Hedging versus not hedging: strategies for managing foreign exchange transaction exposure

This paper compares a number of strategies for managing foreign exchange exposures. The strategies are never hedging, hedging every exposure using a forward exchange contract, and hedging on selective occasions using a forward exchange contract. With regard to the selective hedging, the decision as to whether to hedge or not depends on the future spot exchange rate as determined by a number of forecasting techniques. The techniques include the random walk, the large premia model and a volatility model. The paper considers the USD vis a vis the AUD, SGD and JPY. The results are mixed and show that for the period 1992 to 2003 the Australian exporter is better off always hedging while the Singapore and Japanese exporters are better off never hedging. The various management strategies are compared using Sharpe’s model and the minimum variance model though it seems the results are not sensitive to use of either.Selective foreign exchange currency hedging; random walk; large premia model;

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Learning continuous representations of nodes is attracting growing interest in both academia and industry recently, due to their simplicity and effectiveness in a variety of applications. Most of existing node embedding algorithms and systems are capable of processing networks with hundreds of thousands or a few millions of nodes. However, how to scale them to networks that have tens of millions or even hundreds of millions of nodes remains a challenging problem. In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system. On the CPU end, augmented edge samples are parallelly generated by random walks in an online fashion on the network, and serve as the training data. On the GPU end, a novel parallel negative sampling is proposed to leverage multiple GPUs to train node embeddings simultaneously, without much data transfer and synchronization. Moreover, an efficient collaboration strategy is proposed to further reduce the synchronization cost between CPUs and GPUs. Experiments on multiple real-world networks show that GraphVite is super efficient. It takes only about one minute for a network with 1 million nodes and 5 million edges on a single machine with 4 GPUs, and takes around 20 hours for a network with 66 million nodes and 1.8 billion edges. Compared to the current fastest system, GraphVite is about 50 times faster without any sacrifice on performance.Comment: accepted at WWW 201