The exchange rate exposure of UK non-financial companies

Purpose – The purpose of this paper is to investigate the exchange rate exposure of UK nonfinancial companies from January 1981 to December 2001. Design/methodology/approach – The study employs different exchange rate measures and adopts an equally weighted exchange rate. The analyses are conducted at the firm level. All analyses are conducted by regressing the firm’s exchange rate exposure coefficients on its size, foreign activity variables and financial hedging proxies over the whole sample period. Findings – The findings show that a higher percentage of UK non-financial companies are exposed to exchange rate changes than those reported in previous studies. Generally, the results provide a stronger support for the suggested equally weighted rate as an economic variable, which affects firms’ stock returns. The results also show a high proportion of positive exposure coefficients among firms with significant exchange rate exposure, indicating a higher proportion of firms benefiting from an appreciation of the pound. Finally, the results also indicate evidence that firms’ foreign operations and hedging variables affect their sensitivity to exchange rate exposure. Practical implications – This study provides important implications for public policymakers who wish to understand links between policies that affect exchange rates and relative wealth effects. Originality/value – The empirical results of this study should help investors to examine how common stock returns react to exchange rate fluctuations when making financial decisions, and prove useful for financial managers when measuring exposure to foreign exchange rate changes.

Dynamic models of exchange rate dependence using option prices and historical returns

Models for the conditional joint distribution of the U.S. Dollar/Japanese Yen and Euro/Japanese Yen exchange rates, from November 2001 until June 2007, are evaluated and compared. The conditional dependency is allowed to vary across time, as a function of either historical returns or a combination of past return data and option-implied dependence estimates. Using prices of currency options that are available in the public domain, risk-neutral dependency expectations are extracted through a copula repre- sentation of the bivariate risk-neutral density. For this purpose, we employ either the one-parameter \Normal" or a two-parameter \Gumbel Mixture" specification. The latter provides forward-looking information regarding the overall degree of covariation, as well as, the level and direction of asymmetric dependence. Specifications that include option-based measures in their information set are found to outperform, in-sample and out-of-sample, models that rely solely on historical returns.

Short-horizon excess returns and exchange rate and interest rate effects

stocks. We examine the effects of foreign exchange (FX) and interest rate changes on the excess returns of U.S. stocks, for short-horizons of 1-40 days. Our new evidence shows a tendency for the volatility of both excess returns and FX rate changes to be negatively related with FX rate and interest rate effects. Both the number of firms with significant FX rate and interest rate effects and the magnitude of their exposures increase with the length of the return horizon. Our finding seems inconsistent with the view that firms hedge effectively at short-return horizons.

Forecasting exchange rates with linear and nonlinear models

In this paper, the exchange rate forecasting performance of neural network models are evaluated against the random walk, autoregressive moving average and generalised autoregressive conditional heteroskedasticity models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore, the parameters are chosen according to what the researcher considers to be the best. Such an approach, however,implies that the risk of making bad decisions is extremely high, which could explain why in many studies, neural network models do not consistently perform better than their time series counterparts. In this paper, through extensive experimentation, the level of subjectivity in building neural network models is considerably reduced and therefore giving them a better chance of Forecasting exchange rates with linear and nonlinear models 415 performing well. The results show that in general, neural network models perform better than the traditionally used time series models in forecasting exchange rates.

Volatility changes in drachma exchange rates

In January 2001 Greece joined the eurozone. The aim of this article is to examine whether an intention to join the eurozone had any impact on exchange rate volatility. We apply the Iterated Cumulative Sum of Squares (ICSS) algorithm of Inclan and Tiao (1994) to a set of Greek drachma exchange rate changes. We find evidence to suggest that the unconditional volatility of the drachma exchange rate against the dollar, British pound, yen, German mark and ECU/Euro was nonstationary, exhibiting a large number of volatility changes prior to European Monetary Union (EMU) membership. We then use a news archive service to identify the events that might have caused exchange rate volatility to shift. We find that devaluation of the drachma increased exchange rate volatility but ERM membership and a commitment to joining the eurozone led to lower volatility. Our findings therefore suggest that a strong commitment to join the eurozone may be sufficient to reduce some exchange rate volatility which has implications for countries intending to join the eurozone in the future.

Forecasting exchange rates with linear and nonlinear models

In this paper the exchange rate forecasting performance of neural network models are evaluated against random walk and a range of time series models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore the parameters are chosen according to what the researcher considers to be the best. Such an approach, however, implies that the risk of making bad decisions is extremely high which could explain why in many studies neural network models do not consistently perform better than their time series counterparts. In this paper through extensive experimentation the level of subjectivity in building neural network models is considerably reduced and therefore giving them a better chance of performing well. Our results show that in general neural network models perform better than traditionally used time series models in forecasting exchange rates.

