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In this paper, we develop a daily-frequency measure of economic and policy uncertainty for Chile, employing information obtained from Twitter accounts using web scraping techniques and following closely the methodology proposed by Baker et al. (2016). Our proposed measure, called DEPUC, aims to capture the level of general disagreement -a proxy for economic and policy uncertainty-in topics such as the economy, economic policies, uncertainty about particular events, and Chile's conjuncture situation. The index, available from 2012 onwards, shows significant hikes that coincide with several local and international episodes that provoked extraordinary levels of uncertainty in Chile, especially after the events around the civil protests in mid-October 2019 and the start of the COVID-19 pandemic in mid-March 2020. An empirical exercise reveals that the proposed measure is a significant determinant of the nominal exchange rate dynamics, especially when this variable's magnitude is high and a week after the shock occurs. On the contrary, when the exchange rate is low, the impact of uncertainty on this variable is quantitatively smaller for any forecasting horizon. These features, and others discussed in the paper, highlight the usefulness of the proposed metric as an additional indicator that policymakers can incorporate into their monitoring toolkit. [ABSTRACT FROM AUTHOR]

The article discusses the spillover effects of the Federal Open Market Committee's (FOMC) actions on foreign economies. It presents several observations on the market-specific content of international spillovers, with a focus on the importance of country-specific mentions in FOMC communication. It further highlights the effects that changes to U.S. monetary policy has on trade balances, global financial conditions, and central bank communication.

African nations have, with the passage of time, over‐relied on remittance inflow to augment domestic savings for growth and development. Although remittance—both as a viable investment financing option and as a growth complementary factor—helps drive growth and development, concerns of fading altruism exist. As regards altruistic giving, decreasing external financing options might overwhelm growth and development. We argue that the altruistic connection, which has been the bedrock of sending remittances to African countries, could eventually fade into oblivion. We assigned numerical weights to establish the influence of remittances on the future of African economies. We took cognizance of endogeneity of regressors and accounted for cross‐sectional dependence. We found a positive relationship between remittances and financial development besides the influence of exchange rate, technological change, inflation and population on the latter. African governments, donors, investment partners and the society at large should be concerned about fading altruistic connections from old and young African migrants. Altruism could be a springboard to the diversification of external capital sourcing and creation of a migrant policy that puts Africa at the forefront of development ahead of seeking increased worker remittances from abroad. [ABSTRACT FROM AUTHOR]

• We study the efficiency of inflation and exchange rate forecast revisions in Colombia. • We use the individual responses of the Central Bank expectations survey of external economic analysts. • We relax the individual homogeneity assumption. • We use a comprehensive list of variables which are assumed to be in the information set of the forecasters. • We find that the forecast of more than half of the analysts are weakly efficient, and that during periods of market instability analysts use a wide array of variables. We use the Banco de la República expectations survey of external economic analysts to study whether fixed-event individual forecasts of inflation and exchange rate are updated efficiently when new information becomes available. To this end, we test for weak-form and strong-form efficiency. The novel aspects of this paper are that we relax the individual homogeneity assumption, and consider a forecasters' information set that contains a large number of empirically relevant variables. We address model selection using two of the most popular methods available in the penalised regression literature, and a new form of multiple testing. Our results show that more than half of the analysts' revisions are independent of one another (weakly efficient). Also, conditional on passing weak efficiency, we find that analysts use past values of inflation and exchange rate changes to revise their forecasts and a broader array of variables during periods of market instability. [ABSTRACT FROM AUTHOR]

Abstract The volatility of exchange rate is very important to a country's trading. Accurately forecasting exchange rate time series appears to be a challenging task for the scientific community on account of its nonstationary and nonlinear structural nature. In order to improve the performance of exchange rate forecasting, this study develops two evolutionary support vector regression models to forecast four typical RMB exchange rates (CNY against USD, EUR, JPY and GBP), and employs four evaluation criteria to assess the performance of out-of-sample exchange rate forecasting. In this study, the evolutionary algorithm optimizes the SVR parameters by balancing search between the global and local optima. However, the inputs of models are selected though phase space reconstruction method from historical data of exchange rate series. The empirical results demonstrate that our proposed evolutionary support vector regression outperforms all other benchmark models in terms of level forecasting accuracy, directional forecasting accuracy and statistical accuracy. As it turns out, our proposed evolutionary support vector regression is a promising approach for RMB exchange rate forecasting. Highlights • Two evolutionary SVR models are developed to forecast foreign exchange rates. • Out-of-sample predictive performances are compared under four evaluation criteria. • Comparison analysis between our models and other benchmark models are presented. • Statistical test are used to evaluate the forecasting efficiency of the models. [ABSTRACT FROM AUTHOR]

