Fuzzy modelling of static system optimum traffic assignment problem having multi origin-destination pair

Traffic congestion is an unpreventable problem to avoid in a transportation network and it has negative effects on traffic accident, time wasting, traffic delay and safety problem. Besides, in transportation networks, drivers do not want to deal with traffic jam while traversing between specified origin-destination pair. Therefore, traffic assignment (TA) is imperative to improve traffic management, transportation safety, time, and cost savings. System Optimum Traffic Assignment Problem (SOTAP) is a kind of TA model which aims to minimize the total system travel time on the network, and satisfies the flow conservation constraints. To model the SOTAP more realistically, the imprecise parameters can be taken as fuzzy. Therefore, in this paper, we focus on converting the conventional SOTAP to a fuzzy quadratic programming problem (QPP) which is named System Optimum Fuzzy Traffic Assignment Problem (SOFTAP). Here, link travel time is expressed with BPR function as generally used in the literature by converting to fuzzy except link-dependent parameters. Thus, the nonlinear objective function of SOFTAP is expressed in terms of fuzzy link flows and fuzzy link travel times. A solution approach from the literature is modified to the reconstructed SOFTAP.     

Network structure,portfolio diversification and systemic risk

We investigate the effect of portfolio diversification on banking systemic risk, where the network effect is incorporated. We analyze three kinds of interbank networks, namely, random networks, small-world networks and scale-free networks. We show that the effect of portfolio diversification on banking systemic risk depends on interbank network structures and shock types. First, systemic risk increases first and then reduces with the increase of the level of portfolio diversification in the case of the individual shock. Second, in the case of the systemic shock, systemic risk reduces with the increases of the level of portfolio diversification. Third, banking systems with scale-free network structures are the most stable, and those with small-world network structures are the most vulnerable.     

EMPIRICAL PERFORMANCE OF GARCH MODELS WITH HEAVY‐TAILED INNOVATIONS

We introduce a new type of heavy‐tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten‐Jagannathan‐Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy‐tailed distribution, the Student's t distribution and the normal inverse Gaussian distribution (NIG), using a variety of asset return series. Our results illustrate that there is no overwhelmingly dominant distribution in fitting the data under the GARCH framework, although the NRIG distribution performs slightly better than the other two types of distribution. For market indexes series, it is important to introduce both GJR‐terms and the NRIG distribution to improve the models’ performance, but it is ambiguous for individual stock prices series. Our results also show the GJR‐GARCH NRIG model has practical advantages in quantitative risk management. Finally, the convergence of numerical solutions in maximum‐likelihood estimation of GARCH and GJR‐GARCH models with the three types of heavy‐tailed distribution is investigated.     

Trade networks and economic fluctuations in Asian countries

The paper presents a new methodology, based on tensor decomposition, to map dynamic trade networks and to assess its strength in forecasting economic fluctuations at different periods of time in Asia. Using the monthly merchandise import and export data across 33 Asian economies, together with the US, EU and UK, we detect the community structure of the evolving network and we identify clusters and central nodes inside each of them. Our findings show that data are well represented by two communities, in which People's Republic of China and Japan play the major role. We then analyze the synchronisation between GDP growth and trade. Furthermore we apply our model to the prediction of economic fluctuations. Our findings show that the model leads to an increase in predictive accuracy, as higher order interactions between countries are taken into account.     

Predictive regressions under asymmetric loss: Factor augmentation and model selection

This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.     

Re‐vitalizing money demand in the Euro area. Still valid at the zero‐lower bound

The analysis of monetary developments has always been a cornerstone of the ECB's monetary analysis and, thus, of its overall monetary policy strategy. In this respect, money demand models provide a framework for explaining monetary developments and assessing price stability over the medium term. It is a well‐documented fact in the literature that, when interest rates are at the zero‐lower bound, the analysis of money stocks become even more important for monetary policy. Therefore, this paper re‐investigates the stability properties of M3 demand in the euro area in the light of the recent economic crisis. A cointegration analysis is performed over the sample period 1983 Q1 and 2015 Q1 and leads to a well‐identified model comprising real money balances, income, the long‐term interest rate and the own rate of M3 holdings. The specification appears to be robust against the Lucas critique of a policy dependent parameter regime, in the sense that no signs of breaks can be found when interest rates reach the zero‐lower bound. Furthermore, deviations of M3 from its equilibrium level do not point to substantial inflation pressure at the end of the sample. Excess liquidity models turn out to outperform the autoregressive benchmark, as they deliver more accurate CPI inflation forecasts, especially at the longer horizons. The inclusion of unconventional monetary policy measures does not contradict these findings.     

信息技术提高企业组织敏捷性的作用机理

从考察企业组织的敏捷性与交易成本的相互关系入手,对企业组织敏捷性的影响因素进行了剖析。详细分析了信息技术(IT)对提高企业组织敏捷性的作用机理。指出IT正是通过降低企业内、外部的交易成本、缩短流程时间、提高人的认识理性、扼制机会主义、促进企业组织结构、决策方式     

Macroeconomic News and Stock Returns in the United States and Germany

Abstract. Using daily data for the January 1997 to June 2002 period, we analyze similarities and differences in the impact of macroeconomic news on stock returns in the United States and Germany. We consider 27 different types of news for the United States and 12 different types of news for Germany. For the United States, we present evidence for asymmetric reactions of stock prices to news. In a boom (recession) period, bad (good) news on GDP growth and unemployment or lower (higher) than expected interest rates may be good news for stock prices. In the period under consideration there is little evidence for asymmetric effects in Germany. However, in the case of Germany, international news appears at least as important as domestic news. There is no evidence that US stock prices are influenced by German news. The analysis of bi-hourly data for Germany confirms these results.     

Is Volatility the Best Predictor of Market Crashes?

The objective of this paper is to determine the best predictor of equity market crashes by focusing particularly on volatility and market liquidity. In finance, volatility has traditionally been regarded as the best measure of market risk. However, this paper shows that the forecast value of market liquidity, in particular our modified calculated market depth, predicts equity market crashes much more accurately than does the forecast values of EGARCH or Implied Volatility.     

Generating Enhanced Fishery Rents by Internalizing Product Quality Characteristics

This paper demonstrates howadditional rents are generated in a fisherycharacterized by intraseasonal variation infish characteristics, including size,condition, and composition. Based on anexpanded conceptual model of the optimalharvest rule, fish characteristics affect preand post harvest production yields and outputprices. A dynamic empirical model, which uses asystem of quality characteristics and anhedonic equation, illustrates the complexrelationships and management choices associatedwith internalizing seafood qualitycharacteristics in a hake fishery. The modelretains the regulated open access managementsystem, but controls intertemporal andintersectoral quotas, production portfolios,and total allowable catch. Results demonstratethat including revenue-side effects frominternalizing fish quality can generatesignificantly greater rents and reduce therelative benefits of increased productionyields. If excluded, bioeconomic models canunderestimate the level of regulatory rentdissipation and overemphasize managementobjectives such as full utilization, whichcould misdirect processing decisions and resultin a suboptimal resource management plan.Implications for data collection,multidisciplinary analysis, and improvements inmarine resource management are discussed.