In this issue we have: Variable Selection and Inference for Multi-period Forecasting Problems ; "Ripple Effects” and Forecasting Home Prices ; Identifying good inflation forecaster ; Forecasting a Large Dimensional Covariance Matrix of a Portfolio of Different Asset Classes ; and more.

- Variable Selection and Inference for Multi-period Forecasting Problems

Date: 2009-01 By: Pesaran, M.H.

Pick, A.

Timmermann, A.URL: http://d.repec.org/n?u=RePEc:cam:camdae:0901&r=for This paper conducts a broad-based comparison of iterated and direct multi-step forecasting approaches applied to both univariate and multivariate models. Theoretical results and Monte Carlo simulations suggest that iterated forecasts dominate direct forecasts when estimation error is a first-order concern, i.e. in small samples and for long forecast horizons. Conversely, direct forecasts may dominate in the presence of dynamic model misspecification. Empirical analysis of the set of 170 variables studied by Marcellino, Stock and Watson (2006) shows that multivariate information, introduced through a parsimonious factor-augmented vector autoregression approach, improves forecasting performance for many variables, particularly at short horizons. - "Ripple Effects" and Forecasting Home Prices in Los Angeles, Las Vegas, and Phoenix

Date: 2009-01 By: Rangan Gupta (Department of Economics, University of Pretoria)

Stephen M. Miller (Department of Economics, University of Nevada, Las Vegas)URL: http://d.repec.org/n?u=RePEc:nlv:wpaper:0902&r=for We examine the time-series relationship between housing prices in Los Angeles, Las Vegas, and Phoenix. First, temporal Granger causality tests reveal that Los Angeles housing prices cause housing prices in Las Vegas (directly) and Phoenix (indirectly). In addition, Las Vegas housing prices cause housing prices in Phoenix. Los Angeles housing prices prove exogenous in a temporal sense and Phoenix housing prices do not cause prices in the other two markets. Second, we calculate out-of-sample forecasts in each market, using various vector autoregessive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different cities. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 200 6:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points. Keywords: Ripple effect, Housing prices, Forecasting JEL: C32 R31 - Identifying good inflation forecaster

Date: 2008 By: Duasa, Jarita

Ahmad, NursilahURL: http://d.repec.org/n?u=RePEc:pra:mprapa:13302&r=for The objective of this paper is to identify the best indicator variable in forecasting inflation in Malaysia. Due to the fact that Malaysia experienced the rise of CPI by 4.8 percent in March 2006, the country's highest inflation rate in seven years, there is a need to foresee future trend of general price level. To determine whether certain indicator (variable) could predict inflation, we construct a simple forecasting model that incorporates the variable. We estimate a two-variable VECM model of quasi-tradable inflation using monthly data covering the period 1980:01 to 2006:12. We alternate between the following inflation indicators: commodity prices, financial indicators and economic activities. We evaluate each model using out-of-sample forecast. The study proposes that a simple model using industrial production index improves the accuracy of inflation forecasts. The results support our hypothesis. Keywords: Goods inflation; VECM ; Malaysian economy. JEL: C50 E31 C22 - Forecasting a Large Dimensional Covariance Matrix of a Portfolio of Different Asset Classes

Date: 2009-01 By: Lillie Lam (Research Department, Hong Kong Monetary Authority)

Laurence Fung (Research Department, Hong Kong Monetary Authority)

Ip-wing Yu (Research Department, Hong Kong Monetary Authority)URL: http://d.repec.org/n?u=RePEc:hkg:wpaper:0901&r=for In portfolio and risk management, estimating and forecasting the volatilities and correlations of asset returns plays an important role. Recently, interest in the estimation of the covariance matrix of large dimensional portfolios has increased. Using a portfolio of 63 assets covering stocks, bonds and currencies, this paper aims to examine and compare the predictive power of different popular methods adopted by i) market practitioners (such as the sample covariance, the 250-day moving average, and the exponentially weighted moving average); ii) some sophisticated estimators recently developed in the academic literature (such as the orthogonal GARCH model and the Dynamic Conditional Correlation model); and iii) their combinations. Based on five different criteria, we show that a combined forecast of the 250-day moving average, the exponentially weighted moving average and the orthogonal GARCH model consistently outperform s the other methods in predicting the covariance matrix for both one-quarter and one-year ahead horizons. Keywords: Volatility forecasting; Risk management; Portfolio management; Model evaluation JEL: G32 C52 - A REGRESSION-BASED METHODOLOGY FOR EFFICIENTLY BUILDING FUTURES' PORTFOLIOS

Date: 2009 By: Konstantinos Maris

Dimitra Koutsothymiou

Fotios Petropoulos

Eleni Petra

Panagiotis Evangelopoulos

Vassilios Assimakopoulos

Konstantinos NikolopoulosURL: http://d.repec.org/n?u=RePEc:uop:wpaper:0032&r=for Nowadays financial markets are facing continuous values' fluctuations, resulting in higher risks that eventually influence investors' decisions. In this article a methodology is proposed in order to efficiently build portfolios of futures. The new methodology is tested on data from the derivative indices FTSE/ASE-20 and FTSE/ASA MID 40 in Greece. The final result is an investment decision, based on forecasting the indices' direction. Both the statistical and economic significance of the methodology has been evaluated. Keywords: Greece, Decision Support, Options Trading, Forecasting, Regression, Directional Accuracy. - Forecasting Consumption Growth with the Real Term Structure