In this paper, we detect how to improve the performance of exchange rate momentum strategy using volatility information. We consider a trading scheme which allocates wealth between momentum portfolio and Treasury bill and the weight of each asset is inversely dependent of the volatility forecasts. Our results indicate that this strategy significantly improves upon the momentum strategy, especially during the period of financial crisis. The superiority of our strategy is robust for the look-back periods from 1 to 6 months. The performance of our strategy can be further improved after imposing a constraint on the optimal weight of each asset. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new exchange rate forecasting model using Variational Mode Decomposition (VMD) with parameter optimization by the combined Mean Square Error (MSE) and Error Entropy (EE) criterion. Exchange rate is decomposed into a series of underlying data components in the transformed multiscale domain using the VMD model. A new MSE–EE criterion is proposed to determine the scale for transient factors among different extracted data components. The proposed model extracts the transient factor more accurately and produces more accurate forecasts. Empirical studies using extensive exchange rates confirmed that the multiscale data structure can be identified more effectively in the decomposed multiscale domain using the proposed methodology. The proposed model demonstrates the superior performance compared to the benchmark models. [ABSTRACT FROM AUTHOR]

This paper presents the heavy ordered weighted moving average (HOWMA) operator. It is an aggregation operator that uses the main characteristics of two well-known techniques: the heavy ordered weighted averaging (OWA) and the moving averages. Therefore, this operator provides a parameterized family of aggregation operators from the minimum to the total operator and includes the OWA operator as a special case. It uses a heavy weighting vector in the moving average formulation and it represents the information available and the knowledge of the decision maker about the future scenarios of the phenomenon, according to his attitudinal character. Some of the main properties of this operator are studied, including a wide range of families of HOWMA operators such as the heavy moving average and heavy weighted moving average operators. The HOWMA operator is also extended using generalized and quasi-arithmetic means. An example concerning the foreign exchange rate between US dollars and Mexican pesos is also presented. [ABSTRACT FROM AUTHOR]

Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study. [ABSTRACT FROM AUTHOR]

In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been assessed. The methodology used in this study considered the short-term historical stock prices as well as the day of week as inputs. Daily stock exchange rates of NASDAQ from January 28, 2015 to 18 June, 2015 are used to develop a robust model. First 70 days (January 28 to March 7) are selected as training dataset and the last 29 days are used for testing the model prediction ability. Networks for NASDAQ index prediction for two type of input dataset (four prior days and nine prior days) were developed and validated. [ABSTRACT FROM AUTHOR]

This paper introduces the multiple linear regression heavy ordered weighted average (MLR-HOWA) operator. On the MLR-HOWA operator, the beta values are obtained with the use of the HOWA means. In that sense, it provides a new range of possibilities by under or overestimating the result based on the decision maker's expectations and knowledge. Therefore, the MLR-HOWA provides a forecasting tool that can analyze multiple scenarios from minimum to maximum. The main properties and two extensions using induced and generalized variables are also presented. An application in exchange rate forecasting based on inflation and interest rate as independent variables for five Latin American countries is submitted. Among the main results, it is possible to identify that the forecasting error is reduced when different combinations of MLR with OWA operators are done. [ABSTRACT FROM AUTHOR]

El artículo discurre sobre el control cambiario desde principios del siglo 21 en Venezuela. Los autores comentan sobre la asignación de divisas y el ingreso de divisas según el Banco Central de Venezuela. También se considera la previsión económica para 2016 en Venezuela, con un enfoque en la industria de petróleo.

