nep-for New Economics Papers
on Forecasting
Issue of 2019‒11‒25
five papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Aluminum Prices with Commodity Currencies By Pincheira, Pablo; Hardy, Nicolás
  2. A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression By Gary S. Anderson; Alena Audzeyeva
  3. Repeated Use of IMF-Supported Programs: Determinants and Forecasting By Martin Iseringhausen; Mwanza Nkusu; Wellian Wiranto
  4. Forecasting and stress testing with quantile vector autoregression By Chavleishvili, Sulkhan; Manganelli, Simone
  5. Predicting Indian stock market using the psycho-linguistic features of financial news By B. Shravan Kumar; Vadlamani Ravi; Rishabh Miglani

  1. By: Pincheira, Pablo; Hardy, Nicolás
    Abstract: In this paper we show that the exchange rates of some commodity exporter countries have the ability to predict the price of spot and future contracts of aluminum. This is shown with both in-sample and out-of-sample analyses. The theoretical underpinning of these results relies on the present-value model for exchange rate determination and on the tight connection between commodity prices and the currencies of commodity exporter countries. We show results using traditional statistical metrics of forecast accuracy: Mean Squared Prediction Error and Mean Directional Accuracy. We also show that the first principal component of our sample of exchange rates is a useful way to summarize the predictive information contained in our set of commodity currencies.
    Keywords: Forecasting, commodities, aluminum, univariate time-series models, out-of-sample comparison, exchange rates.
    JEL: C0 C00 C01 C10 C11 C12 C13 C14 C18 C2 C20 C22 C24 C3 C32 C38 C4 C5 C50 C51 C52 C53 C58 E0 E3 E30 E31 E32 E37 E4 E40 E42 E43 E44 E47 E5 E58 F3 F31 F37 G1 G11 G12 G14 G17 Q3 Q31 Q32 Q4 Q47
    Date: 2019–11–15
  2. By: Gary S. Anderson; Alena Audzeyeva
    Abstract: We propose a coherent framework using support vector regression (SRV) for generating and ranking a set of high quality models for predicting emerging market sovereign credit spreads. Our framework adapts a global optimization algorithm employing an hv-block cross-validation metric, pertinent for models with serially correlated economic variables, to produce robust sets of tuning parameters for SRV kernel functions. In contrast to previous approaches identifying a single "best" tuning parameter setting, a task that is pragmatically improbable to achieve in many applications, we proceed with a collection of tuning parameter candidates, employing the Model Confidence Set test to select the most accurate models from the collection of promising candidates. Using bond credit spread data for three large emerging market economies and an array of input variables motivated by economic theory, we apply our framework to identify relatively small sets of SVR models with su perior out-of-sample forecasting performance. Benchmarking our SRV forecasts against random walk and conventional linear model forecasts provides evidence for the notably superior forecasting accuracy of SRV-based models. In contrast to routinely used linear model benchmarks, the SRV-based models can generate accurate forecasts using only a small set of input variables limited to the country-specific credit-spread-curve factors, lending some support to the rational expectation theory of the term structure in the context of emerging market credit spreads. Consequently, our evidence indicates a better ability of highly flexible SVR to capture investor expectations about future spreads reflected in today's credit spread curve.
    Keywords: Support vector machine regressions ; Out-of-sample predictability ; Soverign cedit spreads ; Machine learning ; Emerging markets ; Model confidence set
    JEL: G17 F15 G15 F34 F17 C53
    Date: 2019–10–17
  3. By: Martin Iseringhausen; Mwanza Nkusu; Wellian Wiranto
    Abstract: This paper studies the determinants of repeated use of Fund-supported programs in a large sample covering virtually all General Resources Account (GRA) arrangements that were approved between 1952 and 2012. Generally, the revolving nature of the IMF’s resources calls for the temporary sup-port of member countries to address balance of payments problems while repeated use has often been viewed as program failure. First, using probit models we show that a small number of country-specific variables such as growth, the current account balance, the international reserves position, and the institutional framework play a significant role in explaining repeated use. Second, we discuss the role of IMF-specific and program-specific variables and find evidence that a country’s track record with the Fund is a good predictor of repeated use. Finally, we conduct an out-of-sample forecasting exer-cise. While our approach has predictive power for repeated use, exact forecasting remains challenging. From a policy perspective, the results could prove useful to assess the risk IMF programs pose to the revolving nature of the Fund’s financial resources.
    Date: 2019–11–08
  4. By: Chavleishvili, Sulkhan; Manganelli, Simone
    Abstract: We introduce a structural quantile vector autoregressive (VAR) model. Unlike standard VAR which models only the average interaction of the endogenous variables, quantile VAR models their interaction at any quantile. We show how to estimate and forecast multivariate quantiles within a recursive structural system. The model is estimated using real and financial variables. The dynamic properties of the system change across quantiles. This is relevant for stress testing exercises, whose goal is to forecast the tail behavior of the economy when hit by large financial and real shocks. JEL Classification: C32, C53, E17, E32, E44
    Keywords: growth at risk, regression quantiles, structural VAR
    Date: 2019–11
  5. By: B. Shravan Kumar; Vadlamani Ravi; Rishabh Miglani
    Abstract: Financial forecasting using news articles is an emerging field. In this paper, we proposed hybrid intelligent models for stock market prediction using the psycholinguistic variables (LIWC and TAALES) extracted from news articles as predictor variables. For prediction purpose, we employed various intelligent techniques such as Multilayer Perceptron (MLP), Group Method of Data Handling (GMDH), General Regression Neural Network (GRNN), Random Forest (RF), Quantile Regression Random Forest (QRRF), Classification and regression tree (CART) and Support Vector Regression (SVR). We experimented on the data of 12 companies stocks, which are listed in the Bombay Stock Exchange (BSE). We employed chi-squared and maximum relevance and minimum redundancy (MRMR) feature selection techniques on the psycho-linguistic features obtained from the new articles etc. After extensive experimentation, using the Diebold-Mariano test, we conclude that GMDH and GRNN are statistically the best techniques in that order with respect to the MAPE and NRMSE values.
    Date: 2019–11

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