Trading the foreign exchange market with technical analysis and Bayesian Statistics

In this study, the profitability of technical analysis and Bayesian Statistics in trading the EUR/USD, GBP/USD, and USD/JPY exchange rates are examined. For this purpose, seven thousand eight hundred forty-six technical rules are generated, and their profitability is assessed through a data snooping procedure. Then, the most promising rules are combined with a Naïve Bayes, a Relevance Vector Machine, a Dynamic Model Averaging, a Dynamic Model Selection and a Bayesian regularized Neural Network model. The findings show that technical analysis has value in foreign exchange trading, but the profit margins are small. On the other hand, Bayesian Statistics seems to increase the profitability of technical rules up to five times.     

人民币汇率变动与实际产出——基于协整的VECM分析

本文通过协整分析和VECM检验表明,实际产出、出口额、实际利用FDI和人民币实际有效汇率四个指标变量之间存在长期均衡关系和短期动态关系。长期内人民币实际有效汇率与实际产出呈负相关关系,人民币实际有效汇率每上升1％,实际产出将下降0．26％。短期内,人民币实际有效汇率变化主要对出口产生冲击,并与出口、FDI一起对实际产出产生影响。     

Modelling and trading the EUR/USD exchange rate at the ECB fixing

The motivation for this paper is to investigate the use of alternative novel neural network (NN) architectures when applied to the task of forecasting and trading the euro/dollar (EUR/USD) exchange rate, using the European Central Bank (ECB) fixing series with only auto-regressive terms as inputs. This is done by benchmarking four different NN designs representing a higher-order neural network (HONN), a Psi Sigma Network and a recurrent neural network with the classic multilayer perception (MLP) and some traditional techniques, either statistical such as an auto-regressive moving average model, or technical such as a moving average convergence/divergence model, plus a naïve strategy. More specifically, the trading performance of all models is investigated in a forecast and trading simulation on the EUR/USD ECB fixing time series over the period 1999–2007 using the last one and half years for out-of-sample testing, an original feature of this paper. We use the EUR/USD daily fixing by the ECB as many financial institutions are ready to trade at this level and it is therefore possible to leave orders with a bank for business to be transacted on that basis. As it turns out, the MLP does remarkably well and outperforms all other models in a simple trading simulation exercise. However, when more sophisticated trading strategies using confirmation filters and leverage are applied, the HONN network produces better results and outperforms all other NN and traditional statistical models in terms of annualized return.     

Exchange rate determination and inter-market order flow effects

The dependence of foreign exchange rates on order flow is investigated for four major exchange rate pairs, EUR/USD, EUR/GBP, GBP/USD and USD/JPY, across sampling frequencies ranging from 5 min to 1 week. Strong explanatory power is discovered for all sampling frequencies. We also uncover cross-market order flow effects, e.g. GBP exchange rates are very strongly influenced by EUR/USD order flow. We proceed to investigate the predictive power of order flow for exchange rate changes, and it is shown that the order flow specifications reduce RMSEs relative to a random walk for all exchange rates at high-frequencies and for EUR/USD and USD/JPY at lower sampling frequencies.     

The effectiveness of the Fed’s quantitative easing policy: New evidence based on international interest rate differentials

This paper explores the effects of non-standard monetary policies on international yield relationships. We first document that long-term rates followed a common global downward trend that had already manifested itself prior to the financial crisis. The bond-buying operations (commonly dubbed Quantitative Easing (QE)) of the US Federal Reserve did not disturb this global co-movement – i.e. the global downward trend in interest rates. We model the relationship between USD and euro (riskless) long-term interest rates using a Cointegrated Vector Autoregressive Model (CVAR) employing recursive estimation methods. We find no evidence that QE1 (or the QE episodes) destabilized the transatlantic interest-rate relationship, nor the relationship between interest rates and the US dollar exchange rate. A robustness test using a Vector Autoregressive Model (VAR) with interest rates, inflation rates and output differentials for 11 countries (relative to US) yielded the same result. There is thus little evidence that central bank bond-buying in the US had an independent, distinct impact on US interest rates.     

2013年8腘际金融市场运行综述

2013年8月，美元指数先降后升，总体上涨，欧元兑美元汇率先升后降，总体下跌，英镑兑美元汇率总体上涨，美元兑日元汇率先降后升；美元短期Libor下降，欧元、英镑短期Libor上升，日元短期Libor持平；美国、德国、英国中长期国债收益率大幅上升，日本中长期国债收益率下降；主要股指大幅下跌。     

The High‐Frequency Response of the EUR‐USD Exchange Rate to ECB Communication

We investigate the impact of the European Central Bank's monetary policy communication during the press conference held after the monthly Governing Council meeting on the EUR‐USD exchange rate in high frequency. Based on the method of Content Analysis, we construct communication indicators for the introductory statement and find that communication with respect to future price developments is most relevant. In response to statements about increasing risks to price stability the EUR appreciates on impact. To the contrary, communication about economic activity and monetary aggregates does not generate significant exchange rate reactions.     

