Exchange rate forecast: a note

Wong Hock Tsen
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引用次数: 1

Abstract

The exchange rate forecast is an important topic in international finance especially after the breakdown of the Bretton Woods system in 1973. Firms that involve in international business need to know the future exchange rate for various and accurate decision making in firms such as financing, investing and hedging. An accurate exchange rate forecast is not only important to firms involved in international business but also to households, governments and international organisations engage in international transaction [1]. Nonetheless, exchange rate forecast is not an easy task. An accurate forecast is unlikely to be obtained. There is no single forecasting method that is superior for obtaining accurate exchange rate all the time and for different exchange rate. Generally, exchange rate forecast methods can be classified according to technical forecasting, fundamental forecasting, marketbased forecasting, machine-learning based forecasting and mixed forecasting. Technical forecasting inspects the exchange rate history or studies the chart of exchange rate to find pattern that may recurrent in the future. Fundamental forecasting examines the relationship between exchange rate and other variable or investigates the intrinsic value of the exchange rate. Market-based forecasting explores the expectation of the market on the future exchange rate. Machine-learning based forecasting involves forecasting by using artificial neural network, which data are assumed to be non-linear [2]. Mixed forecasting is a composite of two or more methods. The same or different weight can be assigned to each method in mixed forecasting.
汇率预测:注意事项
汇率预测是国际金融领域的一个重要课题,尤其是1973年布雷顿森林体系崩溃后。从事国际业务的公司需要了解未来汇率,以便在融资、投资和对冲等业务中做出各种准确的决策。准确的汇率预测不仅对从事国际贸易的企业很重要,而且对从事国际交易的家庭、政府和国际组织也很重要[1]。但是,汇率预测不是一件容易的事情。不太可能得到准确的预报。没有一种预测方法在任何时候都能获得准确的汇率,也没有一种预测方法对不同的汇率都有优势。一般来说,汇率预测方法可以分为技术预测、基本面预测、基于市场预测、基于机器学习预测和混合预测。技术预测通过考察汇率历史或研究汇率走势图来发现未来可能反复出现的模式。基本面预测考察的是汇率与其他变量之间的关系,或考察汇率的内在价值。市场预测探讨市场对未来汇率的预期。基于机器学习的预测涉及使用人工神经网络进行预测,假设数据是非线性的[2]。混合预测是两种或两种以上方法的综合。在混合预测中,每种方法可以赋予相同或不同的权重。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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