A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries

In this paper we propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. The new model is a multiple regime smooth transition extension of the Heterogeneous Autoregressive (HAR) model, which is specifically designed to model the behavior of the volatility inherent in financial time series. The model is able to simultaneously approximate long memory behavior, as well as describe sign and size asymmetries. A sequence of tests is developed to determine the number of regimes, and an estimation and testing procedure is presented. Monte Carlo simulations evaluate the finite-sample properties of the proposed tests and estimation procedures. We apply the model to several Dow Jones Industrial Average index stocks using transaction level data from the Trades and Quotes database that covers ten years of data. We find strong support for long memory and both sign and size asymmetries. Furthermore, the new model, when combined with the linear HAR model, is viable and flexible for purposes of forecasting volatility.     

Dissecting models' forecasting performance

The fact that the predictive performance of models used in forecasting stock returns, exchange rates, and macroeconomic variables is not stable and varies over time has been widely documented in the forecasting literature. Under these circumstances excessive reliance on forecast evaluation metrics that ignores this instability in forecasting accuracy, like squared errors averaged over the whole forecast evaluation sample, masks important information regarding the temporal evolution of relative forecasting performance of competing models. In this paper we suggest an approach based on the combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) of Welch and Goyal (2008) and the Bayesian change point analysis of Barry and Hartigan (1993) that tracks the contribution of forecast errors to the aggregate measures of forecast accuracy observation by observation. In doing so, it allows one to track the evolution of the relative forecasting performance over time. We illustrate the suggested approach by using forecasts of the GDP growth rate in Switzerland.     

Regime switches and commonalities of the cryptocurrencies asset class

In this paper we test for regime changes and possible regime commonalities in the price dynamics of Bitcoin, Ethereum, Litecoin and Monero, as representatives of the cryptocurrencies asset class. Several parametric models are considered for the joint dynamics of the basket price where parameters are modulated through a Hidden Markov Chain with finite state space. Best specifications within Gaussian and Autoregressive models for price differences are selected by means of the AIC and BIC information criteria and through an out-of-sample forecasting performance. The empirical results, within the period January 2016 to October 2019, suggest that three or four states may be relevant to describe the dynamics of each individual cryptocurrency, depending on the selection criteria, while the entire basket displays at most three common states. Finally, we show how the identification of appropriate models may be exploited in order to build profitable investment strategies on the considered cryptocurrencies.     

Economic policy uncertainty and stock market returns: New evidence

This paper investigates the predictive performance of the Chinese economic policy uncertainty (EPU) index constructed by Davis, Liu, and Sheng (2019) in forecasting the returns of China’s stock market. Using the univariate and bivariate predictive regression model, we confirm that the monthly EPU index can significantly and negatively impact the next month’s stock returns, and has better out-of-sample predictability than the existing EPU index and several macroeconomic variables. By comparing the forecasting effect of the EPU index before and during special events with sharply increased uncertainty, we find that the EPU’s forecasting power decline rapidly when an event of sharply increased uncertainty occurs. Finally, our conclusions are consistent through a batch of robustness tests.     

Harnessing the wisdom of crowds: Decision spaces for prediction markets

The increased metabolism of business in the modern world has served to heighten both the frequency and the difficulty of organizational decision making. Practitioners and academics are constantly looking for decision-making mechanisms that can be used to address these challenges. One recently emerged mechanism is prediction markets: a group decision-making tool that uses a market mechanism to rapidly aggregate information held by large, diverse groups of participants. Prediction markets have a number of benefits and have been demonstrably successful in a number of contexts; however, it is important to recognize that they are suited to some types of decisions and contexts but not to others. This article examines the benefits of prediction markets and develops a framework that can be used to identify in which situations prediction markets can be profitably deployed within organizations. It also provides a roadmap for practitioners to use to guide their own organizational deployment of prediction markets.     

190水仓变电所交叉口底板组合锚注锚索设计方案

文章基于190水仓变电所交岔点巷道支护现状,对采用底板组合大锚索对底板进行锚注加固技术的可行性进行探讨,并阐述该加固方案的具体实施办法。     

USER INFLUENCE ON THE RELATIONSHIP BETWEEN FORECAST ACCURACY,APPLICATION AND LOGISTICS PERFORMANCE

When companies develop and apply more accurate forecasts in their planning and management activities, they have the potential to improve performance throughout their organization and across the supply chain. To realize these improvements, however, companies must (1) implement techniques and practices that improve forecast accuracy, and (2) integrate the more accurate forecasts into their planning and management activities. While much research has focused on accomplishing the first of these requirements, few studies have investigated the important role that user's play in the application of forecasts for logistics planning and management. This article establishes a connection between forecast performance, user perceptions of the quality of forecasts they receive, the extent that they use the forecasts and the resulting impact on logistics performance.     

先进文化战略视觉下的校园廉政文化建设研究与实践

大学校园廉政文化建设要从社会主义先进文化建设的战略高度加以认识和重视。开设廉洁修身专题教育课程是校园廉政文化建设的主渠道和主阵地;充分利用学校各种教育资源,倡导廉政文化、弘扬廉政精神,强化廉政文化的共识和理论导向。在推进和深化廉政文化建设中,做到六个结合,确保校园廉政文化建设的实效。     

Which are the SIFIs? A Component Expected Shortfall approach to systemic risk

This paper proposes a component approach to systemic risk which allows to decompose the risk of the aggregate financial system (measured by Expected Shortfall) while accounting for the firm characteristics. Developed by analogy with the Component Value-at-Risk concept, our new systemic risk measure, called Component ES, presents several advantages. It is a hybrid measure, which combines the Too Interconnected To Fail and the Too Big To Fail logics. CES relies only on publicly available daily data and encompasses the popular Marginal ES measure. CES can be used to assess the contribution of a firm to systemic risk at a precise date but also to forecast its contribution over a certain period. The empirical application verifies the ability of CES to identify the most systemically risky firms during the 2007–2009 financial crisis. We show that our measure identifies the institutions labeled as SIFIs by the Financial Stability Board.