Forecasting exchange rates is an important financial problem. In this paper, an improved deep belief network (DBN) is proposed for forecasting exchange rates. By using continuous restricted Boltzmann machines (CRBMs) to construct a DBN, we update the classical DBN to model continuous data. The structure of DBN is optimally determined through experiments for application in exchange rates forecasting. Also, conjugate gradient method is applied to accelerate the learning for DBN. In the experiments, three exchange rate series are tested and six evaluation criteria are adopted to evaluate the performance of the proposed method. Comparison with typical forecasting methods such as feed forward neural network (FFNN) shows that the proposed method is applicable to the prediction of foreign exchange rate and works better than traditional methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses market risk prediction for high frequency foreign exchange rates under nonlinear risk scaling behaviour. We use a modified version of the multifractal model of asset returns (MMAR) where trading time is represented by the series of volume ticks. Our dataset consists of 138,418 5-min round-the-clock observations of EUR/USD spot quotes and trading ticks during the period January 5, 2006 to December 31, 2007. Considering fat-tails, long-range dependence as well as scale inconsistency with the MMAR, we derive out-of-sample value-at-risk (VaR) forecasts and compare our approach to historical simulation as well as a benchmark GARCH(1,1) location-scale VaR model. Our findings underline that the multifractal properties in EUR/USD returns in fact have notable risk management implications. The MMAR approach is a parsimonious model which produces admissible VaR forecasts at the 12-h forecast horizon. For the daily horizon, the MMAR outperforms both alternatives based on conditional as well as unconditional coverage statistics. [Copyright &y& Elsevier]

We study trade effects of the euro adoption in Central and Eastern Europe (CEE). We employ a gravity model that controls for an extended set of trade theory and policy variables. The gravity model is estimated using the panel data approach on a sample of Organisation for Economic Co-operation and Development (OECD) and CEE countries trading with the rest of the world during the period 1993–2008. We find that the adoption of the euro results in trade expansion for the CEE countries. This result is driven by elimination of exchange rate volatility and accession to the European Economic and Monetary Union (EMU). However, our forecasts show that this effect is short-lived. [ABSTRACT FROM AUTHOR]

Exchange rate prediction is an important yet challenging problem in financial time series analysis. Although the historical exchange rates can provide valuable information, other factors will also affect the prediction significantly. These factors could be numerical or non-numerical ones, which are related to politics, economics, military, or even market psychology. Previous automatic exchange rate prediction merely considers numerical data (or simply the historical rates) for predicting the next day value. In this paper, we show how to utilize and combine many related factors, both numerical and non- numerical factors, for exchange rate prediction. With an example on forecasting exchange rate between US dollar and Japanese yen, we investigate how to exploit the information from non-numerical factors. We then engage a novel integrated approach which successfully combines information obtained from both numerical and non-numerical factors. We show how to quantify the non-numerical fundamental information, provide details steps on how to construct single predictors on different kinds of information separately, and finally describe how to integrate these separate predictors. Experimental results showed that our method can achieve the Directional Symmetry (DS) accuracy of 86.96%, which is much higher than only exploiting numerical information. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the monetary policy reaction functions of the over the periods 1987:01–2001:12 and 2002:01–2009:05. We specifically attempt to shed light on question to what extent exchange rate developments still continue to influence monetary policy, even under inflation targeting. This study seeks to investigate how the monetary policy responded to the exchange rate shocks before and after adoption of inflation targeting regime. How large the effect of exchange rate shocks is accounted for in forecast error variances decompositions for monetary policy as compared to other shocks? Using the VAR model, this study shows that there has been strong pass-through during whole period. Moreover, in the postcrisis period, exchange rate has been the main reaction variable for the CBRT. [Copyright &y& Elsevier]

