Forecasting Australia's real house price index: A comparison of time series and machine learning methods

We employ 47 different algorithms to forecast Australian log real house prices and growth rates, and compare their ability to produce accurate out-of-sample predictions. The algorithms, which are specified in both single- and multi-equation frameworks, consist of traditional time series models, machine learning (ML) procedures, and deep learning neural networks. A method is adopted to compute iterated multistep forecasts from nonlinear ML specifications. While the rankings of forecast accuracy depend on the length of the forecast horizon, as well as on the choice of the dependent variable (log price or growth rate), a few generalizations can be made. For one- and two-quarter-ahead forecasts we find a large number of algorithms that outperform the random walk with drift benchmark. We also report several such outperformances at longer horizons of four and eight quarters, although these are not statistically significant at any conventional level. Six of the eight top forecasts (4 horizons × 2 dependent variables) are generated by the same algorithm, namely a linear support vector regressor (SVR). The other two highest ranked forecasts are produced as simple mean forecast combinations. Linear autoregressive moving average and vector autoregression models produce accurate olne-quarter-ahead predictions, while forecasts generated by deep learning nets rank well across medium and long forecast horizons.     

Power transformation models and volatility forecasting

This paper considers the forecast accuracy of a wide range of volatility models, with particular emphasis on the use of power transformations. Where one‐period‐ahead forecasts are considered, the power autoregressive models are ranked first by a range of error metrics. Over longer forecast horizons, however, generalized autoregressive conditional heteroscedasticity models are preferred. A value‐at‐risk‐based forecast assessment indicates that, while the forecast errors are independent, they are not independent and identically distributed, although this latter result is sensitive to the choice of forecast horizon. Our results are robust across a number of different asset markets. Copyright © 2008 John Wiley & Sons, Ltd.     

Exchange rate forecasting and the performance of currency portfolios

We examine the potential gains of using exchange rate forecast models and forecast combination methods in the management of currency portfolios for three exchange rates: the euro versus the US dollar, the British pound, and the Japanese yen. We use a battery of econometric specifications to evaluate whether optimal currency portfolios implied by trading strategies based on exchange rate forecasts outperform single currencies and the equally weighted portfolio. We assess the differences in profitability of optimal currency portfolios for different types of investor preferences, two trading strategies, mean squared error‐based composite forecasts, and different forecast horizons. Our results indicate that there are clear benefits of integrating exchange rate forecasts from state‐of‐the‐art econometric models in currency portfolios. These benefits vary across investor preferences and prediction horizons but are rather similar across trading strategies.     

Linear constraints,robust-weighting and efficient composite modeling

Recent studies have shown that composite forecasting produces superior forecasts when compared to individual forecasts. This paper extends the existing literature by employing linear constraints and robust regression techniques in composite model building. Security analysts forecasts may be improved when combined with time series forecasts for a diversified sample of 261 firms with a 1980-1982 post-sample estimation period. The mean square error of analyst forecasts may be reduced by combining analyst and univariate time series model forecasts in constrained and unconstrained ordinary least squares regression models. These reductions are very interesting when one finds that the univariate time series model forecasts do not substantially deviate from those produced by ARIMA (0,1,1) processes. Moreover, security analysts' forecast errors may be significantly reduced when constrained and unconstrained robust regression analyses are employed.     

Does Information Help Intra‐Day Volatility Forecasts?

While much research related to forecasting return volatility does so in a univariate setting, this paper includes proxies for information flows to forecast intra‐day volatility for the IBEX 35 futures market. The belief is that volume or the number of transactions conveys important information about the market that may be useful in forecasting. Our results suggest that augmenting a variety of GARCH‐type models with these proxies lead to improved forecasts across a range of intra‐day frequencies. Furthermore, our results present an interesting picture whereby the PARCH model generally performs well at the highest frequencies and shorter forecasting horizons, whereas the component model performs well at lower frequencies and longer forecast horizons. Both models attempt to capture long memory; the PARCH model allows for exponential decay in the autocorrelation function, while the component model captures trend volatility, which dominates over a longer horizon. These characteristics are likely to explain the success of each model. Copyright © 2013 John Wiley & Sons, Ltd.     

