nep-for New Economics Papers
on Forecasting
Issue of 2010‒12‒11
five papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting in the presence of recent structural change By Eklund, Jana; Kapetanios, George; Price, Simon
  2. Should macroeconomic forecasters use daily financial data and how? By Elena Andreou; Eric Ghysels; Andros Kourtellos
  3. Structural Breaks and GARCH Models of Stock Return Volatility: The Case of South Africa By Ali Babikir; Rangan Gupta; Chance Mwabutwa; Emmanuel Owusu-Sekyere
  4. Currency Forecasting using Multiple Kernel Learning with Financially Motivated Features By Tristan Fletcher; Zakria Hussain; John Shawe-Taylor
  5. South African Stock Return Predictability in the Context of Data Mining: The Role of Financial Variables and International Stock Returns By Rangan Gupta; Mampho P. Modise

  1. By: Eklund, Jana (Bank of England); Kapetanios, George (Queen Mary College, London); Price, Simon (Bank of England)
    Abstract: We examine how to forecast after a recent break. We consider monitoring for change and then combining forecasts from models that do and do not use data before the change; and robust methods, namely rolling regressions, forecast averaging over different windows and exponentially weighted moving average (EWMA) forecasting. We derive analytical results for the performance of the robust methods relative to a full-sample recursive benchmark. For a location model subject to stochastic breaks the relative mean square forecast error ranking is EWMA < rolling regression < forecast averaging. No clear ranking emerges under deterministic breaks. In Monte Carlo experiments forecast averaging improves performance in many cases with little penalty where there are small or infrequent changes. Similar results emerge when we examine a large number of UK and US macroeconomic series.
    Keywords: monitoring; recent structural change; forecast combination; robust forecasts
    JEL: C10 C59
    Date: 2010–12–02
  2. By: Elena Andreou; Eric Ghysels; Andros Kourtellos
    Abstract: We introduce easy to implement regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of MIDAS regressions. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters. Our findings show that while on average the predictive ability of all models worsens substantially following the financial crisis, the models we propose suffer relatively less losses than the traditional ones. Moreover, these predictive gains are primarily driven by the classes of government securities, equities, and especially corporate risk.
    Keywords: MIDAS, macro forecasting, leads, daily financial information, daily factors.
    Date: 2010–11
  3. By: Ali Babikir (Department of Economics, University of Pretoria and South African Treasury, Pretoria, South Africa); Rangan Gupta (Department of Economics, University of Pretoria); Chance Mwabutwa (Department of Economics, University of Pretoria and South African Treasury, Pretoria, South Africa); Emmanuel Owusu-Sekyere (Department of Economics, University of Pretoria and South African Treasury, Pretoria, South Africa)
    Abstract: This paper investigates the empirical relevance of structural breaks in forecasting stock return volatility using both in-sample and out-of-sample tests and daily returns for the Johannesburg Stock Exchange (JSE) All Share Index from 07/02/1995 to 08/25/2010. We find evidence of structural breaks in the unconditional variance of the stock returns series over the period, with high levels of persistence and variability in the parameter estimates of the GARCH (1, 1) model across the sub-samples defined by the structural breaks. This indicates that structural breaks are empirically relevant to stock return volatility in South Africa. In out-of-sample tests, we find that combining forecasts from different benchmark and competing models that accommodate structural breaks in volatility improves the accuracy of volatility forecasting. Furthermore, for shorter horizons, the MS-GARCH model better captures asymmetry in stock return volatility than the GJR-GARCH (1, 1) model, which better suited to longer horizons, but in general, the asymmetric models fail to outperform the GARCH (1,1) model.
    Keywords: stock return volatility, structural breaks, in-sample tests, out-of-sample tests, GARCH Models
    JEL: C22 C53 G11 G12
    Date: 2010–12
  4. By: Tristan Fletcher; Zakria Hussain; John Shawe-Taylor
    Abstract: Multiple Kernel Learning (MKL) is used to replicate the signal combination process that trading rules embody when they aggregate multiple sources of financial information when predicting an asset's price movements. A set of financially motivated kernels is constructed for the EURUSD currency pair and is used to predict the direction of price movement for the currency over multiple time horizons. MKL is shown to outperform each of the kernels individually in terms of predictive accuracy. Furthermore, the kernel weightings selected by MKL highlights which of the financial features represented by the kernels are the most informative for predictive tasks.
    Date: 2010–11
  5. By: Rangan Gupta (Department of Economics, University of Pretoria); Mampho P. Modise (Department of Economics, University of Pretoria and South African Treasury, Pretoria, South Africa)
    Abstract: In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines general-to-specific model selection with out-of-sample tests of predictive ability to analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-ofsample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-ofsample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon.
    Keywords: Stock return predictability, Financial variables, Nested models, In-sample tests, Out-of-sample tests, Data mining, General-to-specific model selection
    JEL: C22 C52 C53 G12 G14
    Date: 2010–12

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