Conditional variance forecasts for long-term stock returns

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step procedure a fully nonparametric local-linear smoother and choose the set of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much less important at longer horizons regardless of the chosen model and that the homoscedastic historical average of the squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the one-year and five-year horizon

Forecasting House Prices in Germany

In the academic debate there is a broad consensus that house price fluctuations have a substantial impact on financial stability and real economic activity. Therefore, it is important to have timely information on actual and expected house price developments. The aim of this paper is to measure the latest price movements in different real estate markets in Germany and forecast near-term price developments. Therefore we construct hedonic house price indices based on real estate advertisements on the internet platform ImmobilienScout24. Then, starting with a naive AR(p) model as a benchmark, we investigate whether VAR and ARDL models using additional macroeconomic information can improve the forecasting performance as measured by the mean squared forecast error (MSFE). While these models reduce the forecast error only slightly, forecast combination approaches enhance the predictive power considerably.Die Finanz- und Wirtschaftskrise hat erneut gezeigt, dass Preisfluktuationen am Immobilienmarkt nicht nur einen substanziellen Einfluss auf die Finanzmarktstabilität sondern auch auf die gesamtwirtschaftliche Aktivität ausüben. Demnach ist es aus makroökonomischer Sicht wichtig, Immobilienpreisentwicklungen frühzeitig erkennen und akkurat prognostizieren zu können. Ziel dieser Arbeit ist es, Preisentwicklungen für Deutschland in verschieden Segmenten des Immobilienmarktes am aktuellen Rand zu erfassen und für die kurze Frist von bis zu sechs Monaten zu prognostizieren. Basierend auf monatlich verfügbaren Immobilieninseraten auf der Internetplattform von ImmobilienScout24 werden dazu in einem ersten Schritt hedonische Preisindizes gebildet. In einem zweiten Schritt wird die Entwicklung dieser Preisindizes prognostiziert. Beginnend mit einem einfachen AR(p)-Prozess wird der Informationsgehalt verschiedener zusätzlicher Indikatoren mit Hilfe von VAR und ARDL-Modellen untersucht. Während sich der durchschnittliche quadratische Schätzfehler in den einzelnen Modellen im Vergleich zum AR-Modell nur geringfügig verringert, zeigt sich, dass mit Hilfe von geeigneten Prognosekombinationen die Prognosegüte signifikant verbessert werden kann

Modelling UK house prices with structural breaks and conditional variance analysis

This paper differs from previous research by examining the existence of structural breaks in the UK regional house prices as well as in the prices of the different property types (flats, terraced, detached and semi-detached houses) in the UK as a whole, motivated by the uncertainty in the UK housing market and various financial events that may lead to structural changes within the housing market. Our paper enhances the conventional unit root tests by allowing for structural breaks, while including structural break tests strengthens our analysis. Our empirical results support the existence of structural breaks in the mean equation in seven out of thirteen regions of the UK as well as in three out of four property types, and in the variance equation in six regions and three property types. In addition, using a multivariate GARCH approach we examine both the behaviour of variances and covariances of the house price returns over time. Our results have significant implications for appropriate economic policy selection and investment management

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Does the Nominal Exchange Rate Regime Affect the Real Interest Parity Condition?

The real interest partity (RIP) condition combines two cornerstones in international finance, uncovered interest parity (UIP) and ex ante purchasing power parity (PPP). The extent of deviation from RIP is therefore an indicator of the lack of product and financial market integration. This paper investigates whether the nominal exchange rate regime has an impact on RIP. The analysis is based on 15 annual real interest rates and covers a long time span, 1870-2006. Four subperiods are distinguished and linked to fixed and flexible exchange rate regimes: the Gold Standard, the interwar float, the Bretton Woods system and the current managed float. Panel integration techniques are used to increase the power of the tests. Cross section correlation is embedded via common factor structures. The results suggest that RIP holds as a long run condition irrespectively of the exchange rate regimes. Adjustment towards RIP is affected by the institutional framework and the historical episode. Half lives of shocks tend to be lower under fixed exchange rates and in the first part of the sample, probably due to higher price flexibility before WWII. Although barriers to foreign trade and capital controls were substantially removed after the collapse of the Bretton Woods system, they did not lead to lower half lives during the managed float

Forecasting: theory and practice

peer reviewedForecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Cliometrics and Time Series Econometrics: Some Theory and Applications

The paper discusses a range of modern time series methods that have become popular in the past 20 years and considers their usefulness for cliometrics research both in theory and via a range of applications. Issues such as, spurious regression, unit roots, cointegration, persistence, causality, structural time series methods, including time varying parameter models, are introduced as are the estimation and testing implications that they involve. Applications include a discussion of the timing and potential causes of the British Industrial Revolution, income „convergence ‟ and the long run behaviour of English Real Wages 1264 – 1913. Finally some new and potentially useful developments are discussed including the mildly explosive processes; graphical modelling and long memory