Article

Brexit and its Impact on the Pound in the Foreign Exchange Market

November 2019
The Economists' Voice 16(1)

DOI:10.1515/ev-2019-0026

Authors:

City, University of London

In this paper we outline the impact and likely future impact of Brexit on the pound. We argue that Brexit implies a significant depreciation of the pound and the degree of depreciation required is heavily linked to whether there will be a soft or hard Brexit. We find that the pound has had broadly similar depreciations to date against both the dollar and the euro. Brexit has considerably raised UK economic policy uncertainty and this, in turn, has at times led to an significant increase in future implied volatility of the pound. While there is an overall link between the state of the ongoing Brexit negotiations with the European Union and movements in the pound in the foreign exchange market, the link is not especially strong unless the perception that the negotiations are going badly has exceeded 60%.

Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques

Article

Full-text available

Mar 2021
COMPLEXITY

Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange (FX) market has become an important focus of both academic and practical research. There are many reasons why FX is important, but one of most important aspects is the determination of foreign investment values. Therefore, FX serves as the backbone of international investments and global trading. Additionally, because fluctuations in FX affect the value of imported and exported goods and services, such fluctuations have an important impact on the economic competitiveness of multinational corporations and countries. Therefore, the volatility of FX rates is a major concern for scholars and practitioners. Forecasting FX volatility is a crucial financial problem that is attracting significant attention based on its diverse implications. Recently, various deep learning models based on artificial neural networks (ANNs) have been widely employed in finance and economics, particularly for forecasting volatility. The main goal of this study was to predict FX volatility effectively using ANN models. To this end, we propose a hybrid model that combines the long short-term memory (LSTM) and autoencoder models. These deep learning models are known to perform well in time-series prediction for forecasting FX volatility. Therefore, we expect that our approach will be suitable for FX volatility prediction because it combines the merits of these two models. Methodologically, we employ the Foreign Exchange Volatility Index (FXVIX) as a measure of FX volatility. In particular, the three major FXVIX indices (EUVIX, BPVIX, and JYVIX) from 2010 to 2019 are considered, and we predict future prices using the proposed hybrid model. Our hybrid model utilizes an LSTM model as an encoder and decoder inside an autoencoder network. Additionally, we investigate FXVIX indices through subperiod analysis to examine how the proposed model’s forecasting performance is influenced by data distributions and outliers. Based on the empirical results, we can conclude that the proposed hybrid method, which we call the autoencoder-LSTM model, outperforms the traditional LSTM method. Additionally, the ability to learn the magnitude of data spread and singularities determines the accuracy of predictions made using deep learning models. In summary, this study established that FX volatility can be accurately predicted using a combination of deep learning models. Our findings have important implications for practitioners. Because forecasting volatility is an essential task for financial decision-making, this study will enable traders and policymakers to hedge or invest efficiently and make policy decisions based on volatility forecasting. 1. Introduction Among various financial asset markets, the foreign exchange (FX) market has become increasingly volatile and fluid over the past decade. According to data released by BIS (Bank for International Settlements) in April of 2019, the global trading volume of FX commodity markets was