Combination of Forecasts across Estimation Windows: An Application to Air Travel Demand

This paper applies combining forecasts of air travel demand generated from the same model but over different estimation windows. The combination approach used resorts to Pesaran and Pick (Journal of Business Economics and Statistics 2011; 29 : 307–318), but the empirical application is extended in several ways. The forecasts are based on a seasonal Box–Jenkins model (SARIMA), which is adequate to forecast monthly air travel demand with distinct seasonal patterns at the largest German airport: Frankfurt am Main. Furthermore, forecasts with forecast horizons from 1 to 12 months ahead, which are based on different average estimation windows, expanding windows and single rolling windows, are compared with baseline forecasts based on an expanding window of the observations after a structural break. The forecast exercise shows that the average window forecasts mostly outperform the alternative single window forecasts. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting exchange rate volatility: a multiple horizon comparison using historical,realized and implied volatility measures

Recent studies suggest realized volatility provides forecasts that are as good as option‐implied volatilities, with improvement stemming from the use of high‐frequency data instead of a long‐memory specification. This paper examines whether volatility persistence can be captured by a longer dataset consisting of over 15 years of intra‐day data. Volatility forecasts are evaluated using four exchange rates (AUD/USD, EUR/USD, GBP/USD, USD/JPY) over horizons ranging from 1 day to 3 months, using an expanded set of short‐range and long‐range dependence models. The empirical results provide additional evidence that significant incremental information is found in historical forecasts, beyond the implied volatility information for all forecast horizons. Copyright © 2008 John Wiley & Sons, Ltd.     

Conditional predictability of daily exchange rates

At what forecast horizon is one time series more predictable than another? This paper applies the Diebold–Kilian conditional predictability measure to assess the out‐of‐sample performance of three alternative models of daily GBP/USD and DEM/USD exchange rate returns. Predictability is defined as a non‐linear statistic of a model's relative expected losses at short and long forecast horizons, allowing flexible choice of both the estimation procedure and loss function. The long horizon is set to 2 weeks and one month ahead and forecasts evaluated according to MSE loss. Bootstrap methodology is used to estimate the data's conditional predictability using GARCH models. This is then compared to predictability under a random walk and a model using the prediction bias in uncovered interest parity (UIP). We find that both exchange rates are less predictable using GARCH than using a random walk, but they are more predictable using UIP than a random walk. Predictability using GARCH is relatively higher for the 2‐weeks‐than for the 1‐month long forecast horizon. Comparing the results using a random walk to that using UIP reveals ‘pockets’ of predictability, that is, particular short horizons for which predictability using the random walk exceeds that using UIP, or vice versa. Overall, GBP/USD returns appear more predictable than DEM/USD returns at short horizons. Copyright © 2002 John Wiley & Sons, Ltd.     

Collinearity and forecasting

Old saw: Collinearity will not harm forecasts as long as it continues into the forecast period. If, however, collinearity is unlikely to continue but has already harmed estimation, then corrective action (introduction of prior information) to improve estimates should improve forecasts. This paper marshals the diagnostic information needed to assess collinearity's continuance and, when required, to direct meaningful corrective action. The process is illustrated by an example involving forecasts using energy prices.     

Beating the VAR: Improving Swedish GDP Forecasts Using Error and Intercept Corrections

This paper examines the forecast accuracy of an unrestricted vector autoregressive (VAR) model for GDP, relative to a comparable vector error correction model (VECM) that recognizes that the data are characterized by co‐integration. In addition, an alternative forecast method, intercept correction, is considered for further comparison. Recursive out‐of‐sample forecasts are generated for both models and forecast techniques. The generated forecasts for each model are objectively evaluated by a selection of evaluation measures and equal accuracy tests. The result shows that the VECM consistently outperforms the VAR models. Further, intercept correction enhances the forecast accuracy when applied to the VECM, whereas there is no such indication when applied to the VAR model. For certain forecast horizons there is a significant difference in forecast ability between the intercept corrected VECM compared to the VAR model. Copyright © 2015 John Wiley & Sons, Ltd.     

