Representation,estimation and forecasting of the multivariate index-augmented autoregressive model

We examine the conditions under which each individual series that is generated by a vector autoregressive model can be represented as an autoregressive model that is augmented with the lags of a few linear combinations of all the variables in the system. We call this multivariate index-augmented autoregression (MIAAR) modelling. We show that the parameters of the MIAAR can be estimated by a switching algorithm that increases the Gaussian likelihood at each iteration. Since maximum likelihood estimation may perform poorly when the number of parameters increases, we propose a regularized version of our algorithm for handling a medium–large number of time series. We illustrate the usefulness of the MIAAR modelling by both empirical applications and simulations.     

A comparison of non-Gaussian VaR estimation and portfolio construction techniques

We propose a multivariate model of returns that accounts for four of the stylised facts of financial data: heavy tails, skew, volatility clustering, and asymmetric dependence with the aim of improving the accuracy of risk estimates and increasing out-of-sample utility of investors’ portfolios. We accommodate volatility clustering, the generalised Pareto distribution to capture heavy tails and skew, and the skewed-t copula to provide for asymmetric dependence. The proposed approach produces more accurate VaR estimates than seven competing approaches across eight data sets encompassing five asset classes. We show that this produces portfolios with higher utility, and lower downside risk than alternative approaches including mean–variance. We confirm that investors can substantially increase utility by accounting for departures from normality.     

Price discovery in bitcoin futures

This paper studies the contribution of the newly launched future contracts to the bitcoin price discovery process. Using well-established methodologies in the literature of the evaluation of price discovery in financial markets, we find evidence that, although the volume of bitcoins traded in the decentralized spot market overwhelms that of the futures market, the latter plays a more important role in incorporating new information about the value of bitcoin. Our empirical investigation also provides evidence of strong bi-directional dependence in the intraday volatility of the spot and futures markets.     

A Model Confidence Set approach to the combination of multivariate volatility forecasts

In predicting conditional covariance matrices of financial portfolios, practitioners are required to choose among several alternative options, facing a number of different sources of uncertainty. A first source is related to the frequency at which prices are observed, either daily or intradaily. Using prices sampled at higher frequency inevitably poses additional sources of uncertainty related to the selection of the optimal intradaily sampling frequency and to the construction of the best realized estimator. Likewise, the choices of model structure and estimation method also have a critical role. In order to alleviate the impact of these sources of uncertainty, we propose a forecast combination strategy based on the Model Confidence Set [MCS] to adaptively identify the set of most accurate predictors. The combined predictor is shown to achieve superior performance with respect to the whole model universe plus three additional competitors, independently of the MCS or portfolio settings.     

Price volatility spillovers between supply chain and innovation of financial pledges in China

This study investigates the price volatility spillover of commodities in China using the Granger causality test and the variable structure Copula model, by employing data covering the daily average transaction prices of silver, copper, aluminum, rebar, and fuel oil from 2009 to 2017. The results show that the causal direction of price volatility spillover is uncertain, and that the impact from the correlation coefficients of copper and aluminum is the highest. Hence, there is a clear long-run price volatility spillover between silver and fuel oil as well as between silver and aluminum. Macroeconomic influences on different combinations of commodities present similar changing structure points, such as the debt crisis in Europe, the reduction of the quantitative easing monetary policy proposed by the U.S. Federal Reserve, and the promotion of RMB internationalization. Overall, our findings provide a theoretical reference for commercial banks to make full use of the volatility spillover effect between commodity pledges and their combinations, in order to achieve the goal of avoiding any price volatility risk.     

Crude oil price forecasting incorporating news text

Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long text to discover knowledge from them. Nonetheless, some of the existing research about text-based crude oil forecasting employs LDA to explore topics from news headlines, resulting in a mismatch between the short text and the topic model and further affecting the forecasting performance. Exploiting advanced and appropriate methods to construct high-quality features from news headlines becomes crucial in crude oil forecasting. This paper introduces two novel indicators of topic and sentiment for the short and sparse text data to tackle this issue. Empirical experiments show that AdaBoost.RT with our proposed text indicators, with a more comprehensive view and characterization of the short and sparse text data, outperforms the other benchmarks. Another significant merit is that our method also yields good forecasting performance when applied to other futures commodities.     

Systemic Risks Spillovers and Interdependence among Stock Markets: International Evidence with Covar‐Copulas

Empirical studies have provided ample evidence on the potential benefits of international diversification with portfolios that consist of both domestic and foreign assets. This coupled with sudden and periodic crashes in global and developed equity markets have stimulated the interest of investors to diversify across markets that have the potential to provide decorrelation with global markets during turbulent periods. At the same time, international diversification may intensify cross‐border listing of stocks with its antecedent implication of shocks transmission. The above have engendered renewed interest among researchers to explore the dependence levels and spillover effects of shocks among emerging and developed equity markets. This paper examines tail dependence structure and (extreme) systemic risks spillover effects among international equity markets using advanced econometric techniques that underpin the modelling of asset returns. We find evidence of low positive significant dependencies between all African markets and their developed counterparts, except for Egypt. Although no evidence of spillover effects to the markets in Africa was found, both unidirectional and bi‐directional causality between some African and developed equity markets is found, albeit with differences. We are unable to ascribe the dynamics in the causality structure to level of market integration. It is inferred that the degree of individual local markets interdependence with developed counterparts may reflect the relative size, liquidity and degree of foreign investors' participation.     

Discrimination of Observations into One of Two Elliptic Populations based on Monotone Training Samples

In this paper we deal with the problem of classifying a p-dimensional random vector into one of two elliptically contoured populations with unknown and distinct mean vectors and a common, but unknown, scale matrix. The classification procedure is based on two-step monotone training samples, one from each population, with the same monotone pattern. Our aim is to extend the classification procedure, which proposed recently by Chung and Han (Ann Ins Stat Math 52:544–556, 2000). This procedure is a linear combination of two discriminant functions, one based on the complete samples and the other on the incomplete samples. The performance of the proposed classification rule is compared with the plug-in method, this means with the classification rule which arises if the unknown parameters are substituted, into the usual classification rule, by their estimators. In order to apply the plug-in method, the MLE of the location parameters and of the common scale matrix of g ≥ 2 elliptically contoured populations are analytically obtained on the basis of two-step monotone training samples.     

基于消费者物价指数的养老金担保成本评估模型

探讨了以消费者物价指数为最低担保的缴费确定型养老金计划,并在应用Copula函数度量投资组合回报率与消费者物价指数增长率的相关关系基础上,建立了评估这种担保成本的模型,最后给出了担保成本的随机模拟算法。     

Cross country relations in European tourist arrivals

This paper introduces an optimized Multivariate Singular Spectrum Analysis (MSS) algorithm for identifying leading indicators. Exploiting European tourist arrivals data, we analyse cross country relations for European tourism demand. Cross country relations have the potential to aid in planning and resource allocations for future tourism demand by taking into consideration the variation in tourist arrivals across other countries in Europe. Our findings indicate with statistically significant evidence that there exists cross country relations between European tourist arrivals which can help in improving the predictive accuracy of tourism demand. We also find that MSSA has the capability of not only identifying leading indicators, but also forecasting tourism demand with far better accuracy in comparison to its univariate counterpart, Singular Spectrum Analysis.