LINEAR RELATIONSHIP BETWEEN THE AUD/USD EXCHANGE RATE AND THE RESPECTIVE STOCK MARKET INDICES: A COMPUTATIONAL FINANCE PERSPECTIVE

In the recent era, computational intelligence techniques have found an increased popularity in addressing varied financial issues, including foreign exchange rate prediction. This article, through an intelligent system research framework, relates the Australian dollar (AUD)/US dollar (USD) exchange rate to the Australian and the US stock market indices. Information for exchange rate, All Ordinaries Index (AOI) and Dow Jones Industrial Average (DJI) for the trading days over the period January 1991–May 2011 is considered in this research. Utilizing a set of statistical and computational intelligence techniques, the research establishes that the AUD/USD exchange rate is best estimated by a linear forecast model compared with the nonlinear and ensemble‐based intelligent system models. This research further highlights that, among the competing linear models, the model with both the stock market indices and historical exchange rate values as the predictors is the best forecaster. Parameters of the linear model are deduced through a Monte Carlo stochastic approach. Relative importance of the predictors is also studied, and the influence of historical exchange rates, the immediate impact of AOI and the lagged effect of DJI are noted. Copyright © 2012 John Wiley & Sons, Ltd.     

Deep networks for predicting direction of change in foreign exchange rates

Trillions of dollars are traded daily on the foreign exchange (forex) market, making it the largest financial market in the world. Accurate forecasting of forex rates is a necessary element in any effective hedging or speculation strategy in the forex market. Time series models and shallow neural networks provide acceptable point estimates for future rates but are poor at predicting the direction of change and, hence, are not very useful for supporting profitable trading strategies. Machine learning classifiers trained on input features crafted based on domain knowledge produce marginally better results. The recent success of deep networks is partially attributable to their ability to learn abstract features from raw data. This motivates us to investigate the ability of deep convolution neural networks to predict the direction of change in forex rates. Exchange rates for the currency pairs EUR/USD, GBP/USD and JPY/USD are used in experiments. Results demonstrate that trained deep networks achieve satisfactory out‐of‐sample prediction accuracy.     

Technological bias at the exchange rate market

Prediction of exchange rates has been a topic for debate in economic literature since the late 1980s. The recent development of machine learning techniques has spurred a plethora of studies that further improves the prediction models for currency markets. This high-tech progress may create challenges for market efficiency along with information asymmetry and irrationality of decision-making. This technological bias emerges from the fact that recent innovative approaches have been used to solve trading tasks and to find the best trading strategies. This paper demonstrates that traders can leverage technological bias for financial market forecasting. Those traders who adapt faster to the changes in market innovations will get excess returns. To support this hypothesis we compare the performance of deep learning methods, shallow neural networks with baseline prediction methods and a random walk model using daily closing rate between three currency pairs: Euro and US Dollar (EUR/USD), British Pound and US Dollar (GBP/USD), and US Dollar and Japanese Yen (USD/JPY). The results demonstrate that deep learning achieves higher accuracy than alternate methods. The shallow neural network outperforms the random walk model, but cannot surpass ARIMA accuracy significantly. The paper discusses possible outcomes of the technological shift for financial market development and accounting conforming also to adaptive market hypothesis.     

Volatility transmission in emerging European foreign exchange markets

This paper studies the dynamics of volatility transmission between Central European (CE) currencies and the EUR/USD foreign exchange using model-free estimates of daily exchange rate volatility based on intraday data. We formulate a flexible yet parsimonious parametric model in which the daily realized volatility of a given exchange rate depends both on its own lags as well as on the lagged realized volatilities of the other exchange rates. We find evidence of statistically significant intra-regional volatility spillovers among the CE foreign exchange markets. With the exception of the Czech and, prior to the recent turbulent economic events, Polish currencies, we find no significant spillovers running from the EUR/USD to the CE foreign exchange markets. To measure the overall magnitude and evolution of volatility transmission over time, we construct a dynamic version of the Diebold–Yilmaz volatility spillover index and show that volatility spillovers tend to increase in periods characterized by market uncertainty.     

外汇市场、股票市场及预期退货膨胀对货币需求的影响

本文利用2005年7月到2012年12月的月度数据,使用菲利普斯-奥肯曲线计算预期通货膨胀率,将汇率、股票市场以及预期通货膨胀率纳入货币需求函数模型,通过协整检验、VEC模型以及VAR模型,研究了汇率、股市以及通胀预期对实际货币需求的影响,结果发现汇率、股市收益率以及通胀预期对实际货币需求的影响具有统计显著性,但是影响程度很小。     

Forecasting the Term Structure of South African Government Bond Yields

This article uses the parsimonious dynamic Nelson–Siegel model to fit the yields of South African government bonds. We find that the dynamic Nelson–Siegel model has good fitting abilities for all maturities. We further forecast the term structure by seven different dynamic Nelson–Siegel models with time series models. We find that the DNS–VAR–GARCH model is useful for forecasting the short-term rates, the DNS–VAR best predicts the medium-term rates, and the DNS–RW best predicts the long-term rates. In addition, the dynamic Nelson–Siegel models provide better forecasts of yield data than a random walk model, especially for the 12-month forecasting horizons.     

