Take Bitcoin into your portfolio:a novel ensemble portfolio optimization framework for broad commodity assets

The emergence and growing popularity of Bitcoins have attracted the attention of the financial world.However,few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market.It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio.In this study,we propose a novel ensemble portfolio optimization(NEPO)framework utilized for broad commodity assets,which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation.Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors.In addition,it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies,commodity funds,and indices.

A Demand Forecasting Method Based on Stochastic Frontier Analysis and Model Average: An Application in Air Travel Demand Forecasting

Demand forecasting is often difficult due to the unobservability of the applicable historical demand series. In this study, the authors propose a demand forecasting method based on stochastic frontier analysis(SFA) models and a model average technique. First, considering model uncertainty,a set of alternative SFA models with various combinations of explanatory variables and distribution assumptions are constructed to estimate demands. Second, an average estimate from the estimated demand values is obtained using a model average technique. Finally, future demand forecasts are achieved, with the average estimates used as historical observations. An empirical application of air travel demand forecasting is implemented. The results of a forecasting performance comparison show that in addition to its ability to estimate demand, the proposed method outperforms other common methods in terms of forecasting passenger traffic.

A Hierarchical Forecasting Model for China's Foreign Trade

Analyzing the underlying characteristics of trade values movements has attracted much attention in the domestic research.However,the proposed understanding of these characteristics is limited by the intrinsic complexity of the imports/exports.Since economic systems are naturally organized by hierarchies,a novel hierarchical model is proposed in this paper to forecast China's foreign trade.First,the foreign trade data are disaggregated from perspectives of trading partners and trading products,forming two independent hierarchical models with total exports and imports as target variables.Second,a bottom-up strategy is applied.All bottom time series are modelled by corresponding control variables according to trading theories.Forecasts for bottom time series are then combined to generate initial forecasts for total exports and imports.Finally,forecasts for total imports and exports from the two hierarchical models,plus a single VECM model are combined to generate final forecasts.Empirical experiments show that this proposed forecasting model approach significantly outperforms benchmark models and produces consistent forecasts for both total imports and exports or detailed items,which helps a lot for analyzing future trading structure evolution and making foreign trade policies.

Foreign Trade Survey Data:Do They Help in Forecasting Exports and Imports?

Business survey,which starts from the microeconomic level,is a widely used short-term forecasting tool in practice.In this study,the authors examine whether foreign trade survey data collected by China’s Ministry of Commerce would provide reliable forecasts of China’s foreign trade.The research procedure is designed from three perspectives including forecast information test,turning point forecast,and out-of-sample value forecast.First,Granger causality test detects whether survey data lead exports and imports.Second,business cycle analysis,a non-model based method,is performed.The authors construct composite indexes with business survey data to forecast turning points of foreign trade.Third,model-based numerical forecasting methods,including the Autoregressive Integrated Moving Average Model with Exogenous Variables(ARIMAX)and the artificial neural networks(ANNs)models are estimated.Empirical results show that survey data granger cause imports and exports,the leading composite index provides signal for changes of trade cycles,and quantitative models including survey data generate more accurate forecasts than benchmark models.It is concluded that trade survey data has excellent predictive capabilities for imports and exports,which can offer some priorities for government policy-making and enterprise decision making.

A decomposition clustering ensemble learning approach for forecasting foreign exchange rates

A decomposition clustering ensemble(DCE)learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition(VMD),the selforganizing map(SOM)network,and the kemel extreme leaming machine(KELM).First,the exchange rate time series is decomposed into N subcomponents by the VMD method.Second,each subcomponent series is modeled by the KELM.Third,the SOM neural network is introduced to cluster the subcomponent forecasting results of the in-sample dataset to obtain cluster centers.Finally,each cluster's ensemble weight is estimated by another KELM,and the final forecasting results are obtained by the corresponding clusters'ensemble weights.The empirical results illustrate that our proposed DCE learning approach can significantly improve forecasting performance,and statistically outperform some other benchmark models in directional and level forecasting accuracy.

A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting

In this paper, a KELM-based ensemble learning approach, integrating Granger causality test, grey relational analysis and KELM(Kernel Extreme Learning Machine), is proposed for the exchange rate forecasting. The study uses a set of sixteen macroeconomic variables including, import,export, foreign exchange reserves, etc. Furthermore, the selected variables are ranked and then three of them, which have the highest degrees of relevance with the exchange rate, are filtered out by Granger causality test and the grey relational analysis, to represent the domestic situation. Then, based on the domestic situation, KELM is utilized for medium-term RMB/USD forecasting. The empirical results show that the proposed KELM-based ensemble learning approach outperforms all other benchmark models in different forecasting horizons, which implies that the KELM-based ensemble learning approach is a powerful learning approach for exchange rates forecasting.

