nep-for New Economics Papers
on Forecasting
Issue of 2019‒07‒15
seven papers chosen by
Rob J Hyndman
Monash University

  1. Realized variance modeling: decoupling forecasting from estimation By Fabrizio Cipollini; Giampiero M. Gallo; Alessandro Palandri
  2. Improved Forecasting of Cryptocurrency Price using Social Signals By Maria Glenski; Tim Weninger; Svitlana Volkova
  3. Exchange rate forecasting on a napkin By Michele Ca' Zorzi; Micha􏰀l Rubaszek
  4. Investor Sentiment as a Predictor of Market Returns By Kim Kaivanto; Peng Zhang
  5. Forecasting security's volatility using low-frequency historical data, high-frequency historical data and option-implied volatility By Huiling Yuan; Yong Zhou; Zhiyuan Zhang; Xiangyu Cui
  6. Forecasting the Remittances of the Overseas Filipino Workers in the Philippines By Merry Christ E. Manayaga; Roel F. Ceballos
  7. Simulation smoothing for nowcasting with large mixed-frequency VARs By Sebastian Ankargren; Paulina Jon\'eus

  1. By: Fabrizio Cipollini (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze); Giampiero M. Gallo (Italian Court of Audits, and New York University in Florence); Alessandro Palandri (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze)
    Abstract: In this paper we evaluate the in-sample fit and out-of-sample forecasts of various combinations of realized variance models and estimation criteria . Our empirical findings highlight that: independently of the econometrician’s forecasting loss function, certain estimation criteria perform significantly better than others; the simple ARMA modeling of the log realized variance generates superior forecasts than the HAR family, for any of the forecasting loss functions considered; the (2,1) parameterizations with negative lag-2 coefficient emerge as the benchmark specifications generating the best forecasts and approximating long-run dependence as well as the HAR family.
    Keywords: Variance modeling; Variance forecasting; Heterogeneous Autoregressive (HAR) model; Multiplicative Error Model (MEM); Realized variance space
    JEL: C32 C53 C58 G17
    Date: 2019–07
  2. By: Maria Glenski; Tim Weninger; Svitlana Volkova
    Abstract: Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with significant political and economic implications. In this paper we leverage and contrast the predictive power of social signals, specifically user behavior and communication patterns, from multiple social platforms GitHub and Reddit to forecast prices for three cyptocurrencies with high developer and community interest - Bitcoin, Ethereum, and Monero. We evaluate the performance of neural network models that rely on long short-term memory units (LSTMs) trained on historical price data and social data against price only LSTMs and baseline autoregressive integrated moving average (ARIMA) models, commonly used to predict stock prices. Our results not only demonstrate that social signals reduce error when forecasting daily coin price, but also show that the language used in comments within the official communities on Reddit (r/Bitcoin, r/Ethereum, and r/Monero) are the best predictors overall. We observe that models are more accurate in forecasting price one day ahead for Bitcoin (4% root mean squared percent error) compared to Ethereum (7%) and Monero (8%).
    Date: 2019–07
  3. By: Michele Ca' Zorzi (European Central Bank); Micha􏰀l Rubaszek (SGH Warsaw School of Economics)
    Abstract: This paper shows that there are two regularities in foreign exchange markets in advanced countries with flexible regimes. First, real exchange rates are mean-reverting, as implied by the Purchasing Power Parity model. Second, the adjustment takes place via nominal exchange rates. These features of the data can be exploited, even on the back of a napkin, to generate nominal exchange rate forecasts that outperform the random walk. The secret is to avoid estimating the pace of mean reversion and assume that relative prices are unchanged. Direct forecasting or panel data techniques are better than the random walk but fail to beat this simple calibrated model.
    Keywords: exchange rates, forecasting, Purchasing Power Parity, panel data, mean reversion
    JEL: C32 F31 F37 F41
  4. By: Kim Kaivanto; Peng Zhang