Date: 2008 By: Kwok Ping Tsang URL: http://d.repec.org/n?u=RePEc:vpi:wpaper:e07-14&r=for From the log-linearized consumption Euler equation, consumption growth of any horizon m is a function of the expected real return of maturity m, and they are linked through the elasticity of intertemporal substitution (EIS). Instead of using only the 1- period return and consumption growth, this result allows us to use the term structure of interest rates to identify the EIS. Using quarterly US data from 1954Q1 to 2007Q4, GMM results show that the real term structure is unrelated to future consumption growth: after controlling for small sample bias, we cannot reject the hypothesis that the EIS is zero. However, allowing a break in 1979 changes the results dramatically: the EIS is around 0.4 in the first period and it drops to around 0.2 in the second period. Not only is the EIS smaller, the out-sample forecasting power of ex post real return also drops in the second subsample compared to a simple AR(1) model for consumpti on growth. I find a lower EIS also for annual data. Keywords: Consumption Euler Equation, Term Structure of Interest Rates, Inflation, Forecast, Elasticity of Intertemporal Substitution, GMM - What Does the Yield Curve Tell Us About Exchange Rate Predictability?

Date: 2009 By: Yu-chin Chen

Kwok Ping TsangURL: http://d.repec.org/n?u=RePEc:vpi:wpaper:e07-15&r=for This paper uses information contained in the cross-country yield curves to test the asset-pricing approach to exchange rate determination, which models the nominal exchange rate as the discounted present value of its expected future fundamentals. Research on the term structure of interest rates has long argued that the yield curve contains information about future economic activity such as GDP growth and inflation. Bringing this lesson to the international context, we extract the Nelson-Siegel (1987) factors of relative level, slope, and curvature from cross-country yield differences to proxy expected movements in future exchange rate fundamentals. Using monthly data between 1985-2005 for the United Kingdom, Canada, Japan and the US, we show that the yield curve factors indeed can explain and predict bilateral exchange rate movements and excess currency returns one month to two years ahead. Out-of- sample analysis also sh ows the yield curve factors to outperform a random walk in forecasting short-term exchange rate returns. Keywords: Exchange Rate Forecasting, Term Structure of Interest Rates, Uncovered Interest, Parity - Forecasting temperature indices with timevarying long-memory models

Date: 2009-02 By: Massimiliano Caporin (University di Padova)

Paolo Paruolo (Università dell'Insubria)URL: http://d.repec.org/n?u=RePEc:pad:wpaper:0091&r=for This paper proposes structured parametrizations for multivariate volatility models, which use spatial weight matrices induced by economic proximity. These structured specifications aim at solving the curse of dimensionality problem, which limits feasibility of model-estimation to small cross-sections for unstructured models. Structured parametrizations possess the following four desirable properties: i) they are flexible, allowing for covariance spill-over; ii) they are parsimonious, being characterized by a number of parameters that grows only linearly with the cross-section dimension; iii) model parameters have a direct economic interpretation that reflects the chosen notion of economic classification; iv) model-estimation computations are faster than for unstructured specifications. We give examples of structured specifications for multivariate GARCH models as well as for Stochastic- and Realized-Volatility models. The paper also discusses how to construct spatial weight matrices that are time-varying and possibly derived from a set of covariates. Keywords: MGARCH, Stochastic Volatility, Realized Volatility, Spatial models, ANOVA JEL: C31 C32 G11 - A mathematical proof of the existence of trends in financial time series

Date: 2009 By: Michel Fliess (LIX – Laboratoire d'informatique de l'école polytechnique – CNRS : UMR7161 – Polytechnique – X, INRIA Saclay – Ile de France – ALIEN – INRIA – Polytechnique – X – CNRS : UMR – Ecole Centrale de Lille)

Cédric Join (INRIA Saclay – Ile de France – ALIEN – INRIA – Polytechnique – X – CNRS : UMR – Ecole Centrale de Lille, CRAN – Centre de recherche en automatique de Nancy – CNRS : UMR7039 – Université Henri Poincaré – Nancy I – Institut National Polytechnique de Lorraine – INPL)URL: http://d.repec.org/n?u=RePEc:hal:journl:inria-00352834_v1&r=for We are settling a longstanding quarrel in quantitative finance by proving the existence of trends in financial time series thanks to a theorem due to P. Cartier and Y. Perrin, which is expressed in the language of nonstandard analysis (Integration over finite sets, F. & M. Diener (Eds): Nonstandard Analysis in Practice, Springer, 1995, pp. 195–204). Those trends, which might coexist with some altered random walk paradigm and efficient market hypothesis, seem nevertheless difficult to reconcile with the celebrated Black-Scholes model. They are estimated via recent techniques stemming from control and signal theory. Several quite convincing computer simulations on the forecast of various financial quantities are depicted. We conclude by discussing the rôle of probability theory. Keywords: Financial time series; mathematical finance; technical analysis; trends; random walks; efficient markets; forecasting; volatility; heteroscedasticity; quickly fluctuating functions; low-pass filters; nonstandard analysis; operational calculus.

Taken from the NEP-FOR mailing list edited by Rob Hyndman.