Model specification and forecasting foreign exchange rates with vector autoregressions

This study examines the forecasting accuracy of alternative vector autoregressive models each in a seven‐variable system that comprises in turn of daily, weekly and monthly foreign exchange (FX) spot rates. The vector autoregressions (VARs) are in non‐stationary, stationary and error‐correction forms and are estimated using OLS. The imposition of Bayesian priors in the OLS estimations also allowed us to obtain another set of results. We find that there is some tendency for the Bayesian estimation method to generate superior forecast measures relatively to the OLS method. This result holds whether or not the data sets contain outliers. Also, the best forecasts under the non‐stationary specification outperformed those of the stationary and error‐correction specifications, particularly at long forecast horizons, while the best forecasts under the stationary and error‐correction specifications are generally similar. The findings for the OLS forecasts are consistent with recent simulation results. The predictive ability of the VARs is very weak. Copyright © 2001 John Wiley & Sons, Ltd.     

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd.     

The Role of High‐Frequency Intra‐daily Data,Daily Range and Implied Volatility in Multi‐period Value‐at‐Risk Forecasting

This paper assesses the informational content of alternative realized volatility estimators, daily range and implied volatility in multi‐period out‐of‐sample Value‐at‐Risk (VaR) predictions. We use the recently proposed Realized GARCH model combined with the skewed Student's t distribution for the innovations process and a Monte Carlo simulation approach in order to produce the multi‐period VaR estimates. Our empirical findings, based on the S&P 500 stock index, indicate that almost all realized and implied volatility measures can produce statistically and regulatory precise VaR forecasts across forecasting horizons, with the implied volatility being especially accurate in monthly VaR forecasts. The daily range produces inferior forecasting results in terms of regulatory accuracy and Basel II compliance. However, robust realized volatility measures, which are immune against microstructure noise bias or price jumps, generate superior VaR estimates in terms of capital efficiency, as they minimize the opportunity cost of capital and the Basel II regulatory capital. Copyright © 2013 John Wiley & Sons, Ltd.     

Direct multiperiod forecasting for algorithmic trading

This paper examines the performance of iterated and direct forecasts for the number of shares traded in high‐frequency intraday data. Constructing direct forecasts in the context of formulating volume weighted average price trading strategies requires the generation of a sequence of multistep‐ahead forecasts. I discuss nonlinear transformations to ensure nonnegative forecasts and lag length selection for generating a sequence of direct forecasts. In contrast to the literature based on low‐frequency macroeconomic data, I find that direct multiperiod forecasts can outperform iterated forecasts when the conditioning information set is dynamically updated in real time.     

Correcting the January optimism effect

Each month, various professional forecasters give forecasts for next year's real gross domestic product (GDP) growth and unemployment. January is a special month, when the forecast horizon moves to the following calendar year. Instead of deleting the January data when analyzing forecast updates, I propose a periodic version of a test regression for weak-form efficiency. An application of this periodic model for many forecasts across a range of countries shows that in January GDP forecast updates are positive, whereas the forecast updates for unemployment are negative. I document that this January optimism about the new calendar year is detrimental to forecast accuracy. To empirically analyze Okun's law, I also propose a periodic test regression, and its application provides more support for this law.     

On long memory origins and forecast horizons

Most long memory forecasting studies assume that long memory is generated by the fractional difference operator. We argue that the most cited theoretical arguments for the presence of long memory do not imply the fractional difference operator and assess the performance of the autoregressive fractionally integrated moving average (ARFIMA) model when forecasting series with long memory generated by nonfractional models. We find that ARFIMA models dominate in forecast performance regardless of the long memory generating mechanism and forecast horizon. Nonetheless, forecasting uncertainty at the shortest forecast horizon could make short memory models provide suitable forecast performance, particularly for smaller degrees of memory. Additionally, we analyze the forecasting performance of the heterogeneous autoregressive (HAR) model, which imposes restrictions on high-order AR models. We find that the structure imposed by the HAR model produces better short and medium horizon forecasts than unconstrained AR models of the same order. Our results have implications for, among others, climate econometrics and financial econometrics models dealing with long memory series at different forecast horizons.     