Abstract: A financial crisis is typically a rare kind of an event, but it hurts sustainable economic development when it occurs. This study proposes a multiscale neural network learning paradigm to predict financial crisis events for early-warning purposes. In the proposed multiscale neural network learning paradigm, currency exchange rate, a typical financial indicator that usually reflects economic fluctuations, is first chosen. Then a Hilbert-EMD algorithm is applied to the currency exchange rate series. Using the Hilbert-EMD procedure, some intrinsic mode components (IMCs) of the currency exchange rate series, with different scales, can be obtained. Subsequently, the internal correlation structures of different IMCs are explored by a neural network model. Using the neural network weights, some important IMCs are selected as the final neural network inputs and some unimportant IMCs that are of little use in mapping from inputs to output are discarded. Using these selected IMCs, a neural network learning paradigm is used to predict future financial crisis events, based upon some historical data. For illustration purpose, the proposed multiscale neural network learning paradigm is applied to exchange rate data of two Asian countries to evaluate the state of financial crisis. Experimental results reveal that the proposed multiscale neural network learning paradigm can significantly improve the generalization performance relative to conventional neural networks. [Copyright &y& Elsevier]

Abstract: Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modelling approaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organising modelling methods for the daily prediction of the exchange rate market. We also propose a combined approach where the parametric and nonparametric self-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchange rates: the American Dollar and the Deutche Mark against the British Pound. [Copyright &y& Elsevier]

Abstract: Nowadays a great deal of effort has been made in order to gain advantages in foreign exchange (FX) rates predictions. However, most existing techniques seldom excel the simple random walk model in practical applications. This paper describes a self-organising network formed on the basis of a mixture of adaptive autoregressive models. The proposed network, termed self-organising mixture autoregressive (SOMAR) model, can be used to describe and model nonstationary, nonlinear time series by means of a number of underlying local regressive models. An autocorrelation coefficient-based measure is proposed as the similarity measure for assigning input samples to the underlying local models. Experiments on both benchmark time series and several FX rates have been conducted. The results show that the proposed method consistently outperforms other local time series modelling techniques on a range of performance measures including the mean-square-error, correct trend predication percentage, accumulated profit and model variance. [Copyright &y& Elsevier]

Abstract: Fuzzy time series model has been successfully employed in predicting stock prices and foreign exchange rates. In this paper, we propose a new fuzzy time series model termed as distance-based fuzzy time series (DBFTS) to predict the exchange rate. Unlike the existing fuzzy time series models which require exact match of the fuzzy logic relationships (FLRs), the distance-based fuzzy time series model uses the distance between two FLRs in selecting prediction rules. To predict the exchange rate, a two factors distance-based fuzzy time series model is constructed. The first factor of the model is the exchange rate itself and the second factor comprises many candidate variables affecting the fluctuation of exchange rates. Using the exchange rate data released by the Central Bank of Taiwan, we conducted several experiments on exchange rate forecasting. The experiment results showed that the distance-based fuzzy time series outperformed the random walk model and the artificial neural network model in terms of mean square error. [Copyright &y& Elsevier]

Abstract: This paper examines heterogeneity in exchange rate expectations. Whereas agents’ heterogeneity is key in modern exchange rate models, evidence on determinants of heterogeneity is weak thus far. Our sample, covering expectations from about 300 forecasters over 15 years, shows remarkable time variation in dispersion. Determinants of dispersion are consistent with the chartist–fundamentalist approach: misalignments of the exchange rate and exchange rate changes explain heterogeneity. The risk premium influences heterogeneity as well, but possible impacts from macroeconomic variables and exchange rate''s volatility are dominated by the other determinants. [Copyright &y& Elsevier]

The main goal of this paper is to evaluate the impact of the exchange rate volatility in price prediction of derivative securities in the Brazilian capital markets using an artificial neural network technique, given the Black & Scholes Model limitations. For this purpose a multiplayer, feedforward neural network, trained by the backpropagation algorithm model, to perform the prediction of the Telemar option prices was developed. The model results show that price estimates are close to the real values, mainly when appended to the exchange rate, confirming that the performance of neural network is superior to other results. The inclusion of the exchange rate in neural networks technique results in a better price forecasting for the options, because the volatility in the price of the underlying asset is caused by temporary arbitrage of quotes among the national and foreign stock markets where the company is listed. [ABSTRACT FROM AUTHOR]