6.6 trillion per day, representing a 30% increase compared to April of 2016 (

5.1 trillion). With the advent of globalization and increased demand for overseas investment, the number of FX transactions has increased rapidly based on investments in companies in various countries. Additionally, FX rates significantly affect the estimation of currency risks and profits for international trades. Governments and policymakers are keeping a close watch on FX fluctuations to perform risk management. Therefore, FX is considered to be the most important financial index for international monetary markets (Huang et al. [1]). In addition to FX rates, FX volatility has also been a significant source of concern for practitioners. FX volatility is defined by fluctuations in FX rates, so it is also known as a measure of FX risk. Because FX risk is directly linked to transaction costs related to international trade, it is of great importance for multinational firms, financial institutions, and traders who wish to hedge currency risks. In this regard, FX volatility has affected the external sector competitiveness of international trade and the global economy. In particular, financial asset price volatility is a crucial concern for scholars, investors, and policymakers. This is because volatility is important for derivative pricing, hedging, portfolio selection, and risk management (see Vasilellis and Meade [2], Knopf et al. [3], Brownlees and Gallo [4], Gallo and Otranto [5], and Bollerslev et al. [6]). Therefore, the forecasting and modeling of volatility have recently become the focus of many empirical studies and theoretical investigations in academia. Forecasting volatility accurately remains a crucial challenge for scholars. Because many academics and practitioners are interested in volatility, many studies on volatility prediction have been reported. In these studies, many approaches have been utilized for forecasting. The autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) models proposed by Bollerslev [7] are mainly used to predict volatility (Vee et al. [8], Dhamija and Bhalla [9], Bala and Asemota [10], Kambouroudis et al. [11], and Köchling et al. [12]). Various characteristics of volatility, such as leverage effects, volatility clustering, and persistence (Cont [13] and Cont [14]), are the main reasons for employing GARCH-based models. Based on the recent development of artificial neural network (ANN) models, the use of ANN methods for forecasting volatility has increased (Pradeepkumar and Ravi [15], Liu [16], Ramos-Pérez et al. [17], and Bucci [18]). Previous studies have employed various ANN models, such as the random forest (RF) (Breiman [19]), support vector machine (SVM) (Cortes and Vapnik [20]), and long short-term memory (LSTM) (Hochreiter and Schmidhuber [21]). Several studies have shown that ANN methods outperform GARCH-based models for forecasting time series (see Pradeepkumar and Ravi [15], Liu [16], and Bucci [18]). Additionally, hybrid models based on ANNs and GARCH-type models have been introduced (Hajizadeh et al. [22], Kristjanpoller et al. [23], Kristjanpoller and Minutolo [24], Kim and Won [25], Baffour et al. [26], and Hu et al. [27]). Such models are reported to have advantages compared to using ANNs or GARCH-based models alone. Additional literature on this topic will be covered in Section 2. Based on the discussion above, we focus on volatility forecasting based on FX volatility. As measures of FX volatility, we adopt three FX volatility indexes (FXVIXs), namely, the FX euro volatility index (EUVIX), FX British pound volatility index (BPVIX), and FX yen volatility index (JYVIX), which are equally weighted indices of the Chicago Board Option Exchange’s (CBOE’s) 30 day implied volatility readings for the euro (EUR), pound sterling (GBP), and Japanese yen (JPY), respectively. Because the three currency pairs of EUR/USD, USD/JPY, and GBP/USD are the three most heavily traded currency pairs on the FX market, we selected the three corresponding FXVIX indices. Additionally, these indexes reflect global economic trends (see Ishfaq et al. [28], Dicle and Dicle [29], and Pilbeam [30]). As mentioned previously, the forecasting of volatility in the FX market is important for global firms, financial institutions, and traders who wish to hedge currency risks (see Guo et al. [31], Abdalla [32], and Menkhoff et al. [33]). Practically, the FX market consists of three associated components: spot transactions, forward transactions, and derivative contracts (Baffour et al. [26]). Additionally, because FX was originally defined by two currencies, FX has more observable factors that affect changes compared to other financial indices. Furthermore, according to Liu et al. [34], the periodic characteristics of the FX market are some of the main reasons why it is difficult to predict changes in the FX market. Therefore, we utilize ANN models as data-driven methods, rather than model-driven methods such as GARCH-type models, to forecast the three aforementioned FXVIXs. In particular, we employ the LSTM and autoencoder (Rumelhart et al. [35]) models as ANN techniques. We propose a hybrid neural network model based on these two models. To combine an autoencoder with LSTM, we apply LSTM as an encoder and decoder for sequence data inside an autoencoder network. Therefore, the proposed hybrid model can leverage the advantages of both the autoencoder and LSTM. A detailed discussion of this topic is presented in Section 3. Methodologically, we adopt a machine learning algorithm (LSTM) to implement an autoencoder-LSTM model for forecasting FXVIXs from 2010 to 2019. We optimize the adopted algorithms using a grid search procedure provided by Full-Stack Python. Testing is also performed using subperiod analysis to investigate whether data deviations and outliers affect model training. Such subperiod analysis has been commonly implemented in previous studies (Sharma et al. [36], García and Kristjanpoller [37], Ramos-Pérez et al. [17], and Choi and Hong [38]). Specifically, we split the entire sample period into three subperiods called Period 1 (January, 2010 to December, 2015), Period 2 (January, 2016 to December, 2016), and Period 3 (January, 2017 to December, 2019). Period 2 exhibits uncertainty in the European market based on the Brexit movement. In this manner, we investigate the accuracy of prediction and model performance according to different data states. There are two major aspects of this study that differ from previous studies. First, we use FXVIXs, which play key roles in the FX market. Although previous empirical studies have