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on Forecasting |
| By: | Sotiris Tsolacos (Centre for Spatial and Real Estate Economics (CSpREE)University of Reading); Chris Brooks (ICMA Centre, Henley Business School, University of Reading); Ogonna Nneji (ICMA Centre, Henley Business School, University of Reading) |
| Abstract: | This paper employs a probit model and a Markov switching model using information from the Conference Board Leading Indicator series to detect the turning points in four key US commercial rents series. We find that both the approaches based on the leading indicator have considerable power to predict changes in the direction of commercial rents up to two years ahead, exhibiting strong improvements over a naïve model, especially for the warehouse and apartment sectors. The empirical support for the adequacy of these prediction methodologies, from both in-sample and real time forecasting assessments, makes them a valuable tool to real estate professionals forecasting the US real estate markets. We find that while the Markov switching model nominally appears to be more successful in predicting periods of negative growth, it lags behind actual turnarounds in market outcomes whereas the probit is able to detect turning points several quarters ahead. |
| Keywords: | Leading indicator, US rents, turning point forecasting, direction prediction |
| JEL: | C53 C34 R39 |
| Date: | 2013–03 |
| URL: | http://d.repec.org/n?u=RePEc:rdg:icmadp:icma-dp2013-02&r=for |
| By: | Rangan Gupta; Shawkat Hammoudeh; Mampho P. Modise; Duc Khuong Nguyen |
| Abstract: | This article attempts to examine whether the equity premium in the United States can be predicted from a comprehensive |
| Keywords: | Equity premium forecasting; asset pricing model; economic uncertainty; business cycle. |
| JEL: | C52 C53 C58 E37 G17 |
| Date: | 2014–07–24 |
| URL: | http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-436&r=for |
| By: | David E. Allen (School of Accounting, Finance and Economics Edith Cowan University, Australia.); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute, The Netherlands, Department of Quantitative Economics, Complutense University of Madrid, and Institute of Economic Research, Kyoto University.); Marcel Scharth (Post Doctoral Fellow, Australian School of Business, University of New South Wales) |
| Abstract: | In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this inpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks. |
| Keywords: | Realized volatility; Volatility of volatility; Volatility risk; Value-at-risk; Forecasting; Conditional heteroskedasticity. |
| JEL: | C58 G12 |
| Date: | 2014–06 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1416&r=for |
| By: | Arthur Henriot |
| Abstract: | In a power system featuring a large share of intermittent renewable energy sources (RES) and inflexible thermal generators, efficiency gains on generation costs could be achieved by curtailing the production of RES. However, as RES feature very low variable production costs, over-curtailment can be costly. In this article, we use a stylised analytical model to assess this trade-off. We show that while curtailing RES when their variability is high and the system flexibility is low can reduce generation costs, the different stakeholders (consumers, dispatchable generators, RES) will not necessarily benefit from such measures. As a consequence, generators will opt for a sub-optimal level of curtailment, and this level of curtailment should rather be set by the TSO. Either incentive to provide the TSO with accurate forecasts of RES availability, or alternatively centralised forecasting by the TSO, should then be put into place to solve the resulting problem of asymmetry of information. |
| Keywords: | Market design, Curtailment, Large-scale renewables, Intermittency |
| JEL: | Q42 L94 |
| Date: | 2014–05 |
| URL: | http://d.repec.org/n?u=RePEc:rsc:rsceui:2014/57&r=for |