Analyses of Political Crisis Impact on Tourism:A Panel Counterfactual Approach with Internet Search Index

Existing research has shown that political crisis events can directly impact the tourism industry.However,the current methods suffer from potential changes of unobserved variables,which poses challenges for a reliable evaluation of the political crisis impacts.This paper proposes a panel counterfactual approach with Internet search index,which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest.It also provides a tool to examine potential channels through which the crisis may affect tourist outflows.This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea.Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place.This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.

Frequentist model averaging for linear mixed-effects models

Linear mixed-effects models are a powerful tool for the analysis of longitudinal data. The aim of this paper is to study model averaging for linear mixed-effects models. The asymptotic distribution of the frequentist model average estimator is derived, and a confidence interval procedure with an actual coverage probability that tends to the nominal level in large samples is developed. The two confidence intervals based on the model averaging and based on the full model are shown to be asymptotically equivalent. A simulation study shows good finite sample performance of the model average estimators.

Out-of-sample forecasts of China's economic growth and inflation using rolling weighted least squares

Macroeconomic forecasting in China is essential for the government to take proper policy decisions on government expenditure and money supply,among other matters.The existing literature on forecasting Chinas macroeconomic variables is unclear on the crucial issue of how to choose an optimal window to estimate parameters with rolling out-of-sample forecasts.This study fills this gap in forecasting economic growth and inflation in China,by using the rolling weighted least squares(WLS)with the practically feasible cross-validation(CV)procedure of Hong et al.(2018)to choose an optimal estimation window.We undertake an empirical analysis of monthly data on up to 30 candidate indicators(mainly asset prices)for a span of 17 years(2000-2017).It is documented that the forecasting performance of rolling estimation is sensitive to the selection of rolling windows.The empirical analysis shows that the rolling WLS with the CV-based rolling window outperforms other rolling methods on univariate regressions in most cases.One possible explanation for this is that these macroeconomic variables often suffer from structural changes due to changes in institutional reforms,policies,crises,and other factors.Furthermore,we find that,in most cases,asset prices are key variables for forecasting macroeconomic variables,especially output growth rate.

Economic forecasting with big data:A literature review

Big data technology has revolutionized the research paradigm of economic forecasting regardless of the data source,forecasting method,or forecasting result.This study evaluates the current literature on economic forecasting using big data and employs bibliometric approaches to offer a comprehensive analysis.Additionally,utilizing the advanced structural variation analysis technique,we can identify papers with transformative potential in this domain.This study provides valuable suggestions for enhancing scholars'understanding of significant research,novel breakthroughs,and emerging trends in the role of big data in economic forecasting.

An Event Analysis of Bitcoin Based on a Novel DRE Methods

Since Bitcoin came into the world,modelling and analyzing the underlying characteristics of Bitcoin has attracted increasing attention.This paper uses a framework including decomposition,reconstruction and extraction method(DRE)to analyze price fluctuations based on ultra-high-frequency data from Dec.1,2019,to Nov.30,2021.First,the ensemble mode decomposition(EMD)is employed to decompose the Bitcoin hourly spot price into 13 intrinsic mode functions(IMF)plus a residual.Second,the IMFs are reconstructed into high-frequency components,low-frequency components and a trend based on fine-to-coarse reconstruction.Furthermore,the intraday volatility analysis based on LM test is applied on 15-minutes frequency data to detect discontinuous jump arrivals and extract jump from realized quadratic variation.Empirical results show that three components of reconstruction can be identified as short term fluctuations process caused by microstructure noise,the shocks affected by major events,and a long-term trend based on inelastic supply and rigid demand.We find that approximately 40%of jumps can be matched with the news from the public news database(Factiva),and the jump sizes are larger than that of stock markets.This finding indicates that the Bitcoin market has more irregularly noise and unforeseen shocks from unscheduled events.

An Empirical Study on the Time-Varying Connectedness Between Shanghai and Hong Kong Markets—A Perspective from Liquidity and Trading Activities

We conduct an empirical analysis of Shanghai-Hong Kong Stock Connect to reveal the dynamic impacts of stock connect trading activity on the stock pool's Amihud illiquidity proxy, index return, and CNY-HKD exchange rate. From pairwise conditional g causality analysis, we note a mutual significant causal connection between northbound net buying volume and Shanghai stock exchange return on all frequency levels. Meanwhile, we find a significant causal impact on the Shanghai portfolio's liquidity from northbound net buying volume. And there is a significant causal impact from the southbound net buying volume on Hang Seng Index return. Both are significant at the low-frequency level. In particular, northbound trading activity stimulates the Shanghai portfolio's liquidity in the low trading activity regime from the threshold VAR analysis. In robust analysis, we find similar significant dynamic causal connection and stimulation effects for the northbound trades when replacing Amihud illiquidity with the turnover rate. The result might relate to the investment behaviors looking for opportunity in the low trading activity regime. In contrast, the investors' beliefs may vary in the high trading activity regime, which weakens the connection between trading activities and other factors like liquidity.

