Search results

The purpose of this paper is to estimate long-term currency exchange rate and also identify the key factors for decision makers in the currency exchange market. The study is expected to aid decision makers to take positions in the dynamic Forex market.

This study is based on quantitative and fundamental analysis of statistically oriented regression models. The trend of quarterly exchange rates is investigated using 110 variables including economic elements, interest rate and other currencies. This research is based on the same information that banks’ dealers use for the analysis. Ordinary least squares linear regression also known as “least squared errors regression” was used to estimate the value of the dependent variable.

The study concludes that “only Australian economic data” or “only the US economic data” cannot fully reflect the trend of AUD/USD. EUR influences AUD relatively larger than the other main market currencies. Six-month Australian interest rate itself affects AUD/USD trend much more than the six-month interest difference between AUD and USD.

The results indicate that the economic autoregressive moving average model can be used to predict future exchange rate using primary factors identified and not from the generic market or economic view. This helps adjust to the general, common (and possibly wrong) views when making a buy or sell decision.

This is one of the first studies in the context using the information of bank dealers for AUD/USD. This study is highly relevant in the current context, given the significant growth in Forex trade.

The purpose of this paper is to investigate whether the factors that summarise the information in the yield curves of Australia and the USA can predict changes in the Australian–USA exchange rate (i.e. the AUD/USD rate) and Australian dollar excess returns.

The paper extracts the three Nelson–Siegel factors (level, slope and curvature) from the relative yield curve of Australia with the USA to predict changes in the bilateral exchange rate and excess returns on the Australian dollar. The full sample regressions allow for a shift in the coefficient on the relative curvature factor which can account for the impact of the Fed’s changed monetary policy to one of quantitative easing.

The paper finds that the relative curvature factor strongly predicts changes in the AUD/USD exchange rate and Australian dollar excess returns out to 12 months ahead in the sample that precedes the Fed’s policy of quantitative easing. The relative curvature factor retains its predictive power in the full sample regressions but anticipates smaller exchange rate changes and excess currency returns in in-sample predictions made from August 2007.

The yield curves of Australia and the USA reliably reflect investor’s expectations about prospective monetary policies in each economy.

The paper investigates the predictive content of the relative Nelson–Siegel factors for changes in the AUD/USD exchange rate and for Australian dollar excess returns over various forecast horizons for a period that covers the Fed’s policy of quantitative easing.

This paper aims to examine the impact of crude oil prices on Australian industry stock returns. With rising energy prices, it is important to consider oil as a pricing factor in asset pricing models.

Multifactor static and dynamic models consider crude oil and other macroeconomic factors as pricing factors in industry excess returns from January 1980 to August 2006. The macroeconomic factors comprise the market portfolio, oil prices, exchange rates and the term premium. The industries consist of banking, diversified financials, energy, insurance, media, property trusts, materials, retailing and transportation.

Oil prices are an important determinant of returns in the banking, energy, materials, retailing and transportation industries. The findings also suggest oil price movements are persistent. Nonetheless, the proportion of variation in excess returns explained by the contemporaneous and lagged oil prices appears to have declined during the sample period.

Macroeconomic factors are important for multifactor asset pricing at the industry level. Apart from oil prices, the market portfolio is a significant pricing factor in all industry excess returns. Exchange rates are also an influential factor for excess returns in the banking and diversified financials industries, and the term premium as a proxy for future real activity is a priced factor in the energy, insurance and retailing industries.

While past studies have provided some evidence that oil prices constitute a source of systematic asset price risk and that exposure varies across industries, no recent work is known in the Australian context.

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

Property market models have the overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption rate). These can be based on independent drivers of core property and economic activities. Accurate predictions can only be conducted when ample quantitative data are available with fewer uncertainties. However, a broad-fronted social, technical and ecological evolution can throw up sudden, unexpected shocks that result in the econometric outputs sceptical to unknown risk factors. Therefore, the purpose of this paper is to evaluate Australian office market forecast accuracy and to determine whether the forecasts capture extreme downside risk events.

