Journal of Forecasting最新文献

This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying parameter vector autoregressive model with stochastic volatility. Monte Carlo simulation shows that the novel model is well-suited to estimate missing monthly observations in an environment that is subject to parameter instability. In a real-time forecast exercise, the model delivers accurate now- and forecasts and, on average, outperforms its competitors. Particularly, inflation and unemployment rate forecasts are more precise.

Many empirical time series show periodic patterns. SARIMA models and exponential smoothing methods are classical approaches to account for seasonal dynamics. However, they allow to model just one periodic component, while several time series have multiple seasonality, with periodic components possibly tangled among them. To face this case, some seasonal-trend decomposition methods have been proposed in the literature, for example, the TBATS model, the MSTL model, the ADAM model, and the Prophet model, while SARIMA models have been quite neglected. To fill this gap, in this work, we suggest a suitable generalization of the SARIMA model, called mSARIMA, able to account for multiple seasonality. First, we define the model, describe its characteristics, and propose a test for residual multiperiodic correlation. Then, we analyze the predictive performance by comparing the mSARIMA model with other approaches, namely, the TBATS, MSTL, ADAM, and Prophet models, under different kinds of seasonality. The results suggest that when seasonality has a stochastic nature, mSARIMA models are more effective in predicting the series. However, if seasonality is basically deterministic, then the model decomposition approach is more suitable. Finally, we provide two comparative forecasting applications for the 5-min series of the number of calls handled by a large North American commercial bank and for the 10-min traffic data on the eastbound lanes of the Ventura Highway in Los Angeles.

The capital asset pricing model (CAPM) is readily used to capture a linear relationship between the daily returns of an asset and a market index. We extend this model to an intraday high-frequency setting by proposing a functional CAPM estimation approach. The functional CAPM is a stylized example of a function-on-function linear regression with a bivariate functional regression coefficient. The two-dimensional regression coefficient measures the cross-covariance between cumulative intraday asset returns and market returns. We apply it to the Standard and Poor's 500 index and its constituent stocks to demonstrate its practicality. We investigate the functional CAPM's in-sample goodness of fit and out-of-sample prediction for an asset's cumulative intraday return. The findings suggest that the proposed functional CAPM methods have superior model goodness of fit and forecast accuracy compared to the traditional CAPM empirical estimation. In particular, the functional methods produce better model goodness of fit and prediction accuracy for stocks traditionally considered less price efficient or more information opaque.

Reverse Unrestricted MIxed DAta Sampling (RU-MIDAS) regressions are used to model high-frequency responses by means of low-frequency variables. However, due to the periodic structure of RU-MIDAS regressions, the dimensionality grows quickly if the frequency mismatch between the high- and low-frequency variables is large. Additionally, the number of high-frequency observations available for estimation decreases. We propose to counteract this reduction in sample size by pooling the high-frequency coefficients and further reducing the dimensionality through a sparsity-inducing convex regularizer that accounts for the temporal ordering among the different lags. To this end, the regularizer prioritizes the inclusion of lagged coefficients according to the recency of the information they contain. We demonstrate the proposed method on two empirical applications, one on realized volatility forecasting with macroeconomic data and another on demand forecasting for a bicycle-sharing system with ridership data on other transportation types.

We analyze the predictive power of fundamentals versus random walk models for horizons from 1 to 24 months in an emerging market. Specifically, we investigate what fundamentals models outperform random walk during periods of appreciation and depreciation of the exchange rate. Furthermore, we analyze whether the fundamentals models that beat random walk contain information not considered by market expectations. Based on data from the Brazilian economy, the findings point out that some fundamentals models are useful for forecasting the exchange rate. The predictive power of fundamentals models increases in periods marked by a trend of currency appreciation or depreciation. In particular, the PPP-type fundamentals models have greater predictive power than the random walk and add information to market expectations for different time horizons and periods of exchange rate appreciation and depreciation.