nep-for New Economics Papers
on Forecasting
Issue of 2008‒06‒27
six papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting with the age-period-cohort model and the extended chain-ladder model By D. Kuang; Bent Nielsen; J. P. Nielsen
  2. Stubborn Sell-Side Stock Analysts By John Beshears; Katherine L. Milkman
  3. Interest Rate Forecasts: A Pathology By Wen Bin Lim; Charles Goodhart
  4. On Forecasting Daily Stock Volatility: the Role of On Forecasting Daily Stock Volatility: the Role of Intraday Information and Market Conditions By Marwan Izzeldin; Ana-Maria Fuertes; Elena Kalotychou
  5. The Role of Implied Volatility in Forecasting Future Realized Volatility and Jumps in Foreign Exchange, Stock, and Bond Markets By Thomas Busch; Thomas Busch; Bent Jesper Christensen; Morten Ørregaard Nielsen
  6. Forecasting Bankruptcy and Physical Default Intensity By Ping Zhou

  1. By: D. Kuang (Department of Statistics, University of Oxford); Bent Nielsen (Nuffield College, Oxford University); J. P. Nielsen (Cass Business School)
    Abstract: We consider forecasting from age-period-cohort models, as well as from the extended chain-ladder model. The parameters of these models are known only to be identified up to linear trends. Forecasts from such models may therefore depend on arbitrary linear trends. A condition for invariant forecasts is proposed. A number of standard forecast models are analysed.
    Keywords: Age-period-cohort model; Chain-ladder model; Forecasting; Identification.
    Date: 2008–06–16
  2. By: John Beshears (Harvard Business School); Katherine L. Milkman (Harvard Business School)
    Abstract: We study how sell-side stock analysts whose views on a company’s future differ from those of their peers update their predictions in response to new information. When an analyst makes an out-of-consensus forecast for a company’s quarterly earnings and turns out to be incorrect, we find that the analyst stubbornly persists in maintaining her out-of-consensus view on the company. Relative to an analyst who was close to the consensus, the out-of-consensus analyst adjusts her forecasts for subsequent quarters less in the direction of the earnings surprise. On average, this stubbornness reduces forecasting accuracy, so it does not seem to reflect superior private information. We discuss the factors that are likely to drive stubbornness, including the possibility that the rewards for correct out-of-consensus forecasts are so large that analysts have an incentive to stand by extreme stock calls even in the face of contradictory evidence.
    Date: 2008–06
  3. By: Wen Bin Lim; Charles Goodhart
    Abstract: This is the first of three prospective papers examining how well forecasters can predict the future time path of short-term interest rates. Most prior work has been done using US data; in this exercise we use forecasts made for New Zealand (NZ) by the Reserve Bank of New Zealand (RBNZ), and those derived from money market yield curves in the UK. In this first exercise we broadly replicate recent US findings for NZ and UK, to show that such forecasts in NZ and UK have been excellent for the immediate forthcoming quarter, reasonable for the next quarter and useless thereafter. Moreover, when ex post errors are assessed depending on whether interest rates have been upwards, or downwards, trending, they are shown to have been biased and, apparently, inefficient. In the second paper we shall examine whether (NZ and UK) forecasts for inflation exhibit the same syndromes, and whether errors in inflation forecasts can help to explain errors in interest rate forecasts. In the third paper we shall set out an hypothesis to explain those findings, and examine whether the apparent ex post forecast inefficiencies may still be consistent with ex ante forecastefficiency. Even if the forecasts may be ex ante efficient, their negligible ex post forecasting ability suggests that, beyond a six months’ horizon from the forecast date, they would be better replaced by a simple ‘no-change thereafter’ assumption.
    Date: 2008–06
  4. By: Marwan Izzeldin; Ana-Maria Fuertes; Elena Kalotychou
    Abstract: Several recent studies advocate the use of nonparametric estimators of daily price vari- ability that exploit intraday information. This paper compares four such estimators, realised volatility, realised range, realised power variation and realised bipower variation, by examining their in-sample distributional properties and out-of-sample forecast ranking when the object of interest is the conventional conditional variance. The analysis is based on a 7-year sample of transaction prices for 14 NYSE stocks. The forecast race is conducted in a GARCH framework and relies on several loss functions. The realized range fares relatively well in the in-sample .t analysis, for instance, regarding the extent to which it brings normality in returns. However, overall the realised power variation provides the most accurate 1-day-ahead forecasts. Fore- cast combination of all four intraday measures produces the smallest forecast errors in about half of the sampled stocks. A market conditions analysis reveals that the additional use of intraday data on day t .. 1 to forecast volatility on day t is most advantageous when day t is a low volume or an up-market day. The results have implications for value-at-risk analysis.
    Keywords: C53; C32; C14.
    Date: 2008
  5. By: Thomas Busch; Thomas Busch; Bent Jesper Christensen; Morten Ørregaard Nielsen (School of Economics and Management, University of Aarhus, Denmark)
    Abstract: We study the forecasting of future realized volatility in the stock, bond, and for- eign exchange markets, as well as the continuous sample path and jump components of this, from variables in the information set, including implied volatility backed out from option prices. Recent nonparametric statistical techniques of Barndor¤-Nielsen & Shephard (2004, 2006) are used to separate realized volatility into its continuous and jump components, which enhances forecasting performance, as shown by Andersen, Bollerslev & Diebold (2005). We generalize the heterogeneous autoregressive (HAR) model of Corsi (2004) to include implied volatility as an additional regressor, and to the separate forecasting of the realized components. We also introduce a new vector HAR (VecHAR) model for the resulting simultaneous system, controlling for possible endogeneity issues in the forecasting equations. We show that implied volatility con- tains incremental information about future volatility relative to both continuous and jump components of past realized volatility. Indeed, in the foreign exchange market, implied volatility completely subsumes the information content of daily, weekly, and monthly realized volatility measures, when forecasting future realized volatility or its continuous component. In addition, implied volatility is an unbiased forecast of future realized volatility in the foreign exchange and stock markets. Perhaps surprisingly, the jump component of realized return volatility is, to some extent, predictable, and options appear to be calibrated to incorporate information about future jumps in all three markets.
    Keywords: Bipower variation, HAR, Heterogeneous Autoregressive Model, implied volatility, jumps, options, realized volatility, VecHAR, volatility forecasting
    JEL: C22 C32 F31 G1
    Date: 2007–06–06
  6. By: Ping Zhou
    Abstract: This report presents two of our investigations: one is to obtain an accurate forecast for the corporate bankruptcy; the other is to obtain a physical default intensity. Both investigations were based on the hazard model, using only firm-specific accounting variables as predictors. Different methods, such as the list-wise deleting, closest- value imputation and multiple imputation, were applied to tackling the problem of missing values. Our empirical studies showed that the multiple imputation performed the best amongst these methods and led to a forecasting model with economically reasonable predictors and corresponding estimates.
    Date: 2008–06

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