This study follows a quantitative research approach, using secondary data analysis to test the accuracy of economists’ forecasts. The forecast accuracy evaluation encompasses the measurement of economic and property forecasts under the following phases: testing for the forecast accuracy; analysing outliers of forecast errors; and testing of causal relationships. Forecast accuracy measurement incorporates scale independent metrics that include Theil’s U values (U1 and U2) and mean absolute scaled error. Inter-quartile range rule is used for the outlier analysis. To find the causal relationships among variables, the time series regression methodology is utilised, including multiple regression analysis and Granger causality developed under the vector auto regression (VAR).

The credibility of economic and property forecasts was questionable around the period of the Global Financial Crisis (GFC); a significant man-made Black Swan event. The forecast accuracy measurement highlighted rental movement and net absorption forecast errors as the critical inaccurate predictions. These key property variables are explained by historic information and independent economic variables. However, these do not explain the changes when error time series of the variables were concerned. According to VAR estimates, all property variables have a significant causality derived from the lagged values of Australian S&P/ASX 200 (ASX) forecast errors. Therefore, lagged ASX forecast errors could be used as a warning signal to adjust property forecasts.

Secondary data were obtained from the premier Australian property markets: Canberra, Sydney, Brisbane, Adelaide, Melbourne and Perth. A limited ten-year timeframe (2001-2011) was used in the ex-post analysis for the comparison of economic and property variables. Forecasts ceased from 2011, due to the discontinuity of the Australian Financial Review quarterly survey of economists; the main source of economic forecast data.

The research strongly recommended naïve forecasts for the property variables, as an input determinant in each office market forecast equation. Further, lagged forecast errors in the ASX could be used as a warning signal for the successive property forecast errors. Hence, data adjustments can be made to ensure the accuracy of the Australian office market forecasts.

The paper highlights the critical inaccuracy of the Australian office market forecasts around the GFC. In an environment of increasing incidence of unknown events, these types of risk events should not be dismissed as statistical outliers in real estate modelling. As a proactive strategy to improve office market forecasts, lagged ASX forecast errors could be used as a warning signal. This causality was mirrored in rental movements and total vacancy forecast errors. The close interdependency between rents and vacancy rates in the forecasting process and the volatility in rental cash flows reflects on direct property investment and subsequently on the ASX, is therefore justified.

Financial theory predicts that a change in an exchange rate should affect the value of a firm or an industry. To a large extent, past research has not supported this theory, which is surprising especially after considering the substantial exchange rate fluctuations over the three decades. This study seeks to extend previous research on the foreign exchange rate exposure of UK nonfinancial companies at the industry level over the period 1981‐2001.

In this study, exchange rate exposure is defined as the change in the value of the firm or industry due to the changes in exchange rates. This study differs from previous studies in that it considers the impact of the changes (actual and unexpected) in exchange rates on firms’ or industries’ stock returns. The approach employs OLS model to estimate foreign exchange rate exposure of 364 UK nonfinancial companies over the period 1981‐2001. All data are collected from the Datastream database.

The findings indicate that a higher percentage of UK industries are exposed to contemporaneous exchange rate changes than those reported in previous studies. There is also evidence of significant lagged exchange rate exposure. This lagged exchange rate exposure is consistent with findings in previous studies that may exist some market inefficiencies in incorporating exchange rate changes into the returns of firms and industries.

Future research in the area should consider additional factors that might affect a firm's and an industry's exposure to exchange rate changes.

The findings of the study have interesting implications for public policy makers who wish to understand links between policies that affect exchange rates and relative wealth affects. These findings should also be of particular importance to investors who under or overweight large multinational corporations.

The study extends previous research on foreign exchange rate exposure of UK companies.

