of estimators of non‐linear transformations of the integrated variance. This bias is a function of the volatility of the forecast variance and the volatility of the integrated variance, and depends on the concavity of the non‐linear transformation. In order to estimate the volatility of the unobserved integrated variance, we employ recent results from the realized volatility literature. As an illustration, we estimate the unconditional bias for both in‐sample and out‐of‐sample forecasts of three non‐linear transformations of the integrated standard deviation of returns for three exchange rate return series, where a GARCH(1, 1) model is used to forecast the integrated variance. Our estimation results suggest that, in practice, the bias can be substantial. Copyright © 2006 John Wiley & Sons, Ltd.     

A note on in‐sample and out‐of‐sample tests for Granger causality

This paper studies in‐sample and out‐of‐sample tests for Granger causality using Monte Carlo simulation. The results show that the out‐of‐sample tests may be more powerful than the in‐sample tests when discrete structural breaks appear in time series data. Further, an empirical example investigating Taiwan's investment–saving relationship shows that Taiwan's domestic savings may be helpful in predicting domestic investments. It further illustrates that a possible Granger causal relationship is detected by out‐of‐sample tests while the in‐sample test fails to reject the null of non‐causality. Copyright © 2005 John Wiley & Sons, Ltd.     

Selection of the relevant information set for predictive relationships analysis between time series

In time series analysis, a vector Y is often called causal for another vector X if the former helps to improve the k‐step‐ahead forecast of the latter. If this holds for k=1, vector Y is commonly called Granger‐causal for X . It has been shown in several studies that the finding of causality between two (vectors of) variables is not robust to changes of the information set. In this paper, using the concept of Hilbert spaces, we derive a condition under which the predictive relationships between two vectors are invariant to the selection of a bivariate or trivariate framework. In more detail, we provide a condition under which the finding of causality (improved predictability at forecast horizon 1) respectively non‐causality of Y for X is unaffected if the information set is either enlarged or reduced by the information in a third vector Z . This result has a practical usefulness since it provides a guidance to validate the choice of the bivariate system { X , Y } in place of { X , Y , Z }. In fact, to test the ‘goodness’ of { X , Y } we should test whether Z Granger cause X not requiring the joint analysis of all variables in { X , Y , Z }. Copyright © 2002 John Wiley & Sons, Ltd.     

Forecasting volatility of emerging stock markets: linear versus non‐linear GARCH models

ARCH and GARCH models are substantially used for modelling volatility of time series data. It is proven by many studies that if variables are significantly skewed, linear versions of these models are not sufficient for both explaining the past volatility and forecasting the future volatility. In this paper, we compare the linear(GARCH(1,1)) and non‐linear(EGARCH) versions of GARCH model by using the monthly stock market returns of seven emerging countries from February 1988 to December 1996. We find that for emerging stock markets GARCH(1,1) model performs better than EGARCH model, even if stock market return series display skewed distributions. Copyright © 2000 John Wiley & Sons, Ltd.     

Estimating the long memory granger causality effect with a spectrum estimator

This paper discusses the Granger causality test by a spectrum estimator which allows the transfer function to have long memory properties. In traditional methodology the relationship among variables is usually assumed to be short memory or contemporaneous. Hence, we have to make sure they are of the same integrated order, else there might be a spurious regression problem. In practice, not all the variables are fractionally co‐integrated in the economic model. They may have the same random resources, but under a different integrated order. This paper focuses on how to capture the long memory Granger causality effect in the transfer function. This does not necessarily assume the variables are of the same fractional integrated order. Moreover, by the transfer function we construct an estimator to test the long memory effect with the Granger causality sense. Copyright © 2006 John Wiley & Sons, Ltd.     

