Search

Stock index trend prediction is a very important topic in the finance. The purpose of this paper is to compare six ensemble learning related techniques for stock index direction prediction, including four boosting methods (Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost) and Gradient Boosting Decision Tree (GBDT)), one bagging method (Random Forest (RF)) and one tree-structured machine learning method (Decision Tree (DT)). The Shanghai Composite Index is chosen for experimental evaluation. A factor library of seventy-two technical factors, thirty-five macro factors and seven micro factors are our inputs. Our predictions are one month ahead, and each prediction model is evaluated by the Area Under Curve (AUC). The results indicate that ensemble learning techniques perform well in stock index prediction, with all AUC values above 0.5. RF is considered as the top algorithm with an AUC value of 0.7355 before feature selection and 0.6736 after feature selection. Also, we predict the stock index trend using a comprehensive factor library and three single factor libraries, respectively. The results show that forecasting stock index directions with a complete factor library is of great importance, which could achieve more stable forecasting results. This study contributes to literature in that it is, to the best of our knowledge, the first to make an extensive evaluation of ensemble learning related methods by constructing a comprehensive factor library and three single factor libraries. [ABSTRACT FROM AUTHOR]

Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and 'short' data imply a 'ragged edge' at both the beginning and the end of regional data sets, which adds a further complication. In this paper, via an application to the UK, we investigate various ways of including a wide range of short data into a regional mixed-frequency vector autoregression (MF-VAR) model. These short data include hitherto unexploited regional value-added tax turnover data. We address the problem of the two ragged edges by estimating regional factors using different missing data algorithms that we then incorporate into our MF-VAR model. We find that nowcasts of regional output growth are generally improved when we condition them on the factors, but only when the regional nowcasts are produced before the national (UK-wide) output growth data are published. [ABSTRACT FROM AUTHOR]

We consider inference for predictive regressions with multiple predictors. Extant tests for predictability (especially for joint predictability) may perform unsatisfactorily and tend to discover spurious predictability as the number of predictors increases. We propose a battery of new instrumental variables-based tests which involve enforcement or partial enforcement of the null hypothesis in variance estimation. A test based on the few-predictors-at-a-time parsimonious system approach is recommended. Empirical Monte Carlos demonstrates the remarkable finite-sample performance regardless of numerosity of predictors and their persistence properties. Empirical application to equity premium predictability is provided. [ABSTRACT FROM AUTHOR]

This study delves into the complex interplay between output growth and inflation volatility in 17 selected African countries to compare outcomes across different regions and socioeconomic settings on the African continent. The empirical analysis is carried out using the GARCH-MIDAS approach as this framework enables the use of combined data with mixed frequencies and allows the circumvention of the problem of information loss due to averaging of series. Our findings indicate that output growth plays a moderating role in taming inflation volatility in specific countries, while it does not hold the same significance in other sub-regions. These results emphasize the importance of region-specific policy strategies for effectively managing and addressing inflation fluctuations in diverse economic contexts. Also, the research observes the persistence of inflation volatility, albeit with a mean reverting tendency. Our robustness checks reveal output gap to predict inflation volatility with regional differences.

We propose a setup for fractionally cointegrated time series which is formulated in terms of latent integrated and short-memory components. It accommodates nonstationary processes with different fractional orders and cointegration of different strengths and is applicable in high-dimensional settings. In an application to realized covariance matrices, we find that orthogonal short- and long-memory components provide a reasonable fit and competitive out-of-sample performance compared with several competing methods. [ABSTRACT FROM AUTHOR]

In this study, we propose a two‐step, less‐volatile value‐at‐risk (LVaR) estimation using a generalized nearly isotonic regression (GNIR) model. In the proposed approach, a VaR sequence is first produced under the generalized autoregressive conditional heteroskedasticity (GARCH) framework. Then, the VaR sequence is adjusted by GNIR, and the generated estimate is denoted as LVaR. The results of an empirical investigation show that LVaR outperformed other VaR estimates under the classic equally weighted and exponentially weighted moving‐average frameworks. Furthermore, we show not only that LVaR is less volatile, but also that it performed reasonably well in various backtests. [ABSTRACT FROM AUTHOR]

This paper describes the set of Bayesian vector autoregression (BVAR) models that Banco de España uses to project GDP growth rates and to simulate macrofinancial risk scenarios for Brazil and Mexico. The toolkit consists of large benchmark models to produce baseline projections and various smaller satellite models to conduct risk scenarios. We showcase the use of this modeling framework with tailored empirical applications. Given the material importance of Brazil and Mexico to the Spanish economy and banking system, this toolkit contributes to the monitoring of Spain’s international risk exposure.