A Method to Visualize the Skeleton Industrial Structure with Input-Output Analysis and Its Application in China,Japan and USA

The paper established a double filtering method (DFM) to visualize the skeleton industrial structure (SIS) of one economy and find its evolution rule. Different with the previous researches, this method is from a new view of industrial conjunctions combined by leading sectors to depict the industrial structure. It was proved that the leading sector selected by DFM must be key sector selected by Hirschman-Rasmussen method. Applied DFM to input-output tables of China, Japan and USA and MFA to Japan, and USA, the results analysis showed that DFM could overtake the two main shortcomings of minimum flow analysis (MFA), scratch SIS of each economy with its own characteristics, visualize the general evolution rules of the industrial structure with crisscrossed conjunctions among leading sectors.

Evaluating the Role of International Trade in the Growth of China's CO_2 Emissions

International trade matters in assessing the extent of China’s responsibility for CO2 emissions.A determining factor is whether emissions are measured in production or in consumption terms.Based on a series of input-output tables,an empirical analysis is conducted to measure the impact of international trade on China’s emissions growth during the period 1997 to 2007.The authors also measure the impact on emissions of bilateral trade between China and US,European Union and Japan.As the largest of the developing countries,China has a trade surplus that can substantially influence its measured responsibility for emissions.The authors consider some policy implications for international negotiations to reduce global greenhouse gas emissions.

The Social Welfare Housing Project and Its Effects in China

It was planned to build 36 million units of social welfare housing during the twelve-five2011-2015 in China.This paper introduces the estimation of owner occupied dwelling sector and develops a dynamic computable general equilibrium model for China's real estate and macro-economy,to simulate the policy effects.The simulation results show that this policy can meet the increased requirements of housing demand due to fast urbanization and improvement of living conditions,therefore it will effectively cool down the price boom of housing market.Meanwhile,although the investment on social welfare housing will reduce the investment on other sectors,it will still stimulate GDP growth.

Impact of the RMB Joining in the SDR Basket on Its Internationalization from the Perspective of Risk Spillover

For evaluating the influence of the Chinese renminbi(RMB) joining in the special drawing right(SDR) basket on RMB's internationalization, the authors systemically study the risk spillover networks and examine the dynamic relationship of exchange rates among the SDR currencies including the US dollar(USD), European Union euro(EUR), Japanese yen(JPY) and British pound(GBP).The empirical results demonstrate that the USD takes a dominant position and holds the biggest risk spillover to other currencies, and the RMB's inclusion to the SDR basket makes the risk spillover to get average, giving rise to the SDR currency system more stable to a certain degree. The inclusion of the RMB in the SDR not only can reduce the systematic risk of the SDR, but also has a certain impact on the international exchange rate markets. Nowadays, in front of the growing trade friction, more such researches could help to effectively deal with the currency disputes.

Return and Volatility Connectedness between Stock Markets and Macroeconomic Factors in the G-7 Countries

We examine the relationship between return and volatility of the stock markets and macroeconomic fundamentals for the G-7 countries by using monthly data ranging from July 1985 to June 2015. To meet this end, we apply the spillover index approach based on the generalized VAR framework developed by Diebold and Yilmaz (2012, 2014). The empirical analysis shows strong interactions between the returns and volatilities of the G-7 stock markets and the considered set of corresponding macroeconomic factors including industrial production, money supply, interest rates, inflation, oil prices and exchange rates. The return and volatility spillover transmission/reception dynamics of the relationships between these stock markets and the macroeconomic fundamentals have changed after the global financial crisis of 2008. Our findings provide useful insights for investors and policy makers concerned with the unprecedented swings in the stock markets of G-7 countries.

Motivations of Chinese Outward Direct Investment:The Sector Perspective

From the sector perspective of mining,manufacturing and services,the motivations of Chinese outward direct investment(further ODI)are discussed during the period from 2001 to 2012,acknowledging different host countries and firms’ownership structures.The estimated results justify that the location determinants of Chinese ODI differ between sectors,which implies the motivation behind such investment may vary.As expected,resource-seeking is the most important motivation for Chinese ODI in mining sector;market-and strategic asset-seeking motivations are possessed by both manufacturing and services sectors.The probability of the host country receiving Chinese FDI,as well as high FDI openness and frequent bilateral trade with China is favorable for doing business.Results also suggest that the factors increasing the probability of a country being chosen as a location for Chinese ODI vary between different host countries,as do different ownership structures.

Choice of weights in FMA estimators under general parametric models

The choice of weights in frequentist model average estimators is an important but difficult problem. Liang et al. (2011) suggested a criterion for the choice of weight under a general parametric framework which is termed as the generalized OPT (GOPT) criterion in the present paper. However, no properties and applications of the criterion have been studied. This paper is devoted to the further investigation of the GOPT criterion. We show that how to use this criterion for comparison of some existing weights such as the smoothed AIC-based and BIC-based weights and for the choice between model averaging and model selection. Its connection to the Mallows and ordinary OPT criteria is built. The asymptotic optimality on the criterion in the case of non-random weights is also obtained. Finite sample performance of the GOPT criterion is assessed by simulations. Application to the analysis of two real data sets is presented as well.