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Crédito rural e produto agropecuário municipal: uma análise de causalidade; Rural credit and agricultural output: a causality analysis

Cavalcanti, Isabel Machado
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 28/11/2008 Português
Relevância na Pesquisa
36.77%
O objetivo deste trabalho é estudar a relação de causalidade entre crédito rural e produto agropecuário. Utilizando dados municipais do período 1999-2004, aplicou-se a metodologia de Granger e Huang (1997), que permite identificar o sentido da causalidade entre duas variáveis em um contexto de painel. Contrariamente à grande parte da literatura que estudou as relações de causalidade entre sistema financeiro e crescimento econômico, este trabalho não identificou a causalidade partindo da variável financeira para o produto. Em geral, os resultados apontaram causalidade unidirecional, partindo do Produto Interno Bruto da agropecuária para o crédito rural.; The main goal of this essay is to evaluate the causal relations between rural credit and agricultural output. Using municipal data for the period 1999-2004, we have implemented the Granger and Huang (1997) methodology, which allows us to identify the causality direction between two variables in a data panel context. Differently from a large part of the literature that has studied causal relations between finance and growth, this work did not find causality from the financial variable towards output. Instead, the results draw attention to unidirectional causality from agricultural output to rural credit.

Causalidade e co-integração de séries temporais : mercado futuro de trigo e a vista de farinha de trigo

Amaro, Rodrigo Kulpa
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Trabalho de Conclusão de Curso Formato: application/pdf
Português
Relevância na Pesquisa
36.75%
Os mercados futuros existem desde a Idade Média, mas a sua importância econômica cresceu após o surgimento da internet e dos pregões eletrônicos. Com o aumento do volume de negociações, os mercados futuros ganharam liquidez e transparência. A partir disso, diversos estudos acadêmicos foram realizados, explorando as funcionalidades dos mercados futuros. Observa-se que ainda há muitas funções indiretas que podem ser exercidas pelos derivativos em geral, não restritas à proteção do risco de variação de preços. As indústrias de transformação elaboram o seu planejamento utilizando expectativas para o futuro, considerando a probabilidade de ocorrência de cada cenário possível. Os critérios para a formulação dessas expectativas são uma escolha particular de cada empresa, porém esse trabalho propõe o uso de dados históricos dos contratos futuros de trigo da Chicaco Mercantile Exchange para embasar uma expectativa para o preço a vista no futuro da farinha de trigo comprada pela PAVIOLI S.A. Para isso, foram utilizados os testes de causalidade de Granger e de co-integração Engle-Granger.; Future markets and options trading exist since Middle Age, but their economic significance grown after the appearance of the internet and the real time quotes. As the operations volume grown...

Evidências de bolhas de preços no mercado acionário brasileiro

Fernandes, Bruno Vinícius Ramos
Fonte: Universidade de Brasília Publicador: Universidade de Brasília
Tipo: Dissertação
Português
Relevância na Pesquisa
37.01%
Dissertação (mestrado)—Universidade de Brasília, Programa Multiinstitucional e Inter-regional de Pós-Graduação em Ciências Contábeis, 2008.; Atualmente, a existência de bolhas na formação dos preços dos ativos tem sido motivo de grande preocupação para governantes e investidores nos países onde há mercados de capitais relevantes. A existência do componente de bolha na formação dos preços pode ser indicada pelo seu desvio em relação ao seu valor fundamental. No caso das ações, uma suspeita de bolha de preços pode ser evidenciada quando os preços se deslocam em relação aos dividendos no longo prazo. O presente estudo buscou encontrar evidências sobre ocorrência de bolhas de preços no mercado acionário brasileiro no período de 1994 a 2007. Foram feitos testes no mercado de forma geral e em 17 setores classificados pelo banco de dados Economática®. Para testar a evidência de bolhas no mercado como um todo, foi utilizado o Ibovespa como proxy do preço médio das ações, e como indicador médio da distribuição de dividendos, foi construído um índice, de dividendos distribuídos, baseado nas próprias carteiras do Ibovespa no período. Foram feitos os testes de cointegração Engle-Granger e Johansen...

Bootstrap Panel Granger-Causality Between Government spending and Revenue

Afonso, António; Rault, Christophe
Fonte: ISEG – Departamento de Economia Publicador: ISEG – Departamento de Economia
Tipo: Outros
Publicado em //2008 Português
Relevância na Pesquisa
46.5%
Using bootstrap panel analysis, allowing for cross-country correlation, without the need of pre-testing for unit roots, we study the causality between government revenue and spending for the EU in the period 1960-2006. Spend-and-tax causality is found for Italy, France, Spain, Greece, and Portugal, while tax-and-spend evidence is present for Germany, Belgium, Austria Finland and the UK, and for several EU New Member States.

Causality Analysis of Neural Connectivity: Critical Examination of Existing Methods and Advances of New Methods

Hu, Sanqing; Dai, Guojun; Worrell, Gregory A.; Dai, Qionghai; Liang, Hualou
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
37.19%
Granger causality (GC) is one of the most popular measures to reveal causality influence of time series and has been widely applied in economics and neuroscience. Especially, its counterpart in frequency domain, spectral GC, as well as other Granger-like causality measures have recently been applied to study causal interactions between brain areas in different frequency ranges during cognitive and perceptual tasks. In this paper, we show that: 1) GC in time domain cannot correctly determine how strongly one time series influences the other when there is directional causality between two time series, and 2) spectral GC and other Granger-like causality measures have inherent shortcomings and/or limitations because of the use of the transfer function (or its inverse matrix) and partial information of the linear regression model. On the other hand, we propose two novel causality measures (in time and frequency domains) for the linear regression model, called new causality and new spectral causality, respectively, which are more reasonable and understandable than GC or Granger-like measures. Especially, from one simple example, we point out that, in time domain, both new causality and GC adopt the concept of proportion, but they are defined on two different equations where one equation (for GC) is only part of the other (for new causality)...