Identification of asymmetric prediction intervals through causal forces

When causal forces are specified, the expected direction of the trend can be compared with the trend based on extrapolation. Series in which the expected trend conflicts with the extrapolated trend are called contrary series. We hypothesized that contrary series would have asymmetric forecast errors, with larger errors in the direction of the expected trend. Using annual series that contained minimal information about causality, we examined 671 contrary forecasts. As expected, most (81%) of the errors were in the direction of the causal forces. Also as expected, the asymmetries were more likely for longer forecast horizons; for six‐year‐ahead forecasts, 89% of the forecasts were in the expected direction. The asymmetries were often substantial. Contrary series should be flagged and treated separately when prediction intervals are estimated, perhaps by shifting the interval in the direction of the causal forces. Copyright © 2001 John Wiley & Sons, Ltd.     

When are Direct Multi‐step and Iterative Forecasts Identical?

Although both direct multi‐step‐ahead forecasting and iterated one‐step‐ahead forecasting are two popular methods for predicting future values of a time series, it is not clear that the direct method is superior in practice, even though from a theoretical perspective it has lower mean squared error (MSE). A given model can be fitted according to either a multi‐step or a one‐step forecast error criterion, and we show here that discrepancies in performance between direct and iterative forecasting arise chiefly from the method of fitting, and is dictated by the nuances of the model's misspecification. We derive new formulas for quantifying iterative forecast MSE, and present a new approach for assessing asymptotic forecast MSE. Finally, the direct and iterative methods are compared on a retail series, which illustrates the strengths and weaknesses of each approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Can urban coffee consumption help predict US inflation?

Motivated by the importance of coffee to Americans and the significance of the coffee subsector to the US economy, we pursue three notable innovations. First, we augment the traditional Phillips curve model with the coffee price as a predictor, and show that the resulting model outperforms the traditional variant in both in‐sample and out‐of‐sample predictability of US inflation. Second, we demonstrate the need to account for the inherent statistical features of predictors such as persistence, endogeneity, and conditional heteroskedasticity effects when dealing with US inflation. Consequently, we offer robust illustrations to show that the choice of estimator matters for improved US inflation forecasts. Third, the proposed augmented Phillips curve also outperforms time series models such as autoregressive integrated moving average and the fractionally integrated version for both in‐sample and out‐of‐sample forecasts. Our results show that augmenting the traditional Phillips curve with the urban coffee price will produce better forecast results for US inflation only when the statistical effects are captured in the estimation process. Our results are robust to alternative measures of inflation, different data frequencies, higher order moments, multiple data samples and multiple forecast horizons.     

Do monetary policy transparency and central bank communication reduce interest rate disagreement?

Yes. This study produces evidence that monetary policy transparency and communication policy of the Bank of England have information content in reducing disagreement about interest rate forecasts. Different from most extant studies employing the transparency index derived from official documents of the central banks, this study extends the literature by using a recently developed market-based monetary transparency index. Moreover, this study analyzes forecast disagreement in a multivariate perspective based on survey data of short- and long-term rates over short and long horizons. This study characterizes several patterns on forecast disagreement related to maturities of interest rates, forecast horizons, recessions, forward guidance, credibility, transparency, and communication policy. Interestingly, disagreement among the Monetary Policy Committee in policy rate decisions is associated with lower disagreement among professional forecasters on interest rate outlook, whereas neither announcement of changes in policy rates nor publication of inflation reports affects forecast disagreement. These results have important implications for monetary policymakers in managing market expectations of interest rates.