Aim/purpose – The underground economy is a major challenge across the world affecting both developed and developing economies. South Africa is no exception to this phenomenon and has lost billions of rands due to the underground economy. The aim of this study is to estimate the size of the underground economy in South Africa.

This paper estimates and analyzes multipliers for tax revenue and public spending for Colombia using structural autoregressive vectors and local projections models. Quarterly series of the central national government between 2000Q1 and 2018Q4 are used. The results show fiscal multipliers that are less than unity, except when the economy is in either an expansionary or contractionary phase, when the spending multiplier is greater than one. Furthermore, the spending multiplier is greater during a contractionary phase, supporting the case for conducting countercyclical fiscal policy in this state. The tax revenue multiplier is negative and much smaller in magnitude than the spending multiplier. The results are robust to different identification schemes and estimation methodologies.

Although survey‐based point predictions have been found to outperform successful forecasting models, corresponding variance forecasts are frequently diagnosed as heavily distorted. Professional forecasters who report inconspicuously low ex ante variances often produce squared forecast errors that are much larger on average. In this paper, we document the novel stylized fact that this variance misalignment is related to the rounding behavior of survey participants. Rounding may reflect the fact that some survey participants employ a rather judgmental approach to forecasting as opposed to using a formal model. We use the distinct numerical accuracies of panelists' reported probabilities as a way to propose several alternative and easily implementable corrections that (i) can be carried out in real time, that is, before outcomes are observed, and (ii) deliver a significantly improved match between ex ante and ex post forecast uncertainty. According to our estimates, uncertainty about inflation, output growth and unemployment in the U.S. and the Euro area is higher after correcting for the rounding effect. The increase in the share of nonrounded responses in recent years also helps to understand the trajectory of survey‐based average uncertainty during the years since the financial and sovereign debt crisis. [ABSTRACT FROM AUTHOR]

Rather than relying on a potentially poor point estimate of a coefficient break date when forecasting, this paper proposes averaging forecasts over sub-samples indicated by a confidence interval or set for the break date. Further, we examine whether explicit consideration of a possible variance break and the use of a two-step methodology improves forecast accuracy compared with using heteroskedasticity robust inference. Our Monte Carlo results and empirical application to US productivity growth show that averaging using the likelihood ratio-based confidence set typically performs well in comparison with other methods, while two-step inference is particularly useful when a variance break occurs concurrently with or after any coefficient break. [ABSTRACT FROM AUTHOR]

A multivariate GARCH model of natural resources is specified to capture the effects of time varying portfolio risk. A special feature of the model is the inclusion of realized volatility for natural resource assets that are available at multiple frequencies as well as being sensitive to sudden changes in climatic conditions. Natural resource portfolios under climate change are simulated from bootstrapping schemes as well as being derived from global climate model projections. Both approaches are applied to a multiasset water portfolio model consisting of reservoir inflows, rainwater harvesting, and desalinated water. The empirical results show that while reservoirs remain the dominant water asset, adaptation to climate change involves increased contributions from rainwater harvesting and more frequent use of desalinated water. It is estimated that climate change increases annual water supply costs by between 7% and 44% over a 20‐year forecast horizon. [ABSTRACT FROM AUTHOR]

We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications. [ABSTRACT FROM AUTHOR]

In this paper the authors investigate the statistical properties of some cryptocurrencies by using three layers of analysis: alpha-stable distributions, Metcalfe’s law and the bubble behaviour through the LPPL modelling. The results show, in the medium to long-run, the validity of Metcalfe's law (the value of a network is proportional to the square of the number of connected users of the system) for the evaluation of cryptocurrencies; however, in the short-run, the validity of Metcalfe’s law for Bitcoin is questionable. According to the bidirectional causality between the price and the network size, the expected price increase is a driver for more investors to join the Bitcoin network, which may lead in the end to a super-exponential price growth, possibly due to a herding behaviour of investors. The authors then used LPPL models to capture the behaviour of cryptocurrencies exchange rates during an endogenous bubble and to predict the most probable time of the regime switching. The main conclusion of this paper is that Metcalfe’s law may be valid in the long-run, however in the short-run, on various data regimes, its validity is highly debatable.