nep-for New Economics Papers
on Forecasting
Issue of 2016‒11‒20
four papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Financial Vulnerability in the US: A Factor Model Approach By Hyeongwoo Kim; Wen Shi
  2. High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models By F. Lilla
  3. Forecast combination for euro area inflation: a cure in times of crisis? By Hubrich, Kirstin; Skudelny, Frauke
  4. Empirical analysis of daily cash flow time series and its implications for forecasting By Francisco Salas-Molina; Juan A. Rodr\'iguez-Aguilar; Joan Serr\`a; Francisco J. Martin

  1. By: Hyeongwoo Kim; Wen Shi
    Abstract: This paper presents a factor-based forecasting model for the financial market vulnerability, measured by changes in the Cleveland Financial Stress Index (CFSI). We estimate latent common factors via the method of the principal components from 170 monthly frequency macroeconomic data in order to out-of-sample forecast the CFSI. Our factor models outperform both the random walk and the autoregressive benchmark models in out-of-sample predictability at least for the short-term forecast horizons, which is a desirable feature since financial crises often come to a surprise realization. Interestingly, the first common factor, which plays a key role in predicting the financial vulnerability index, seems to be more closely related with real activity variables rather than nominal variables. We also present a binary choice version factor model that estimates the probability of the high stress regime successfully.
    Keywords: Financial Stress Index; Method of the Principal Component; Out-of-Sample Forecast; Ratio of Root Mean Square Prediction Error; Diebold-Mariano-West Statistic; Ordered Probit Model
    JEL: E44 E47 G01 G17
    Date: 2016–11
  2. By: F. Lilla
    Abstract: Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem, such as the discreteness of the data, the properties of the trading mechanism and the existence of bid-ask spread. Moreover, these data are not always available and, even if they are, the asset’s liquidity may be not sufficient to allow for frequent transactions. This paper considers different variants of these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping prices and leverage effects for volatility. Findings suggest that GARJI model provides more accurate VaR measures for the S&P 500 index than RV models. Furthermore, the assumption of conditional normality is shown to be not sufficient to obtain accurate risk measures even if jump contribution is provided. More sophisticated models might address this issue, improving VaR results.
    JEL: C58 C53 C22 C01 C13
    Date: 2016–11
  3. By: Hubrich, Kirstin; Skudelny, Frauke
    Abstract: The period of extraordinary volatility in euro area headline inflation starting in 2007 raised the question whether forecast combination methods can be used to hedge against bad forecast performance of single models during such periods and provide more robust forecasts. We investigate this issue for forecasts from a range of short-term forecasting models. Our analysis shows that there is considerable variation of the relative performance of the different models over time. To take that into account we suggest employing performance-based forecast combination methods, in particular one with more weight on the recent forecast performance. We compare such an approach with equal forecast combination that has been found to outperform more sophisticated forecast combination methods in the past, and investigate whether it can improve forecast accuracy over the single best model. The time-varying weights assign weights to the economic interpretations of the forecast stemming from different models. We also include a number of benchmark models in our analysis. The combination methods are evaluated for HICP headline inflation and HICP excluding food and energy. We investigate how forecast accuracy of the combination methods differs between pre-crisis times, the period after the global financial crisis and the full evaluation period including the global financial crisis with its extraordinary volatility in inflation. Overall, we find that forecast combination helps hedge against bad forecast performance and that performance-based weighting outperforms simple averaging. JEL Classification: C32, C52, C53, E31, E37
    Keywords: euro area inflation, forecast combinations, forecast evaluation, forecasting
    Date: 2016–10
  4. By: Francisco Salas-Molina; Juan A. Rodr\'iguez-Aguilar; Joan Serr\`a; Francisco J. Martin
    Abstract: Cash management models determine policies based either on the statistical properties of daily cash flow or on forecasts. Usual assumptions on the statistical properties of daily cash flow include normality, independence and stationarity. Surprisingly, little empirical evidence confirming these assumptions has been provided. In this work, we provide a comprehensive study on 54 real-world daily cash flow data sets, which we also make publicly available. Apart from the previous assumptions, we also consider linearity, meaning that cash flow is proportional to a particular explanatory variable, and we propose a new cross-validated test for time series non-linearity. We further analyze the implications of all aforementioned assumptions for forecasting, showing that: (i) the usual assumption of normality, independence and stationarity hardly appear; (ii) non-linearity is often relevant for forecasting; and (iii) common data transformations such as outlier treatment and Box-Cox have little impact on linearity and normality. Our results highlight the utility of non-linear models as a justifiable alternative for time series forecasting.
    Date: 2016–11

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