This study aims to compare investors of major conventional currencies and Bitcoin (BTC) investors by using the value at risk (VaR) method common risk measure.

The paper used a risk analysis named as VaR. The analysis has various computations that Historical Simulation and Monte Carlo Simulation methods were used for this paper.

Findings of the analysis are assessed in two different aspects of singular currency risk and portfolios built. First, BTC is found to be significantly risky with respect to the major currencies; and it is six times riskier than the singular most risky currency. Second, in terms of inclusion of BTC into a portfolio, which equally weights all currencies, it elevates overall portfolio risk by 98 per cent.

In spite of the remarkable risk level, it could be considered that investors are desirous of making an investment on BTC could mitigate their overall exposed risk relatively by building a portfolio.

The paper questions the risk level of Bitcoin, which is a digital currency. BTC, a matter of debate in the contemporary period, is seen as a digital currency free from control or supervision of a regulatory board. With the comparison of major currencies and BTC shows that how could be risky of a financial instrument without regulations. However, there is some advice for investors who would like to invest digital currencies despite the risk level in this study.

The purpose of this paper is to consider the implicit effect of the underlying foreign currency exposure on the performance characteristics of country exchange traded funds.

To arrive at an overall estimation of the exchange-traded fund (ETF)’s tracking error, the mean of the three measures of tracking error was calculated for both the hedged (r_LC) and unhedged (r_NAV) return series. Since tracking error does not capture all the risk inherent in a country index fund, the study extends the analysis using the Sortino and Modified Sharpe ratios.

The decision to hedge currency risk should not be taken on the sole basis of historical volatilities. The investor must also factor in transactions costs, the possible roll of futures contracts and prevailing interest rate differentials. If the rate on the foreign currency is greater than the dollar (euro) rate, the investor will pay for the hedge. If the rate on the foreign currency is less than the dollar (euro) rate, the investor will gain on the trade. Given that hedging entails additional costs, in cases where the neutralization of currency volatility only reduces risk modestly, it would be advisable to leave the exchange rate risk unhedged. We propose two metrics for ETF investors deciding whether to hedge a country ETF’s underlying currency risk.

The results highlight a key finding: while the majority of country funds accurately track the performance of the underlying foreign index when measured in the local currency, returns in the fund currency can be much more volatile. In breaking down the sources of country fund volatility, the paper demonstrates the impact of the underlying currency movements on overall fund risk. In cases where the currency impact has a significant impact on fund tracking errors, an index-oriented investor benefits from neutralizing the exchange rate effect. Additionally, as the Sortino and Modified Sharpe measures suggest that the underlying currency exposure offers in most cases a better risk-adjusted return for country exchange-traded funds (ETFs) in the listing currency, we also calculate the risk minimizing foreign currency exposure for each fund and propose a decision rule based on the net currency variance to decide whether to hedge the ETF’s currency risk. The optimal hedge ratio indicates that US-based investors should only partially hedge the underlying currency risk while European-based investors are better off fully hedging currency risk.

In this paper the authors examine evidence on exchange rate predictability through commodity prices for a set of countries categorized as commodity import- and export-dependent developed and emerging countries.

The authors perform in-sample and out-of-sample forecasting analysis. The commodity prices are modeled to predict the exchange rate and to analyze whether this commodity price model can perform better than the random walk model (RWM) or not. These two models are compared and evaluated in terms of exchange rate forecasting abilities based on mean squared forecast error and Theil inequality coefficient.

The authors find that primary commodity prices better predict exchange rates in almost two-thirds of export-dependent developed countries. In contrast, the RWM shows superior performance in the majority of export-dependent emerging, import-dependent emerging and developed countries.

Previous studies examined the exchange rate of commodity export-dependent developed countries mainly. This study examines both developed and emerging countries and finds for which one the changes in prices of export commodities (in case of commodity export-dependent country) or prices of major importing commodities (in case of import-dependent countries) can significantly predict the exchange rate.