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on Forecasting |
| By: | Baumeister, Christiane; Guerin, Pierre |
| Abstract: | This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world GDP using mixed-frequency models. We find that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecast accuracy, while other monthly measures have more mixed success. This global economic conditions indicator contains valuable information also for assessing the current and future state of the economy for a set of individual countries and groups of countries. We use this indicator to track the evolution of the nowcasts for the US, the OECD area, and the world economy during the coronavirus pandemic and quantify the main factors driving the nowcasts. |
| Keywords: | Forecasting; global economic conditions; MIDAS models; Mixed frequency; Nowcasting; world GDP growth |
| JEL: | C22 C52 E37 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:15403&r= |
| By: | Matthieu Garcin |
| Abstract: | The fractional Brownian motion (fBm) extends the standard Brownian motion by introducing some dependence between non-overlapping increments. Consequently, if one considers for example that log-prices follow an fBm, one can exploit the non-Markovian nature of the fBm to forecast future states of the process and make statistical arbitrages. We provide new insights into forecasting an fBm, by proposing theoretical formulas for accuracy metrics relevant to a systematic trader, from the hit ratio to the expected gain and risk of a simple strategy. In addition, we answer some key questions about optimizing trading strategies in the fBm framework: Which lagged increments of the fBm, observed in discrete time, are to be considered? If the predicted increment is close to zero, up to which threshold is it more profitable not to invest? We also propose empirical applications on high-frequency FX rates, as well as on realized volatility series, exploring the rough volatility concept in a forecasting perspective. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2105.09140&r= |
| By: | A Fronzetti Colladon; B Guardabascio; R Innarella |
| Abstract: | Forecasting tourism demand has important implications for both policy makers and companies operating in the tourism industry. In this research, we applied methods and tools of social network and semantic analysis to study user-generated content retrieved from online communities which interacted on the TripAdvisor travel forum. We analyzed the forums of 7 major European capital cities, over a period of 10 years, collecting more than 2,660,000 posts, written by about 147,000 users. We present a new methodology of analysis of tourism-related big data and a set of variables which could be integrated into traditional forecasting models. We implemented Factor Augmented Autoregressive and Bridge models with social network and semantic variables which often led to a better forecasting performance than univariate models and models based on Google Trend data. Forum language complexity and the centralization of the communication network, i.e. the presence of eminent contributors, were the variables that contributed more to the forecasting of international airport arrivals. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2105.07727&r= |
| By: | Filippou, Ilias; Rapach, David; Taylor, Mark P; Zhou, Guofu |
| Abstract: | We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfi tting, we use the elastic net to estimate a high-dimensional panel predictive regression and find that the resulting forecast consistently outperforms the naive no-change benchmark, which has proven difficult to beat in the literature. The forecast also markedly improves the performance of a carry trade portfolio, especially during and after the global financial crisis. When we allow for more complex deep learning models, nonlinearities do not appear substantial in the data. |
| Keywords: | carry trade; deep neural network; Elastic Net; exchange rate predictability |
| JEL: | C45 F31 F37 G11 G12 G15 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:15305&r= |
| By: | Afees A. Salisu (University of Ibadan); Rangan Gupta (University of Pretoria); Sayar Karmakar (University of Florida); Sonali Das (University of Pretoria) |
| Abstract: | In this paper we develop a proxy for global uncertainty based on the volatility of gold market over the annual period of 1311 to 2019, and then use this proxy metric to forecast historical growth rates for eight advance economies namely, France, Germany, Holland, Italy, Japan, Spain, the United Kingdom (UK), and the United States (US). We find that for the within-sample period, uncertainty negatively impacts output growth, but more importantly, over the out-of-sample period, gold market volatility produces statistically significant forecasting gains. Our findings are robust to an alternative measure of uncertainty based on the volatility of the changes in long-term sovereign real-rates over 1315 to 2019. These historical results have important implications for investors and policymakers in the current context in which high frequency gold price data is available. |
| Keywords: | Historical output growth, advance economies, gold market volatility, forecasting |
| JEL: | C22 C53 E32 Q02 |
| Date: | 2021–05–17 |
| URL: | http://d.repec.org/n?u=RePEc:cth:wpaper:gru_2021_017&r= |
| By: | M. Elshendy; A. Fronzetti Colladon; E. Battistoni; P. A. Gloor |
| Abstract: | This study looks for signals of economic awareness on online social media and tests their significance in economic predictions. The study analyses, over a period of two years, the relationship between the West Texas Intermediate daily crude oil price and multiple predictors extracted from Twitter, Google Trends, Wikipedia, and the Global Data on Events, Language, and Tone database (GDELT). Semantic analysis is applied to study the sentiment, emotionality and complexity of the language used. Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) models are used to make predictions and to confirm the value of the study variables. Results show that the combined analysis of the four media platforms carries valuable information in making financial forecasting. Twitter language complexity, GDELT number of articles and Wikipedia page reads have the highest predictive power. This study also allows a comparison of the different fore-sighting abilities of each platform, in terms of how many days ahead a platform can predict a price movement before it happens. In comparison with previous work, more media sources and more dimensions of the interaction and of the language used are combined in a joint analysis. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2105.09154&r= |