A monetary policy framework based on targeting a relative inflation-forecast is proposed for the economies converging to the euro. Such strategy aims at containing the differentials between the domestic and the implicit monetary union inflation-forecasts. Hence, these differentials become a basis for setting an operational policy target. The proposed framework can be viewed as an extension of flexible inflation targeting that prioritizes low and stable inflation over the exchange rate stability. It is believed to be consistent with the Maastricht convergence criteria and can be implemented in concurrence with the exchange rate stability benchmark for the ERM2. Several empirical tests are conducted to determine feasibility of adopting an instrument rule for the proposed policy framework in the three largest inflation-targeting candidates to the euro: the Czech Republic, Hungary and Poland. The stability tests as well as the volatility dynamics tests suggest that adoption of the relative inflation-forecast targeting framework is possible in these countries. [Copyright &y& Elsevier]

We consider a non-stationary regression-type model for stock returns in which the innovations are described by four-parameter distributions and the parameters are assumed to be smooth, deterministic functions of time. Also incorporating normal distributions for modelling the innovations, our model is capable of adapting to light-tailed innovations as well as to heavy-tailed innovations. Thus, it turns out to be a very flexible approach. For both the fitting of the model and for forecasting the distributions of future returns, we use local likelihood methods to estimate the parameters. We apply our model to the S&P 500 return series, observed over a period of 12 years. We show that it fits these data quite well and that it yields reasonable one-day-ahead forecasts. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the relationship between nominal exchange rate volatility and dollarization in Nigeria by applying Granger causality test for the period 1986 (1)–2003 (4). Previous theoretical and empirical studies on this issue provided conflicting results. The empirical results of Granger causality test support a bi-directional relationship. However, causality from dollarization to exchange rate volatility appears stronger and dominates. This suggests that policies that aim to reduce exchange rate volatility in Nigeria must include measures that specifically address the issue of dollarization. An important factor in this case is the supply of sufficient domestic currency assets that would permit portfolio diversification and capable of dousing negative expectations about future inflation in the country. [Copyright &y& Elsevier]

Abstract: New member states will join the EMU in the coming years. Setting the central parity at which they will join has been and will be a challenging task, as there is a considerable amount of uncertainty, both from a theoretical and an empirical perspective, surrounding the determination of the optimal exchange rate. Given the probable difficulty in evaluating the equilibrium rate it is thus advisable to focus on the effects of a misalignment of the entry rate for the economy, as it has implications for countries’ real and nominal convergence. An overvalued exchange rate would have an adverse impact on a country''s competitiveness and its growth, while an undervalued currency would contribute to an overheating of the economy and an excessive inflation. The objective of this paper is to better understand the role of the entry rates for short run inflation and GDP developments and their implications for the inflation criterion and the real convergence process. Having estimated equilibrium exchange rates for eight out of ten countries that entered the EU in May 2004: Czech Republic, Estonia, Hungary, Lithuania, Latvia, Poland, Slovenia and Slovakia we conduct simulations showing what their adjustments to equilibrium would be if their entry rates deviated from the optimal ones. [Copyright &y& Elsevier]

In recent years, many economists have argued that governments are discarding pegged exchange rates in favor of alternative exchange rate regimes such as monetary unions and currency boards on the one hand, or floating exchange rates on the other. Capital mobility, such economists argue, has made pegged exchange rates costly to maintain for long periods, and thus the pegging option is being “hollowed-out.” Few, however, have tried to present evidence that capital mobility has a direct effect on exchange rate regime choices. I present two sets of tests using different measures of capital mobility that provide qualified confirmation that developing countries peg less as capital mobility rises. These tests indicate that direct measures of capital mobility have some correlation with de facto exchange rate regimes but not with de jure exchange rate regimes. Capital flows, a consequence of capital mobility, may have a direct effect on the choices of both de jure and de facto regimes. Governments do not make changes in their declared exchange rate policies in rational anticipation of the growing costs of pegging associated with increasing capital mobility. Rather, governments normally adapt their declared exchange rate policies after capital flows have increased and actual exchange rates have become more difficult to manage. The tests also indicate that hollowing-out has not only been the result of systemic factors such as increasing capital mobility and capital flows, but also due to domestic factors such as growing public sector indebtedness and the spread of democracy. [ABSTRACT FROM AUTHOR]