Forecasting and trading currency volatility: an application of recurrent neural regression and model combination

In this paper, we examine the use of non‐parametric Neural Network Regression (NNR) and Recurrent Neural Network (RNN) regression models for forecasting and trading currency volatility, with an application to the GBP/USD and USD/JPY exchange rates. Both the results of the NNR and RNN models are benchmarked against the simpler GARCH alternative and implied volatility. Two simple model combinations are also analysed. The intuitively appealing idea of developing a nonlinear nonparametric approach to forecast FX volatility, identify mispriced options and subsequently develop a trading strategy based upon this process is implemented for the first time on a comprehensive basis. Using daily data from December 1993 through April 1999, we develop alternative FX volatility forecasting models. These models are then tested out‐of‐sample over the period April 1999–May 2000, not only in terms of forecasting accuracy, but also in terms of trading efficiency: in order to do so, we apply a realistic volatility trading strategy using FX option straddles once mispriced options have been identified. Allowing for transaction costs, most trading strategies retained produce positive returns. RNN models appear as the best single modelling approach yet, somewhat surprisingly, model combination which has the best overall performance in terms of forecasting accuracy, fails to improve the RNN‐based volatility trading results. Another conclusion from our results is that, for the period and currencies considered, the currency option market was inefficient and/or the pricing formulae applied by market participants were inadequate. Copyright © 2002 John Wiley & Sons, Ltd.     

Realized Volatility Forecast of Stock Index Under Structural Breaks

We investigate the realized volatility forecast of stock indices under the structural breaks. We utilize a pure multiple mean break model to identify the possibility of structural breaks in the daily realized volatility series by employing the intraday high‐frequency data of the Shanghai Stock Exchange Composite Index and the five sectoral stock indices in Chinese stock markets for the period 4 January 2000 to 30 December 2011. We then conduct both in‐sample tests and out‐of‐sample forecasts to examine the effects of structural breaks on the performance of ARFIMAX‐FIGARCH models for the realized volatility forecast by utilizing a variety of estimation window sizes designed to accommodate potential structural breaks. The results of the in‐sample tests show that there are multiple breaks in all realized volatility series. The results of the out‐of‐sample point forecasts indicate that the combination forecasts with time‐varying weights across individual forecast models estimated with different estimation windows perform well. In particular, nonlinear combination forecasts with the weights chosen based on a non‐parametric kernel regression and linear combination forecasts with the weights chosen based on the non‐negative restricted least squares and Schwarz information criterion appear to be the most accurate methods in point forecasting for realized volatility under structural breaks. We also conduct an interval forecast of the realized volatility for the combination approaches, and find that the interval forecast for nonlinear combination approaches with the weights chosen according to a non‐parametric kernel regression performs best among the competing models. Copyright © 2014 John Wiley & Sons, Ltd.     

Cross‐Section Stock Return and Implied Covariance between Jump and Diffusive Volatility

I examine the information content of option‐implied covariance between jumps and diffusive risk in the cross‐sectional variation in future returns. This paper documents that the difference between realized volatility and implied covariance (RV‐ICov) can predict future returns. The results show a significant and negative association of expected return and realized volatility–implied covariance spread in both the portfolio level analysis and cross‐sectional regression study. A trading strategy of buying a portfolio with the lowest RV‐ICov quintile portfolio and selling with the highest one generates positive and significant returns. This RV‐Cov anomaly is robust to controlling for size, book‐to‐market value, liquidity and systematic risk proportion. Copyright © 2015 John Wiley & Sons, Ltd.     

Trading volume and realized volatility forecasting: Evidence from the China stock market

The existing contradictory findings on the contribution of trading volume to volatility forecasting prompt us to seek new solutions to test the sequential information arrival hypothesis (SIAH). Departing from other empirical analyses that mainly focus on sophisticated testing methods, this research offers new insights into the volume-volatility nexus by decomposing and reconstructing the trading activity into short-run components that typically represent irregular information flow and long-run components that denote extreme information flow in the stock market. We are the first to attempt at incorporating an improved empirical mode decomposition (EMD) method to investigate the volatility forecasting ability of trading volume along with the Heterogeneous Autoregressive (HAR) model. Previous trading volume is used to obtain the decompositions to forecast the future volatility to ensure an ex ante forecast, and both the decomposition and forecasting processes are carried out by the rolling window scheme. Rather than trading volume by itself, the results show that the reconstructed components are also able to significantly improve out-of-sample realized volatility (RV) forecasts. This finding is robust both in one-step ahead and multiple-step ahead forecasting horizons under different estimation windows. We thus fill the gap in studies by (1) extending the literature on the volume-volatility linkage to EMD-HAR analysis and (2) providing a clear view on how trading volume helps improve RV forecasting accuracy.     