The Taylor Rule and Forecast Intervals for Exchange Rates

In this paper, we examine the Meese–Rogoff puzzle from a different perspective: out‐of‐sample interval forecasting. While most studies in the literature focus on point forecasts, we apply semiparametric interval forecasting to a group of exchange rate models. Forecast intervals for 10 OECD exchange rates are generated and the performance of the empirical exchange rate models are compared with the random walk. Our contribution is twofold. First, we find that in general, exchange rate models generate tighter forecast intervals than the random walk, given that their intervals cover out‐of‐sample exchange rate realizations equally well. Our results suggest a connection between exchange rates and economic fundamentals: economic variables contain information useful in forecasting distributions of exchange rates. We also find that the benchmark Taylor rule model performs better than the monetary, PPP and forward premium models, and its advantages are more pronounced at longer horizons. Second, the bootstrap inference framework proposed in this paper for forecast interval evaluation can be applied in a broader context, such as inflation forecasting.     

FX volatility forecasts and the informational content of market data for volatility

The paper examines the medium-term forecasting ability of several alternative models of currency volatility. The data period covers more than eight years of daily observations, January 1991 to March 1999, for the spot exchange rate, 1- and 3-month volatility of the DEM/JPY, GBP/DEM, GBP/USD, USD/CHF, USD/DEM and USD/JPY. Comparing with the results of ‘pure’ time series models, the reported work investigates whether market implied volatility data can add value in terms of medium-term forecasting accuracy. This is done using data directly available from the marketplace in order to avoid the potential biases arising from ‘backing out’ volatility from a specific option pricing model. On the basis of the over 34 000 out-of-sample forecasts produced, evidence tends to indicate that, although no single volatility model emerges as an overall winner in terms of forecasting accuracy, the ‘mixed’ models incorporating market data for currency volatility perform best most of the time.     

The Construction and Application of a New Exchange Rate Forecast Model Combining ARIMA with a Chaotic BP Algorithm

This article introduces a new model that combines an ARIMA with a chaotic BP (Backforward Propagation Neural Network) algorithm for exchange rate forecasting purposes, which is based on sample data collected from January 4, 2010, to October 20, 2011. The forecast of the exchange rate trend is then provided for the subsequent twenty-five days. Other models are also constructed, such as the ARIMA, BP, ARIMA, and BP algorithms, in order to evaluate the forecast accuracy. Based on our results, the combination of an ARIMA and a chaotic BP algorithm outperforms all other models in terms of the statistical accuracy of short-term forecasts.     

Central bank interventions and implied exchange rate correlations

This paper examines the effects of the foreign exchange market interventions by the Bank of Japan on the ex ante correlations between the JPY/USD, EUR/USD, and GBP/USD exchange rates. The correlation estimates used in the analysis are derived from the market prices of OTC currency options. The results show that central bank interventions significantly affect the market expectations about future exchange rate co-movements. In particular, we find that interventions tend to temporarily increase the ex ante correlations among the major exchange rates. However, our results also suggest that intervention episodes are associated with lower-than-average levels of exchange rate correlations.     

认购权证与标的股票间的价格相关关系实证研究

为系统分析我国认购权证市场与其标的股票市场间的价格相关关系，发现权证市场与股票市场间的联系。本文以12组样本认购权证目收盘价和标的股票的目收盘价为观测对象，运用ADF单位根检验、Johansen协整检验、向量误差修正模型（VECM）、Granger因果检验和Hasbrouck方差分解方法，考察了两个时序数列间的长期均衡关系、短期动态关系、Granger因果关系和两市在价格发现功能中作用的大小、反映信息的效率，得出了研究结论。     

A data mining approach to financial time series modelling and forecasting

This paper describes one of the relatively new data mining techniques that can be used to forecast the foreign exchange time series process. The research aims to contribute to the development and application of such techniques by exposing them to difficult real-world (non-toy) data sets. The results reveal that the prediction of a Radial Basis Function Neural Network model for forecasting the daily $US/$NZ closing exchange rates is significantly better than the prediction of a traditional linear autoregressive model in both directional change and prediction of the exchange rate itself. We have also investigated the impact of the number of model inputs (model order), the number of hidden layer neurons and the size of training data set on prediction accuracy. In addition, we have explored how the three different methods for placement of Gaussian radial basis functions affect its predictive quality and singled out the best one. Copyright © 2001 John Wiley & Sons, Ltd.     

An empirical study of portfolio-balance and information effects of order flow on exchange rates

We propose a simple structural model of exchange rate determination which is inspired by the analytical framework recently forward by Bacchetta and van Wincoop (2006) and allows us to disentangle the portfolio-balance and information effects of order flow on exchange rates. We estimate this model employing an innovative transaction data-set that covers all indirect foreign exchange transactions completed in the USD/EUR market via EBS and Reuters between August 2000 and January 2001. Our results indicate that the strong contemporaneous correlation between order flow and exchange rates is largely due to portfolio-balance effects. This result also appears to carry through the four FX intervention events that appear in our sample.