The microstructure of the foreign exchange market:the determinants of bid-ask spreads in the foreign exchange market

The purpose of this thesis is to shed more light in the FX market microstructure by examining the determinants of bid-ask spread for three currencies pairs, the US dollar/Japanese yen, the British pound/US dollar and the Euro/US dollar in different time zones. I examine the commonality in liquidity with the elaboration of FX market microstructure variables in financial centres across the world (New York, London, Tokyo) based on the quotes of three exchange rate currency pairs over a ten-year period. I use GARCH (1,1) specifications, ICSS algorithm, and vector autoregression analysis to examine the effect of trading activity, exchange rate volatility and inventory holding costs on both quoted and relative spreads. ICSS algorithm results show that intraday spread series are much less volatile compared to the intraday exchange rate series as the number of change points obtained from ICSS algorithm is considerably lower. GARCH (1,1) estimation results of daily and intraday bid-ask spreads, show that the explanatory variables work better when I use higher frequency data (intraday results) however, their explanatory power is significantly lower compared to the results based on the daily sample. This suggests that although daily spreads and intraday spreads have some common determinants there are other factors that determine the behaviour of spreads at high frequencies. VAR results show that there are some differences in the behaviour of the variables at high frequencies compared to the results from the daily sample. A shock in the number of quote revisions has more effect on the spread when short term trading intervals are considered (intra-day) compared to its own shocks. When longer trading intervals are considered (daily) then the shocks in the spread have more effect on the future spread. In other words, trading activity is more informative about the future spread when intra-day trading is considered while past spread is more informative about the future spread when daily trading is considered

The management of foreign exchange exposure on business contracts overseas

This thesis is a piece of applied research. It is the result of a joint project between the University of Aston Interdisciplinary Higher Degrees Scheme and International Aeradio plc (IAL). It considers the structure and organisation of overseas business and the effects that exchange rate movements have on financial performance. It looks in detail at a series of overseas contracts and factors which affect the monitoring and performance of those contracts. From this initial research is developed a series of conceptual models which attempt to capture the effects of foreign exchange rate movements on contract costing, the monitoring of performance on overseas contracts and a measure of company wide exposure. These models are then considered in the context of real IAL generated data and circumstances. The work is finally considered in the context of a survey of other companies with a similar mode of undertaking overseas business with the aim of placing the work in a general context.

Bid-ask spread dynamics in foreign exchange markets

The aim of this paper is to examine the short term dynamics of foreign exchange rate spreads. Using a vector autoregressive model (VAR) we show that most of the variation in the spread comes from the long run dependencies between past and future spreads rather than being caused by changes in inventory, adverse selection, cost of carry or order processing costs. We apply the Integrated Cumulative Sum of Squares (ICSS) algorithm of Inclan and Tiao (1994) to discover how often spread volatility changes. We find that spread volatility shifts are relatively uncommon and shifts in one currency spread tend not to spillover to other currency spreads. © 2013.

An improved novelty criterion for resource allocating networks

Online model order complexity estimation remains one of the key problems in neural network research. The problem is further exacerbated in situations where the underlying system generator is non-stationary. In this paper, we introduce a novelty criterion for resource allocating networks (RANs) which is capable of being applied to both stationary and slowly varying non-stationary problems. The deficiencies of existing novelty criteria are discussed and the relative performances are demonstrated on two real-world problems : electricity load forecasting and exchange rate prediction.

Estimating conditional volatility with neural networks

It is well known that one of the obstacles to effective forecasting of exchange rates is heteroscedasticity (non-stationary conditional variance). The autoregressive conditional heteroscedastic (ARCH) model and its variants have been used to estimate a time dependent variance for many financial time series. However, such models are essentially linear in form and we can ask whether a non-linear model for variance can improve results just as non-linear models (such as neural networks) for the mean have done. In this paper we consider two neural network models for variance estimation. Mixture Density Networks (Bishop 1994, Nix and Weigend 1994) combine a Multi-Layer Perceptron (MLP) and a mixture model to estimate the conditional data density. They are trained using a maximum likelihood approach. However, it is known that maximum likelihood estimates are biased and lead to a systematic under-estimate of variance. More recently, a Bayesian approach to parameter estimation has been developed (Bishop and Qazaz 1996) that shows promise in removing the maximum likelihood bias. However, up to now, this model has not been used for time series prediction. Here we compare these algorithms with two other models to provide benchmark results: a linear model (from the ARIMA family), and a conventional neural network trained with a sum-of-squares error function (which estimates the conditional mean of the time series with a constant variance noise model). This comparison is carried out on daily exchange rate data for five currencies.