During the 1997 economic crisis in Asia, we observed that several East Asian economies encountered significant external and internal shocks that led to a widespread economic recession. The severe exchange rate depreciation may have harmed the supply side of these economies such that production was contracted. Many argue that the initial tightened domestic policies aimed at countering these shocks and restoring economic growth may have further exacerbated the initial crisis by contracting the domestic sector. This paper analyzes the impact of anticipated and unanticipated changes in the real exchange rate, fiscal spending, the money supply, and energy prices on output and price in Korea, Indonesia, Malaysia, Philippines, and Thailand. The results indicate that anticipated real depreciation during the crisis may have adversely affected the supply side, shrinking output growth while accelerating price inflation. In contrast, the combined effects of supply and demand channels render the outcome of unanticipated currency depreciation inconclusive on output growth and price inflation. Indonesia and Malaysia have experienced a significant reduction in average real output growth beyond the crisis period with a significant increase in domestic price inflation in Indonesia only. In most cases, we credit accommodating policies for countering external shocks. [ABSTRACT FROM AUTHOR]

This article reviews the 'pro-poor' macroeconomic policy alternative to the Washington consensus. The pro-poor approach draws heavily on heterodox economic theory, and offers a compelling view of an alternative economic strategy oriented primarily to the satisfaction of the basic needs of the majority of the population, the equitable distribution of income, wealth and power, and the preservation of macroeconomic stability. These aims point to a specific set of fiscal, monetary, trade and exchange rate policies. The paper argues that such policies should be supported by social programmes designed to achieve the desired pro-poor outcomes as rapidly as possible. [ABSTRACT FROM AUTHOR]

This article develops an international comparison, characterization and econometric evaluation about the development of Chilean FX derivatives market using a unique database of exchange rate operations performed by residents against domestic and international counterparts. The level of activity of the Chilean spot exchange rate market as well as its transaction costs are similar to those in more advanced economies, and a level of exchange rate derivative market above other emerging economies, yet at a significant distance from developed economies. Also, the article reports a growing number of foreign counterparts and a maturity of derivatives contracts slightly above the world average. From the cross country level regressions, it is worth mentioning the positive role of flexible exchange rate regimes as well as the negative effect of international reserve accumulation on the development of the exchange rate market. It is shown that the Chilean FX activity converges to the levels suggested by the estimation, according to the fundamentals of the economy. Nonetheless, it states at a significant distance from countries like Australia, New Zealand, and Canada. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyzes the likely welfare impact of devaluation in Seychelles. The presented analysis uses the analytical framework that allows for estimating the potential welfare impacts of devaluation ex ante, i.e. before the policy change has taken place. To analyze the impact of devaluation on the incidence of poverty and inequality, the estimated devaluation-induced changes in prices and wages are applied to the unit record household survey data. We also investigate the effectiveness of various mitigation policies. Our analysis indicates that the poverty headcount would likely increase immediately after the devaluation. In terms of per capita expenditure, the relative loss will be larger for the richer population, thus causing a slight decrease in inequality. To mitigate the transitory adverse effects on the most vulnerable people, the policy of strengthening the social safety nets through increased transfers to poor households is found to be most effective (per money spent). However, the ultimate poverty reduction impact is estimated to come through the resumption of economic growth following devaluation. [Copyright &y& Elsevier]

The article focuses on the effect of exchange rates on emission scenarios. These scenarios are aimed to predict the trends of future greenhouse gas emissions and their driving forces. Six different integrated assessment models have been used for the development of emission scenarios. The economic growth is reported in U.S. dollars using market-exchange rates (MER) in most of the scenarios. MER-based economic projections has been criticized by various researchers. They argue that the actual welfare in different regions can be compared properly only by using purchasing power parities.