predicted various types of financial asset price volatility using various models, research on forecasting FXVIXs is scarce. Additionally, research on FX price prediction and volatility prediction using various approaches is being conducted, but research on the prediction of the FXVIX is relatively rare. Therefore, it is necessary to predict FXVIX volatility. Second, we propose a hybrid model based on an autoencoder and LSTM to forecast the three FXVIXs. LSTM is known to be good at forecasting time series (Fischer and Krauss [39], Kumar et al. [40], and Muzaffar and Afshari [41]), and one of the advantages of an autoencoder is that it can automatically extract features from input data (Phaisangittisagul and Chongprachawat [42], Zhang et al. [43], and Zeng et al. [44]). Therefore, the autoencoder technique has been widely used to predict time series data (Saha et al. [45], Lv et al. [46], Sagheer and Kotb [47], and Boquet et al. [48]). The proposed hybrid model has excellent potential as a novel method for forecasting the FXVIX and time series. The main contributions of this paper can be summarized as follows: First, we expand upon previous studies by forecasting the FXVIX using ANN models. Our experiments were motivated by the observation that previous studies on the FX market have mainly focused on the FX rate, volatility of returns, or historical volatility. In particular, FXVIXs represent future FX risk measures for market participants. Therefore, our findings have important implications for practitioners managing FX risk exposure. Second, we propose a hybrid ANN model based on an autoencoder and LSTM. Forecasting performance results demonstrate that the proposed hybrid model outperforms traditional LSTM models. Consequently, this study contributes to the literature on developing ANN models by introducing a novel hybrid model. Our third major contribution is the optimization of model forecasting performance through subperiod analysis. Based on the empirical results of subperiod analysis, we can conclude that a wide distribution of input data and acceptable number of outliers improve forecasting performance. The remainder of this paper is organized as follows. Section 2 presents a brief literature review on FX volatility and studies using machine learning in finance. Section 3 describes the data and methodology adopted in this study. Section 4 presents the results of empirical analysis for the full sample period and subperiod analysis. Finally, we provide concluding remarks in Section 5. 2. Literature Review There is a vast body of literature on forecasting financial time series. In this section, we divide previous research into FX rate and FX volatility research according to the main focus of previous papers. Additionally, we also discuss literature on time-series forecasting using ANNs. First, because the FX rate directly affects the income of multinational firms, many studies have focused on the forecasting FX rate and many studies have used ANN models to predict future FX rates. For example, Liu et al. [34] predicted EUR/USD, GBP/USD, and JPY/USD rates using a model based on a convolutional neural network (CNN). They demonstrated that such a model is suitable for processing 2D structural exchange rate data. Fu et al. [49] developed evolutionary support vector regression (SVR) models to forecast four Renminbi (RMB, Chinese yuan) exchange rates (CNY against USD, EUR, JPY, and GBP). They also demonstrated that the proposed model outperforms the multilayer perceptron (MLP) neural network, Elman neural network, and SVR models in terms of level forecasting accuracy measures. The authors of Sun et al. [50] introduced a novel ensemble deep learning approach based on LSTM and a bagging ensemble learning strategy to predict four major currencies (EUR/USD, GBP/USD, JPY/USD, and USD/CNY). According to their empirical results, their proposed model provided significantly improved forecasting accuracy compared to a traditional LSTM model. As discussed in the previous section, FX volatility is also important for many academics and practitioners, so many studies have focused on FX volatility forecasting. In general, GARCH-based models have been used in many studies to predict FX volatility. Additionally, some studies have predicted FX volatility by incorporating different methodologies into GARCH models to improve forecasting power. For example, the authors of Vilasuso [51] predicted various FX rate volatilities (Canadian dollar, French franc, German mark, Italian lira, Japanese yen, and British pound) using a fractionally integrated GARCH (FIGARCH) model (Baillie et al. [52]). The empirical results of their study demonstrated that the FIGARCH model is better at capturing the features of FX volatility compared to the original GARCH model. The authors of Rapach and Strauss [53] demonstrated that structural breaks in the unconditional variance of FX rate returns can improve the forecasting performance of GARCH(1,1) models for FX volatility by incorporating the daily returns of the US dollar against the currencies of Canada, Denmark, Germany, Japan, Norway, Switzerland, and the UK. Pilbeam and Langeland [54] investigated whether various GARCH-based models can effectively forecast the FX volatility of the four currency pairs of the euro, pound, Swiss franc, and yen against the US dollar. In particular, their empirical results demonstrated that GARCH models perform better in periods of low volatility compared to periods of high volatility. You and Liu [55] employed the GARCH-MIDAS approach (Engle et al. [56]) to forecast the short-run volatility of six FX rates based on monetary fundamentals. They demonstrated that the forecasting power of daily FX volatility is significantly improved by including monthly monetary fundamental volatilities. Various machine learning models have also been used to forecast time series originating from various fields, including engineering and finance. In finance, many studies have used machine learning to predict future stock prices. For example, Trafalis and Ince [57] compared SVR with backpropagation to a radial basis function network on the task of forecasting daily stock prices. Similarly, Henrique et al. [58] utilized SVR and a random walk (RW) method to predict daily stock prices in three different markets (Brazilian, American, and Chinese). Based on comparisons of the price prediction results of the SVR and RW models, they determined that SVR models may perform better than RW models in terms of predictive performance. Recently, various studies using machine learning methods and deep learning methodologies have