Multivariate volatility forecasts for stock market indices

Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In this study, we forecast the realized variance as an observable measure of volatility for several major international stock market indices and accounted for the different predictive information present in jump, continuous, and option-implied variance components. We allowed for volatility spillovers in different stock markets by using a multivariate modeling approach. We used heterogeneous autoregressive (HAR)-type models to obtain the forecasts. Based an out-of-sample forecast study, we show that: (i) including option-implied variances in the HAR model substantially improves the forecast accuracy, (ii) lasso-based lag selection methods do not outperform the parsimonious day-week-month lag structure of the HAR model, and (iii) cross-market spillover effects embedded in the multivariate HAR model have long-term forecasting power.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

A novel cluster HAR-type model for forecasting realized volatility

This paper proposes a cluster HAR-type model that adopts the hierarchical clustering technique to form the cascade of heterogeneous volatility components. In contrast to the conventional HAR-type models, the proposed cluster models are based on the relevant lagged volatilities selected by the cluster group Lasso. Our simulation evidence suggests that the cluster group Lasso dominates other alternatives in terms of variable screening and that the cluster HAR serves as the top performer in forecasting the future realized volatility. The forecasting superiority of the cluster models are also demonstrated in an empirical application where the highest forecasting accuracy tends to be achieved by separating the jumps from the continuous sample path volatility process.     

Determinants in the forecast of the gross national income of China and India from 1952 to 2015

In 2015, China and India's population represented approximately 35.74% of the total number of people living in the world. Due to the historical context and behavior of the most relevant indicators, this study proposes to utilize a wide variety of demographic, economic, and production indicators from 1952 to 2015 to assess their impact on the GNI in China and India. A comprehensive and new fangled modeling process with stepwise, regularization and distributed lag regression approaches is presented. Accordingly, theoretical results were corroborated through extensive diagnostic tests and an empirical check of the models' predictive capacity. The findings show that GNI in China is most influenced by variables such as reserves in foreign currency and the dependency ratio; whereas, variables of energy production and birth rate were generated for India. Therefore, it's the timing for China to relax the universal two-child policy. Due to the current value below the substitution rate, a gloomy outlook for China's future population and economy is predicted. Conversely, a positive outlook is forecasted for India, given the low price in the future of oil- India's primary raw material.     

社会经济及气象因素对水稻产量的影响研究——以三江平原为例

[目的]系统分析三江平原水稻产量的影响因素,对指导三江平原农业生产和耕地保护及减少未来粮食安全风险具有现实意义。[方法]文章利用1988—2018年三江平原水稻产量、气象观测和社会经济数据,结合利用经济学模型——柯布-道格拉斯生产函数和统计学方法 Lasso回归模型,分别分析社会经济因素和自然因素对三江平原水稻产量的影响。[结果]（1）在社会经济因素方面,单位面积农村用电量和农业科研经费投入的单位面积增产效应比较明显,单位面积劳动力投入和化肥施用量对水稻产量具有负效应。（2）在自然因素方面,生长季内平均最低气温和平均日照时数对水稻产量有正向影响,生长季内总降水量和平均日相对湿度对水稻产量有负向影响。[结论]为水稻生产提供政策建议：可以通过合理施肥、加大机械化和农业科技创新的投入来提升水稻产量。同时,适当人工增加光照时间和加强冻害防治对提高水稻产量也具有重要意义。     

基于变量选择方法下的人民币汇率影响因素分析

在影响人民币汇率的众多因素中,选出GDP增长率、进出口差额增长率、货币和准货币供应量增长率、外汇储备增长率、中美相对消费价格指数、通货膨胀率和中美利差等7个影响人民币汇率的主要因素。选用了一种新的变量选择方法———自适应Lasso方法对人民币汇率影响因素进行有效的选择。同时使用真实数据作了实证研究,并与最小二乘法和逐步线性回归方法进行比较。结果表明：自适应Lasso方法在人民币汇率影响因素的选择方面,相对于逐步线性回归和最小二乘法有明显的优势。自适应Lasso方法不仅仅完成了模型的参数估计,同时也完成了对影响人民币汇率因素的筛选。     

Sparse estimation of dynamic principal components for forecasting high-dimensional time series

We present the sparse estimation of one-sided dynamic principal components (ODPCs) to forecast high-dimensional time series. The forecast can be made directly with the ODPCs or by using them as estimates of the factors in a generalized dynamic factor model. It is shown that a large reduction in the number of parameters estimated for the ODPCs can be achieved without affecting their forecasting performance.     

Forecasting the aggregate stock market volatility in a data-rich world

ABSTRACT

In this article, we utilize the basic lasso and elastic net models to revisit the predictive performance of aggregate stock market volatility in a data-rich world. Motivated by the existing literature, we determine several candidate predictors that have 22 technical indicators and 14 macroeconomic and financial variables. Our out-of-sample results reveal several noteworthy findings. First, few macroeconomic and financial variables and most of technical indicators have superior performance relative to the benchmark model. Second, combination forecasts are able to significantly beat the benchmark and some signal predictors Third, the lasso and elastic models with all predictors can generate more accurate forecasts than the benchmark and some other predictors in both the statistical and economic sense. Fourth, the lasso and elastic models exhibit higher forecast accuracy during periods of expansions and recessions. Finally, our findings are robust to several tests, such as different forecasting windows, forecasting models, and forecasting evaluations.     

Granger causality detection in high-dimensional systems using feedforward neural networks

This paper proposes a novel methodology to detect Granger causality on average in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between elements of high-dimensional multivariate time series with weak and strong persistence. To do this, we propose a two-stage procedure: first, we maximize the transfer of information between input and output variables in the network in order to obtain an optimal number of nodes in the intermediate hidden layers. Second, we apply a novel sparse double group lasso penalty function in order to identify the variables that have the predictive ability and, hence, indicate that Granger causality is present in the others. The penalty function inducing sparsity is applied to the weights that characterize the nodes of the neural network. We show the correct identification of these weights so as to increase sample sizes. We apply this method to the recently created Tobalaba network of renewable energy companies and show the increase in connectivity between companies after the creation of the network using Granger causality measures to map the connections.     

Uncertainty and crude oil market volatility: new evidence

ABSTRACT

The main goal of this paper is to investigate the predictability of five economic uncertainty indices for oil price volatility in a changing world. We employ the standard predictive regression framework, several model combination approaches, as well as two prevailing model shrinkage methods to evaluate the performances of the uncertainty indices. The empirical results based on simple autoregression models including only one index suggest that global economic policy uncertainty (GEPU) and US equity market volatility (EMV) indices have significant predictive power for crude oil market volatility. In addition, the model combination approaches adopted in this paper can improve slightly the performances of individual autoregressive models. Lastly, the two model shrinkage methods, namely Elastin net and Lasso, outperform other individual AR-type model and combination models in most forecasting cases. Other empirical results based on alternative forecasting methods, estimation window sizes, high/low volatility and economic expansion/recession time periods further make sure the robustness of our major conclusions. The findings in this paper also have several important economic implications for oil investors.