Assessing inefficiency in the s&p 500 futures market

We analyse the price movement of the S&P 500 futures market for violations of the efficient market hypothesis on a short-term basis. To assess market inefficiency we construct a model and find that the returns, i.e. the difference in the logarithm of closing prices on consecutive days, exhibit the usual conditional heteroscedasticity behaviour typical of long series of financial data. To account for this non-linear behaviour we scale the returns by a volatility factor which depends on the daily high, low, and closing price. The rescaled series, which may be interpreted as the trend-countertrend component of the time series, is modelled using Box and Jenkins techniques. The resulting model is an ARMA(1,1). The scale factors are assumed to form a time series and are modelled using a semi-non-parametric method which avoids the restrictive assumptions of most ARCH or GARCH models. Using the combined model we perform 1000 simulations of market data, each simulation comprising 250 days (approximately one year). We then formulate a naive trading strategy which is based on the ratio of the one-day-ahead expected return to its one-day-ahead expected conditional standard deviation. The trading strategy has four adjustable parameters which are set to maximize profits for the simulation data. Next, we apply the trading strategy to one year of recent out-of-sample data. Our conclusion is that the S&P 500 futures market exhibits only slight inefficiencies, but that there exist, in principle, better trading strategies which take account of risk than the benchmark strategy of buy-and-hold. We have also constructed a linear model for the return series. Using the linear model, we have simulated returns and determined the optimum values for the adjustable parameters of the trading strategy. In this case, the optimum trading strategy is the same as the benchmark strategy, buy-and-hold. Finally, we have compared the profitability of the optimized trading strategy, based on the non-linear model, to three ad hoc trading strategies using the out-of-sample data. The three ad hoc strategies are more profitable than the optimized strategy.     

Bayesian Analysis of a Threshold Stochastic Volatility Model

This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of the mean equation follows a threshold distribution in which the mean innovation switches between two regimes. In our model, the threshold is treated as an unknown parameter. We show that the proposed threshold SV model can not only capture the time‐varying volatility of returns, but can also accommodate the asymmetric shape of conditional distribution of the returns. Parameter estimation is carried out by using Markov chain Monte Carlo methods. For model selection and volatility forecast, an auxiliary particle filter technique is employed to approximate the filter and prediction distributions of the returns. Several experiments are conducted to assess the robustness of the proposed model and estimation methods. In the empirical study, we apply our threshold SV model to three return time series. The empirical analysis results show that the threshold parameter has a non‐zero value and the mean innovations belong to two separately distinct regimes. We also find that the model with an unknown threshold parameter value consistently outperforms the model with a known threshold parameter value. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting Errors,Directional Accuracy and Profitability of Currency Trading: The Case of EUR/USD Exchange Rate

We provide a comprehensive study of out‐of‐sample forecasts for the EUR/USD exchange rate based on multivariate macroeconomic models and forecast combinations. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations, in particular those based on principal components of forecasts, help to improve over benchmark trading strategies, although the excess return per unit of deviation is limited. Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of Realized Measure and Implied Volatility in Forecasting Volatility

This paper compares the information content of realized measures constructed from high‐frequency data and implied volatilities from options in the context of forecasting volatility. The comparison is based on within‐sample and out‐of‐sample (over horizons of 1–22 days) forecasts of daily S&P 500 index return volatility. The paper adds to the findings of previous studies, by considering recent developments in the related practice and the literature. It is shown that, for within‐sample fitting, the realized measure is more informative than the implied volatility. In contrast, the implied volatility is more informative than the realized measure for out‐of‐sample forecasting, in particular for multi‐step‐ahead forecasting. Moreover, we show that it is helpful to use all the information provided by the realized measure and the implied volatility for the within‐sample fitting. For multi‐step‐ahead forecasting, however, it is better to use only the implied volatility. Copyright © 2013 John Wiley & Sons, Ltd.     