been reported. For example, the authors of Selvin et al. [59] employed deep learning models, namely, a recurrent neural network (RNN), LSTM, and CNN to predict minute-wise stock prices. They determined that the CNN algorithm provided the best performance. Chong et al. [60] employed an autoencoder to extract features from stock data and constructed a deep neural network (DNN) to predict future stock returns. They determined that it is possible to extract features from a large set of raw data without relying on prior knowledge regarding predictors, which is one of the main advantages of DNNs. Pradeepkumar and Ravi [15] proposed a particle swarm optimization-trained quantile RNN to forecast FX volatility. Their model provides superior forecasting performance compared to the GARCH model. In [16] and [18], various ANN models were employed to predict the volatility of the S&P 500 stock index. According to the findings of these studies, ANN models are able to outperform traditional econometric methods, including GARCH and autoregressive moving average models. In particular, LSTM models seem to improve the accuracy of volatility forecasts. Additionally, Ramos-Pérez et al. [17] predicted S&P 500 index volatility using a stacked ANN model based on a set of various machine learning techniques, including gradient descent boosting, RF, and SVM. They demonstrated that volatility forecasts can be improved by stacking machine learning algorithms. Additionally, regardless of the volatility model adopted, high-volatility regimes lead to higher error rates. Several studies have proposed hybrid models based on GARCH-based models and ANN models. For example, various GARCH-based models have been combined with ANNs based on MLPs and many hybrid models have been used to enhance the ability of GARCH models to forecast the volatility of stocks, gold, and FX rate returns (Hajizadeh et al. [22], Kristjanpoller et al. [23], Kristjanpoller and Minutolo [24], and Baffour et al. [26]). Additionally, some studies have proposed hybrids of LSTM and GARCH models and have used such models to predict the volatility of financial assets (Kim and Won [25] and Hu et al. [27]). According to empirical results, hybrid models based on GARCH and ANN techniques exhibit improved forecasting performance in terms of volatility accuracy. In particular, we focus on studies using LSTM and autoencoder approaches for forecasting time series. LSTM, which was introduced by Hochreiter and Schmidhuber [21], has been widely used to forecast time series in many prediction studies. This method is mainly used to analyze time-series data because it can keep records of past data. Some studies have compared LSTM to traditional methods using neural networks or investigated such models by reconstructing both types of methods. As discussed by Siami-Namini et al. [61] and Ohanyan [62], as computing power improves, implementing deep learning models becomes more practical, and their performance exceeds that of traditional models. Additionally, Deorukhkar et al. [63] demonstrated that neural network models combined with autoregressive integrated moving average or LSTM models provide greater accuracy than either type of model individually. In [64], the method of applying preprocessed stock prices to an LSTM model using a wavelet transform was shown to be superior to traditional methods. The autoencoder presented in [35] aims to generate a representation as close to an original input as possible from reduced encoding results. This method is a transformation of the basic model using stacked layers, denoising, and sparse representation and is used for financial time series prediction. Bao et al. [65] used LSTM and stacked autoencoders to forecast stock prices and demonstrated that this type of hybrid model is more powerful than an RNN or LSTM model alone. In [66], a stacked denoising autoencoder applied to gravitational searching was effective at predicting the direction of stock index movement, which is affected by underlying assets. Additionally, Sun et al. [67] explained that a stacked denoising autoencoder formed through the selection of training sets based on a K-nearest neighbors approach can improve the accuracy compared to traditional methods. This study enhances the existing literature in two main aspects. We first propose a hybrid model that combines LSTM and an autoencoder to forecast FX volatility. There are other studies that have used hybrid models, but they have used models other than autoencoders and LSTM. Additionally, most studies have developed hybrid models based on GARCH models. However, as discussed above, LSTM and autoencoders perform well at time-series prediction, so we adopted these two types of models to forecast FX volatility. Second, as discussed in Section 1, FX volatility has great significance, but there is a significant lack of research on forecasting its changes. We contribute to the finance literature by forecasting FXVIXs using the proposed hybrid model. 3. Data Description and Methodologies 3.1. Data Description The VIX was firstly implemented on the CBOE in 1993. This index is based on the real-time prices of options in the S&P 500 index. Because it is derived from the price inputs of S&P 500 index options, this index not only represents market expectations regarding 30 day forward-looking volatility but also provides a measure of market risk and investor sentiments. Subsequently, various VIXs with different basic assets were developed. In this study, we investigated whether machine learning methods are suitable for forecasting FX volatility time-series data. Our data samples come from the CBOE. The CBOE is one of the world’s largest exchange holding companies, and it provides several derivatives related to implied VIXs. We adopted three currency-related volatility indices, namely, the BPVIX, JYVIX, and EUVIX. Similar to a VIX, FX volatility is calculated using a formula that averages the weighted prices of out-of-the-money puts and calls. We collected 2520 daily time series FXVIX data from January of 2010 to December of 2019. Based on fluctuations caused by the Brexit movement, the data were divided into subsets from 2010 to 2015, 2016, and 2017 to 2019 based on instabilities in 2016. The first period represents the period of recovery following the subprime mortgage crisis and contains the most data (1514 daily data). As shown in Figure 1, the variability of the entire section appears to be large. This observation is confirmed by Table 1. The standard deviations of BPVIX, JYVIX, and EUVIX in this section are the largest among all periods, excluding BPVIX in 2016.