Openness and economic growth

The themes of this thesis are that international trade and foreign direct investment (FDI) are closely related and that they have varying impacts on economic growth in countries at different stages of development. The thesis consists of three empirical studies. The first one examines the causal relationship between FDI and trade in China. The empirical study is based on a panel of bilateral data for China and 19 home countries/regions over the period 1984-98. The specific feature of the study is that econometric techniques designed specially for panel data are applied to test for unit roots and causality. The results indicate a virtuous procedure of development for China. The growth of China’s imports causes growth in inward FDI from a home country/region, which in turn causes the growth of exports from China to the home country/region. The growth of exports causes the growth of imports. This virtuous procedure is the result of China’s policy of opening to the outside world. China has been encouraging export-oriented FDI and reducing trade barriers. Such policy instruments should be further encouraged in order to enhance economic growth. In the second study, an extended gravity model is constructed to identify the main causes of recent trade growth in OECD countries. The specific features include (a) the explicit introduction of R&D and FDI as two important explanatory variables into an augmented gravity equation; (b) the adoption of a panel data approach, and (c) the careful treatment of endogeneity. The main findings are that the levels and similarities of market size, domestic R&D stock and inward FDI stock are positively related to the volume of bilateral trade, while the geographical distance, exchange rate and relative factor endowments, has a negative impact. These findings lend support to new trade, FDI and economic growth theories. The third study evaluates the impact of openness on growth in different country groups. This research distinguishes itself from many existing studies in three aspects: first, both trade and FDI are included in the measurement of openness. Second, countries are divided' into three groups according to their development stages to compare the roles of FDI and trade in different groups. Third, the possible problems of endogeneity and multicollinearity of FDI and trade are carefully dealt with in a panel data setting. The main findings are that FDI and trade are both beneficial to a country's development. However, trade has positive effects on growth in all country groups but FDI has positive effects on growth only in the country groups which have had moderate development. The findings suggest FDI and trade may affect growth under different conditions.

Practical network coding for two way relay channels in LTE networks

In this paper, the implementation aspects and constraints of the simplest network coding (NC) schemes for a two-way relay channel (TWRC) composed of a user equipment (mobile terminal), an LTE relay station (RS) and an LTE base station (eNB) are considered in order to assess the usefulness of the NC in more realistic scenarios. The information exchange rate gain (IERG), the energy reduction gain (ERG) and the resource utilization gain (RUG) of the NC schemes with and without subcarrier division duplexing (SDD) are obtained by computer simulations. The usefulness of the NC schemes are evaluated for varying traffic load levels, the geographical distances between the nodes, the RS transmit powers, and the maximum numbers of retransmissions. Simulation results show that the NC schemes with and without SDD, have the throughput gains 0.5% and 25%, the ERGs 7 - 12% and 16 - 25%, and the RUGs 0.5 - 3.2%, respectively. It is found that the NC can provide performance gains also for the users at the cell edge. Furthermore, the ERGs of the NC increase with the transmit power of the relay while the ERGs of the NC remain the same even when the maximum number of retransmissions is reduced.

Long memory and multifractality:a joint test

The properties of statistical tests for hypotheses concerning the parameters of the multifractal model of asset returns (MMAR) are investigated, using Monte Carlo techniques. We show that, in the presence of multifractality, conventional tests of long memory tend to over-reject the null hypothesis of no long memory. Our test addresses this issue by jointly estimating long memory and multifractality. The estimation and test procedures are applied to exchange rate data for 12 currencies. Among the nested model specifications that are investigated, in 11 out of 12 cases, daily returns are most appropriately characterized by a variant of the MMAR that applies a multifractal time-deformation process to NIID returns. There is no evidence of long memory.

Reshoring:opportunities and limits for manufacturing in the UK - the case of the auto sector

In recent years, offshoring and outsourcing have transformed fundamentally nationally based auto sectors into global networks of design, production and distribution across the global value chains coordinated by the major automotive Original Equipment Manufacturers (OEMs). As manufacturing activities tended to be shifted to low-labour cost locations in Asia, Africa and Latin America, high-end design, R&D, product development have stayed anchored mostly to high-cost and high knowledge-intensive home economy locations (perhaps with the except of some design and styling activities which are often located in major end markets around the world. However, very recently the weaknesses of and risks inherent in such global value chains (GVCs) have been exposed, triggering attempts to rethink their nature and also raising possibilities to reshore some manufacturing activities to home countries. A combination of a more competitive exchange rate (despite the very recent appreciation of sterling), increased transport costs, rising wages in key areas of China, and a greater awareness of supply chain resilience have all contributed to a perceived change in some business fundamentals. The potential for some supply chain relocalisation also links in with the servitisation of manufacturing including the auto sector and shift to a hybrid model where manufacturing and services are increasingly intertwined. However, there are limits as to how far this can go and these raise some important questions and issues over the possible role for industrial policy.