Abstract: Any real life situation where there are nodes and edges, can be modeled as graphs. Road networks are one such example where roads can be modeled as edges and nodes can be modeled as cities. Such a model can be used to analyze and optimize various processes that take place in a road network. These models are commonly used to optimize the distance or time of travel between cities. In this study a model is developed where the speed of a vehicle is a linear function of the traffic volume. This model can be used identify locations of possible traffic congestion. When traffic congestion is observed in a road network traffic planners tend to add extra links to divert some of the traffic. This act which is done with the good intention sometimes worsens the situation. Such a situation can occur in a road network where there are two parallel roads between two cities and the two roads are connected in between by one or more links. This paper explores the criteria under which this problem can ari

• Publication Date: 05-Jan-2014
• DOI: 10.15224/978-981-07-8859-9-64
• Views: 0
• Downloads: 0

Abstract: Transmission mechanisms of volatility between crude oil markets have drawn the attention of numerous academics and practitioners because they both play crucial roles in portfolio and risk management in crude oilmarkets. However, there has been no consensus on the evidence of volatility spillover between WTI and Brent oil prices in the literature. In this context, we reexamined the volatility linkages between two representative crude oil markets using a VECM and an asymmetric bivariate GARCH model. First, looking at the return transmission through the VECM test, we found a long-run equilibrium and bidirectional relationshipbetween two crude oil markets. However, the estimation results of the GARCH-BEKK model suggest that there is unidirectional volatility spillover from the WTI market to the Brent market, implying that the WTI markettends to exert influence over the Brent market and not vice versa. Regarding asymmetric volatility transmission, we also found that bad news volatility in t

• Publication Date: 05-Jan-2014
• DOI: 10.15224/978-981-07-8859-9-65
• Views: 0
• Downloads: 0