The determinants of sovereign risk premiums in the UK and the European government bond market: The impact of Brexit

Article

Full-text available

May 2022
Int Econ Econ Pol

Samir Kadiric

This paper analyzes recent developments in the British and European government bond markets with reference to the United Kingdom´s decision to leave the European Union. The two main goals of the study are, firstly, to examine whether the Brexit referendum result has affected the risk premium and, secondly, whether there are any changes in risk pricing following the referendum. The paper finds a significant impact of the Brexit referendum on the risk premium in selected economies. Furthermore, the results suggest that there is a considerable change in risk pricing after the announcement of the referendum result. Credit default risk and the risk aversion play a much important role in the post-referendum period than they did prior to the vote, particularly in the United Kingdom.

Hierarchies in communities of UK stock market from the perspective of Brexit

Article

Full-text available

Jul 2020
J APPL STAT

Nowadays, increase of analyzing stock markets as complex systems lead graph theory to play a key role. For instance, detecting graph communities is an important task in the analysis of stocks, and as planar maximally filtered graphs let us to get important information for the topology of the market. In this study, we first obtain correlation network representation of UK's leading stock market network by using a novel threshold method. Then, we determine vertex clusters by using modularity and analyze clusters in planar maximally filtered graph substructures. Our analyze include a new measure called weighted Gini index for measuring the sparsity. The main goal of this paper is to study the hierarchical evolution of the market communities throughout the Brexit referendum, which is known as the stress period for the stock market. Hence, the overall sample is divided into two sub-periods of pre-referendum, and post-referendum to obtain communities and hierarchical structures. Our results indicate that financial companies are leading elements of the clusters. Moreover, the significant changes within the network topologies are observed for insurance, consumer goods, consumer services, mining, and technology sectors whereas oil and gas and health care sectors have not been affected by Brexit stress.

The Impact of Brexit on UK Firms

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