The predictive value of temporally disaggregated volatility: evidence from index futures markets

This paper examines the benefits to forecasters of decomposing close-to-close return volatility into close-to-open (nighttime) and open-to-close (daytime) return volatility. Specifically, we consider whether close-to-close volatility forecasts based on the former type of (temporally aggregated) data are less accurate than corresponding forecasts based on the latter (temporally disaggregated) data. Results obtained from seven different US index futures markets reveal that significant increases in forecast accuracy are possible when using temporally disaggregated volatility data. This result is primarily driven by the fact that forecasts based on such data can be updated as more information becomes available (e.g., information flow from the preceding close-to-open/nighttime trading session). Finally, we demonstrate that the main findings of this paper are robust to the index futures market considered, the way in which return volatility is constructed, and the method used to assess forecast accuracy. Copyright © 2008 John Wiley & Sons, Ltd.     

Trading volume and prediction of stock return reversals: Conditioning on investor types' trading

We show that contrasting results on trading volume's predictive role for short‐horizon reversals in stock returns can be reconciled by conditioning on different investor types' trading. Using unique trading data by investor type from Korea, we provide explicit evidence of three distinct mechanisms leading to contrasting outcomes: (i) informed buying—price increases accompanied by high institutional buying volume are less likely to reverse; (ii) liquidity selling—price declines accompanied by high institutional selling volume in institutional investor habitat are more likely to reverse; (iii) attention‐driven speculative buying—price increases accompanied by high individual buying‐volume in individual investor habitat are more likely to reverse. Our approach to predict which mechanism will prevail improves reversal forecasts following return shocks: An augmented contrarian strategy utilizing our ex ante formulation increases short‐horizon reversal strategy profitability by 40–70% in the US and Korean stock markets.     

Direction‐of‐change forecasting using a volatility‐based recurrent neural network

This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction‐of‐change of the market in the case of the NASDAQ composite index. The sample extends over the period 8 February 1971 to 7 April 1998, while the sub‐period 8 April 1998 to 5 February 2002 has been reserved for out‐of‐sample testing purposes. We demonstrate that the incorporation in the trading rule of estimates of the conditional volatility changes strongly enhances its profitability, after the inclusion of transaction costs, during bear market periods. This improvement is being measured with respect to a nested model that does not include the volatility variable as well as to a buy‐and‐hold strategy. We suggest that our findings can be justified by invoking either the ‘volatility feedback’ theory or the existence of portfolio insurance schemes in the equity markets. Our results are also consistent with the view that volatility dependence produces sign dependence. Copyright © 2008 John Wiley & Sons, Ltd.     

Is it a short‐memory,long‐memory,or permanently Granger‐causation influence?

Exploring the Granger‐causation relationship is an important and interesting topic in the field of econometrics. In the traditional model we usually apply the short‐memory style to exhibit the relationship, but in practice there could be other different influence patterns. Besides the short‐memory relationship, Chen (2006) demonstrates a long‐memory relationship, in which a useful approach is provided for estimation where the time series are not necessarily fractionally co‐integrated. In that paper two different relationships (short‐memory and long‐memory relationship) are regarded whereby the influence flow is decayed by geometric, or cutting off, or harmonic sequences. However, it limits the model to the stationary relationship. This paper extends the influence flow to a non‐stationary relationship where the limitation is on ?0.5 ≤ d ≤ 1.0 and it can be used to detect whether the influence decays off (?0.5 ≤ d < 0.5) or is permanent (0.5 ≤ d ≤ 1.0). Copyright © 2008 John Wiley & Sons, Ltd.