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on Forecasting |
| By: | Kim, Hyeongwoo; Ko, Kyunghwan |
| Abstract: | We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets. |
| Keywords: | Partial Least Squares; Principal Component Analysis; Financial Stress Index; Out-of-Sample Forecast; RRMSPE |
| JEL: | C38 C53 E44 E47 G00 G17 |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:89449&r=for |
| By: | Hyeongwoo Kim; Wen Shi; Hyun Hak Kim |
| Abstract: | We propose factor-based out-of-sample forecast models for Korea's financial stress index and its 4 sub-indices that are developed by the Bank of Korea. We extract latent common factors by employing the method of the principal components for a panel of 198 monthly frequency macroeconomic data after differencing them. We augment an autoregressive-type model of the financial stress index with estimated common factors to formulate out-of-sample forecasts of the index. Our models overall outperform both the stationary and the nonstationary benchmark models in forecasting the financial stress indices for up to 12-month forecast horizons. The first common factor that represents not only financial market but also real activity variables seems to play a dominantly important role in predicting the vulnerability in the financial markets in Korea. |
| Keywords: | Financial Stress Index; Principal Component Analysis; PANIC; In-Sample Fit; Out-of-Sample Forecast; Diebold-Mariano-West Statistic |
| JEL: | E44 E47 G01 G17 |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:abn:wpaper:auwp2018-06&r=for |
| By: | Anesti, Nikoleta; Galvao, Ana Beatriz; Miranda-Agrippino, Silvia |
| Abstract: | We design a new econometric framework to nowcast macroeconomic data subject to revisions, and use it to predict UK GDP growth in real-time. To this aim, we assemble a novel dataset of monthly and quarterly indicators featuring over ten years of real-time data vintages. Successive monthly estimates of GDP growth for the same quarter are treated as correlated observables in a Dynamic Factor Model (DFM) that also includes a large number of mixed-frequency predictors, leading to the release-augmented DFM (RA-DFM). The framework allows for a simple characterisation of the stochastic process for the revisions as a function of the observables, and permits a detailed assessment of the contribution of the data flow in informing (i) forecasts of quarterly GDP growth; (ii) the evolution of forecast uncertainty; and (iii) forecasts of revisions to early released GDP data. By evaluating the real-time performance of the RA-DFM, we find that the model’s predictions have information about the latest GDP releases above and beyond that contained in the statistical office earlier estimates; predictive intervals are well-calibrated; and UK GDP growth real-time estimates are commensurate with professional nowcasters. We also provide evidence that statistical office data on production and labour markets, subject to large publication delays, account for most of the forecastability of the revisions. |
| Keywords: | nowcasting; data revisions; dynamic factor model |
| JEL: | C51 C53 |
| Date: | 2018–08–22 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:90382&r=for |
| 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: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:abn:wpaper:auwp2018-07&r=for |
| By: | Pincheira, Pablo; Hardy, Nicolas |
| Abstract: | In this paper we show that survey-based-expectations about the future evolution of the Chilean exchange rate have the ability to predict the returns of the six primary non-ferrous metals: aluminum, copper, lead, nickel, tin and zinc. Predictability is also found for returns of the London Metal Exchange Index. Previous studies have shown that the Chilean exchange rate has the ability to predict copper returns, a world commodity index and base metal prices. Nevertheless, our results indicate that expectations about the Chilean peso have stronger predictive ability relative to the Chilean currency. This is shown both in-sample and out-of-sample. By focusing on expectations of a commodity currency, and not on the currency itself, our paper provides indirect but new and strong evidence of the ability that commodity currencies have to forecast commodity prices. Our results are also consistent with the present-value-model for exchange rate determination. |
| Keywords: | Forecasting; commodities; univariate time-series models; out-of-sample comparison; exchange rates; copper; base metals |
| JEL: | C1 C10 C2 C22 C3 C32 C49 C52 C53 C58 E0 E31 E32 E37 E4 E42 E44 E47 F31 F32 F37 F4 F44 F47 M21 Q3 Q31 Q37 |
| Date: | 2018–10–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:89423&r=for |
| By: | Peru Muniain; Florian Ziel |
| Abstract: | In this paper we include dependency structures for electricity price forecasting and forecasting evaluation. We work with off-peak and peak time series from the German-Austrian day-ahead price, hence we analyze a bivariate data. We first estimate the mean of the two time series, and then in a second step we estimate the residuals. The mean equation is estimated by OLS and elastic net and the residuals are estimated by maximum likelihood. Our contribution is to include a bivariate jump component on a mean reverting jump diffusion model in the residuals. The models' forecasts are evaluated using four different criteria, including the energy score to measure whether the correlation structure between the time series is properly included or not. In the results it is observed that the models with bivariate jumps provide better results with the energy score, which means that it is important to consider this structure in order to properly forecast correlated time series. |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1810.08418&r=for |