Since the development of the EGARCH (Exponential General Autoregressive conditional Heteroskedastic process) by Nelson (1991), several authors have reported presence of asymmetry on stock return volatility. This phenomenon has been widely explained as the leverage effect. However, no strong evidence has been described in developed market exchange rate series. Thus, the objective. of this paper is to extend the analysis, testing exchange rate series on emerging markets for asymmetry in volatility. In emerging markets, elements as country risk, economy size and investors' confidence can cause that negative and positive returns affect the volatility level differently. Finally, after applying several methods to evaluate and compare the selected model forecastings, weak in-sample evidence of asymmetry that completely disappeared out-of-sample is reported. [ABSTRACT FROM AUTHOR]

Abstract: Despite the large number of studies done on the recent East Asian crisis, hardly any of them has however simultaneously evaluated the roots of the inflationary pressures and unearthed the sources of sharp variations between the inflation rates of the various crisis-affected economies. To help fill in this gap, our paper examines and contrasts the sources of inflation in Thailand, Indonesia and South Korea during the period of 1985–2001. A number of potential sources of inflation will be considered in the study. But this study pays a particular attention to the possible roles of the monetary aggregates and the exchange rate uncertainties in encapsulating the rise in the inflationary pressures and the variations between the inflation rates experienced by these economies during the 1997 crisis. [Copyright &y& Elsevier]

Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naïve random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks. [ABSTRACT FROM AUTHOR]

In the Chilean exchange rate market, composed by sophisticated institutional investors and under different models of intervention of Central Bank, it is possible to obtain abnormal returns with the use of technical analysis and autoregresive models. The findings are similar to those observed in other international markets, but they were not the expected by the authors, since the low number of participants in this market, the high volatility due to deep international financial crises and the significant changes in this market introduced by the Central Bank. In effect, in the last third of the sample, the volatility of the exchange rate increased significantly and the abnormal returns obtained under the technical analysis and autoregressive models were equivalent to 60% approximately of the total sample. This period corresponds to the decision of the Chilean Central Bank to intervene in the exchange rate market as a result of the economic crisis in Argentina. Also, we did not observed statistical difference between the exchange rate and the forecasted by the autoregressive models, result that was validated by parametric and nonparametric test. [ABSTRACT FROM AUTHOR]

Compares the performance of a genetic program in forecasting daily exchange rate volatility for the dollar-Deutsche mark and dollar-yen exchange rates with that of a GARCH model and a related RiskMetrics volatility forecast. Review of the benchmark GARCH and RiskMetrics volatility models; Description of genetic algorithms and genetic programming; Information on the forecasting procedures followed by the genetic program.

A market mechanism is basically driven by a superposition of decisions of many agents optimizing their profit. The macroeconomic price dynamic is a consequence of the cumulated excess demand/supply created on this micro level. The behavior analysis of a small number of agents is well understood through the game theory. In case of a large number of agents one may use the limiting case that an individual agent does not have an influence on the market, which allows the aggregation of agents by statistic methods. In contrast to this restriction, we can omit the assumption of an atomic market structure, if we model the market through a multi-agent approach. The contribution of the mathematical theory of neural networks to the market price formation is mostly seen on the econometric side: neural networks allow the fitting of high dimensional nonlinear dynamic models. Furthermore, in our opinion, there is a close relationship between economics and the modeling ability of neural networks because a neuron can be interpreted as a simple model of decision making. With this in mind, a neural network models the interaction of many decisions and, hence, can be interpreted as the price formation mechanism of a market. [ABSTRACT FROM AUTHOR]

An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated form is the risk stemming from geopolitical events, such as wars, political tensions, and conflicts. In contrast, the effects of terrorist acts have been thoroughly examined in the relevant literature. In this paper, we examine the potential ability of geopolitical risk of 14 emerging countries to forecast several assets: oil prices, exchange rates, national stock indices, and the price of gold. In doing so, we build forecasting models that are based on machine learning techniques and evaluate the associated out-of-sample forecasting error in various horizons from one to twenty-four months ahead. Our empirical findings suggest that geopolitical events in emerging countries are of little importance to the global economy, since their effect on the assets examined is mainly transitory and only of regional importance. In contrast, gold prices seem to be affected by fluctuation in geopolitical risk. This finding may be justified by the nature of investments in gold, in that they are typically used by economic agents to hedge risk. [ABSTRACT FROM AUTHOR]