    Abstract: Investor sentiment's effect on asset prices has been studied extensively to date, without delivering consistent results across samples and datasets. We investigate the asset-pricing impacts of eight widely cited investor-sentiment indicators (one direct, six indirect, one composite), within a unified long-horizon regression framework, predicting real NYSE-index returns over horizon lengths of 1, 3, 12, 24, 36, and 48 months. Results reveal that three of the non-composite indicators have consistent predictive power: the Michigan Index of Consumer Sentiment (MICS), IPO volume (NIPO), and the dividend premium (PDND). This finding has implications for the widely cited Baker-Wurgler first principal component (SFPC) composite indicator, which extracts information from the full set of six indirect indicators. As the diffusion-index literature shows, this type of wide-net approach is likely to impound idiosyncratic noise into the composite summary indicator, exacerbating forecasting errors. Therefore we create a new `targeted' composite indicator from the first principal component of the three indicators that perform well in long-horizon regressions, i.e. MICS, NIPO, and PDND. The resulting targeted composite indicator outperforms SFPC in a market-returns prediction horse race. Whereas SFPC primarily predicts Equally Weighted Returns (EWR) rather than Value Weighted Returns (VWR), our new sentiment indicator performs better than SFPC in predicting both VWR and EWR. This improved performance is due in part to a reduction in overfitting, and in part to incorporation of the direct sentiment indicator MICS.
    Keywords: investor sentiment, market return, predictability, long-horizon regression, bootstrap diffusion index, composite index, overfitting
    JEL: G12 G17
    Date: 2019
  5. By: Huiling Yuan; Yong Zhou; Zhiyuan Zhang; Xiangyu Cui
    Abstract: Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security's volatility. In this paper, we propose two econometric models, which integrate three information sources. In GARCH-It\^{o}-OI model, we assume that the option-implied volatility can influence the security's future volatility, and the option-implied volatility is treated as an observable exogenous variable. In GARCH-It\^{o}-IV model, we assume that the option-implied volatility can not influence the security's volatility directly, and the relationship between the option-implied volatility and the security's volatility is constructed to extract useful information of the underlying security. After providing the quasi-maximum likelihood estimators for the parameters and establishing their asymptotic properties, we also conduct a series of simulation analysis and empirical analysis to compare the proposed models with other popular models in the literature. We find that when the sampling interval of the high-frequency data is 5 minutes, the GARCH-It\^{o}-OI model and GARCH-It\^{o}-IV model has better forecasting performance than other models.
    Date: 2019–07
  6. By: Merry Christ E. Manayaga; Roel F. Ceballos
    Abstract: This study aims to find a Box-Jenkins time series model for the monthly OFW's remittance in the Philippines. Forecasts of OFW's remittance for the years 2018 and 2019 will be generated using the appropriate time series model. The data were retrieved from the official website of Bangko Sentral ng Pilipinas. There are 108 observations, 96 of which were used in model building and the remaining 12 observations were used in forecast evaluation. ACF and PACF were used to examine the stationarity of the series. Augmented Dickey Fuller test was used to confirm the stationarity of the series. The data was found to have a seasonal component, thus, seasonality has been considered in the final model which is SARIMA (2,1,0)x(0,0,2)_12. There are no significant spikes in the ACF and PACF of residuals of the final model and the L-jung Box Q* test confirms further that the residuals of the model are uncorrelated. Also, based on the result of the Shapiro-Wilk test for the forecast errors, the forecast errors can be considered a Gaussian white noise. Considering the results of diagnostic checking and forecast evaluation, SARIMA (2,1,0)x(0,0,2)_12 is an appropriate model for the series. All necessary computations were done using the R statistical software.
    Date: 2019–06
  7. By: Sebastian Ankargren; Paulina Jon\'eus
    Abstract: There is currently an increasing interest in large vector autoregressive (VAR) models. VARs are popular tools for macroeconomic forecasting and use of larger models has been demonstrated to often improve the forecasting ability compared to more traditional small-scale models. Mixed-frequency VARs deal with data sampled at different frequencies while remaining within the realms of VARs. Estimation of mixed-frequency VARs makes use of simulation smoothing, but using the standard procedure these models quickly become prohibitive in nowcasting situations as the size of the model grows. We propose two algorithms that alleviate the computational efficiency of the simulation smoothing algorithm. Our preferred choice is an adaptive algorithm, which augments the state vector as necessary to sample also monthly variables that are missing at the end of the sample. For large VARs, we find considerable improvements in speed using our adaptive algorithm. The algorithm therefore provides a crucial building block for bringing the mixed-frequency VARs to the high-dimensional regime.
    Date: 2019–07

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