A new approach to bad news effects on volatility: the multiple-sign-volume sensitive regime EGARCH model (MSV-EGARCH)

In this paper, using daily data for six major international stock market indexes and a modified EGARCH specification, the links between stock market returns, volatility and trading volume are investigated in a new nonlinear conditional variance framework with multiple regimes and volume effects. Volatility forecast comparisons, using the Harvey-Newbold test for multiple forecasts encompassing, seem to demonstrate that the MSV-EGARCH complex threshold structure is able to correctly fit GARCH-type dynamics of the series under study and dominates competing standard asymmetric models in several of the considered stock indexes.

“剪刀差”问题的对策研究

本文首先指出了传统的房地产销售预测方法的不足。为了改进原有的预测方法 ,作者结合影响单个房地产销售量各项因素的特殊性和模糊性 ,运用模糊数学的理论与方法 ,建立了新的数学模型并将其成功地运用于实践 ,取得了较好的效果 ,其理论与方法具有较高的参考和应用价值。     

时序分析在地下洞室变形监测数据处理中的应用

时序分析法是广泛应用于多领域的统计分析方法,在岩土工程数值分析当中也有广泛应用,SAS/ETS(Ti me Series Forecasting)时序分析系统是一种智能化时序分析工具,本文运用SAS数据挖掘工具中时序分析组件对某水电站地下引水发电系统原位监控位移时间序列进行时序预测分析。实践表明,SAS/ETS时序分析方法基本能反映出蕴涵在位移序列中的岩体变形规律,预测结果与实测结果一致,可以运用于工程实践。     

Reviewing IFRS Goodwill Accounting Research: Implementation Effects and Cross-Country Differences

We review 42 studies from 2008 to early 2017 about IFRS goodwill accounting choices for recognition, impairment, and disclosure of goodwill, focusing on cross-country evidence of implementation effects. We develop a model of application of goodwill accounting based on IFRS 3, IAS 36, and country- and firm-level influences to analyze the research and to summarize existing evidence about goodwill accounting choices. We report evidence in support of IFRS accounting for goodwill recognition, impairment, and disclosure from many countries. However, evidence regarding value relevance is mixed. Overall, there is a lack of cross-country evidence regarding factors affecting goodwill accounting. Many studies show goodwill recognition, impairment, and disclosure are associated with economic and firm factors, and there is some evidence about the impact of managerial incentives and a lack of timeliness in impairment recognition. There is scope for more cross-country studies showing how institutional factors affect the application of IFRS 3 and IAS 36.     

On the statistical differences between binary forecasts and real-world payoffs

We map the difference between (univariate) binary predictions, bets and “beliefs” (expressed as a specific “event” will happen/will not happen) and real-world continuous payoffs (numerical benefits/harm from an event) and show the effect of their conflation and mischaracterization in the decision-science literature. We also examine the differences under thin and fat tails. The effects: [A] Spuriousness of many psychological results, particularly those documenting that humans overestimate tail probabilities. We quantify such conflations. [B] Being a “good forecaster” in binary space doesn’t lead to having a good actual performance, and vice versa, especially under nonlinearities. A binary forecasting record is likely to be a reverse indicator under some classes of distributions or deeper uncertainty. [C] Machine Learning: Some nonlinear payoff functions, while not lending themselves to verbalistic expressions, are well captured by ML or expressed in option contracts. Fattailedness: The difference is exacerbated in the power law classes of probability distributions.     

Forecasting inflation in Latin America with core measures

We explore the ability of core inflation to predict headline CPI annual inflation for a sample of eight developing economies in Latin America over the period January 1995–May 2017. Our in-sample and out-of-sample results are roughly consistent in providing robust evidence of predictability in four of the countries in our sample. Mixed evidence is found for the other four countries. The bulk of the out-of-sample evidence of predictability concentrates on the short horizons of one and six months. In contrast, at the longest horizon of 24 months, we only find out-of-sample evidence of predictability for two countries: Chile and Colombia, with robust results only for the latter. This is both important and challenging, given that the monetary authorities in our sample of developing countries are currently implementing or are taking steps toward the future implementation of inflation targeting regimes, which are based heavily on long-run inflation forecasts.     

Do macroeconomic forecasters use macroeconomics to forecast?

Faced with an uncertain future, forecasters often rely on textbook relationships to build a coherent narrative for their macroeconomic forecasts. We focus on two cornerstones of modern macroeconomics – Okun’s law and the Phillips curve – and examine whether or not professionals forecast in a way that is consistent with these. Using microdata from the US, Euro Area, and UK surveys of professional forecasters, we examine forecasts over the period 1981-2017 at the level of the individual and across different time horizons. Our findings show that the majority of forecasters produce their forecasts in a manner that is consistent with macroeconomic theory.     

Structured low-rank matrix completion for forecasting in time series analysis

This paper considers the low-rank matrix completion problem, with a specific application to forecasting in time series analysis. Briefly, the low-rank matrix completion problem is the problem of imputing missing values of a matrix under a rank constraint. We consider a matrix completion problem for Hankel matrices and a convex relaxation based on the nuclear norm. Based on new theoretical results and a number of numerical and real examples, we investigate the cases in which the proposed approach can work. Our results highlight the importance of choosing a proper weighting scheme for the known observations.     

The wisdom of large and small crowds: Evidence from repeated natural experiments in sports betting

Prediction markets have proved excellent tools for forecasting, outperforming experts and polls in many settings. But do larger markets, with a wider participation, perform better than smaller markets? This paper analyses a series of repeated natural experiments in sports betting. The Queen’s Club Tennis Championships are held every year, but every other year the Championships clash with a major soccer tournament. We find that tennis betting prices become significantly less informative when participation rates are affected adversely by the clashing soccer tournament. This suggests that measures which increase prediction market participation may lead to a greater forecast accuracy.     

The forecasting ability of solar and space weather data on NASDAQ’s finance sector price index volatility

We examine the impact of solar and space weather events on the Financial Select Sector SPDR Fund (XLF) price index volatility, spanning the period 1998-2018. Comparing MAPE and RMSFE forecasting criteria, for the ARIMA-GARCH model, augmented with exogenous variables, we find that solar and space weather variables contribute statistically significant information with regard to volatility forecasting.