Confounding Effects of Phase Delays on Causality Estimation

Vakorin, Vasily A.; Mišić, Bratislav; Krakovska, Olga; Bezgin, Gleb; McIntosh, Anthony R.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 21/01/2013 Português
Relevância na Pesquisa
36.91%
Linear and non-linear techniques for inferring causal relations between the brain signals representing the underlying neuronal systems have become a powerful tool to extract the connectivity patterns in the brain. Typically these tools employ the idea of Granger causality, which is ultimately based on the temporal precedence between the signals. At the same time, phase synchronization between coupled neural ensembles is considered a mechanism implemented in the brain to integrate relevant neuronal ensembles to perform a cognitive or perceptual task. Phase synchronization can be studied by analyzing the effects of phase-locking between the brain signals. However, we should expect that there is no one-to-one mapping between the observed phase lag and the time precedence as specified by physically interacting systems. Specifically, phase lag observed between two signals may interfere with inferring causal relations. This could be of critical importance for the coupled non-linear oscillating systems, with possible time delays in coupling, when classical linear cross-spectrum strategies for solving phase ambiguity are not efficient. To demonstrate this, we used a prototypical model of coupled non-linear systems, and compared three typical pipelines of inferring Granger causality...

Markov Switching Causality and the Money-Output Relationship

RAVN, Morten O.; PSARADAKIS, Zacharias; SOLA, Martin
Fonte: Instituto Universitário Europeu Publicador: Instituto Universitário Europeu
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
36.77%
The causal link between monetary variables and output is one of the most studied issues in macroeconomics. One puzzle from this literature is that the results of causality tests appear to be sensitive with respect to the sample period that one considers. As a way of overcoming this difficulty, we propose a method for analysing Granger causality which is based on a vector autoregressive model with time-varying parameters. We model parameter time-variation so as to reflect changes in Granger causality, and assume that these changes are stochastic and governed by an unobservable Markov chain. When applied to US data, our methodology allows us to reconcile previous puzzling differences in the outcome of conventional tests for money-output causality.

Granger-Causal Analysis of Conditional Mean and Volatility Models

WOŹNIAK, Tomasz
Fonte: Instituto Universitário Europeu Publicador: Instituto Universitário Europeu
Tipo: Tese de Doutorado Formato: application/pdf; digital
Português
Relevância na Pesquisa
36.86%
Recent economic developments have shown the importance of spillover and contagion effects in financial markets as well as in macroeconomic reality. Such effects are not limited to relations between the levels of variables but also impact on the volatility and the distributions. Granger causality in conditional means and conditional variances of time series is investigated in the framework of several popular multivariate econometric models. Bayesian inference is proposed as a method of assessment of the hypotheses of Granger noncausality. First, the family of ECCC-GARCH models is used in order to perform inference about Granger-causal relations in second conditional moments. The restrictions for second-order Granger noncausality between two vectors of variables are derived. Further, in order to investigate Granger causality in conditional mean and conditional variances of time series VARMA-GARCH models are employed. Parametric restrictions for the hypothesis of noncausality in conditional variances between two groups of variables, when there are other variables in the system as well are derived. These novel conditions are convenient for the analysis of potentially large systems of economic variables. Bayesian testing procedures applied to these two problems...

Estudo da relación entre a economía real e a economía financeira: análise de causalidade entre o PIB e os principais índices bolsistas para o período 1994-2014

Barcia Ferro, Lucía
Fonte: Universidade da Corunha Publicador: Universidade da Corunha
Tipo: Dissertação de Mestrado
Português
Relevância na Pesquisa
36.63%
Traballo fin de mestrado (UDC.ECO). Banca e finanzas. Curso 2014/2015; [Resumo] A raíz da presente crise económica púxose de manifesto e comprendeuse a importancia de analizar o que mostran os mercados actuais para evitar, na medida do posible, grandes recesións nun futuro próximo como a acontecida a principios do 2008. Así, no presente traballo de fin de mestrado realízase un estudo sobre a causalidade entre o PIB de cinco países: España, Alemaña, Reino Unido, Estados Unidos e Xapón, e o seu respectivo índice bolsista por excelencia. Coa finalidade de levar a cabo o estudo detállanse en primeiro lugar os conceptos chave da análise e a literatura dos principais autores para realizar, en segundo lugar, unha análise econométrica mediante o método de Granger dos datos trimestrais de tales países; os resultados obtidos permiten chegar á conclusión de que, salvo certas excepcións, é a evolución da Bolsa a que causa no sentido de Granger a da variable representativa da economía real.; [Abstract] The current economic crisis revealed the importance of analysing what current markets can show to avoid, insofar as possible in the near future, major recessions, as occurred in early 2008. So, in the present Master Thesis...

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

DUFOUR, Jean-Marie; JOUINI, Tarek
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Artigo de Revista Científica Formato: 267980 bytes; application/pdf
Português
Relevância na Pesquisa
46.43%
Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

A relação entre poupança, investimento e crescimento económico na Europa

Moreira, Rita Ferreira Rodrigues de Sousa
Fonte: Instituto Superior de Economia e Gestão Publicador: Instituto Superior de Economia e Gestão
Tipo: Dissertação de Mestrado
Publicado em //2014 Português
Relevância na Pesquisa
36.78%
Mestrado em Finanças; O estudo das relações entre a poupança, o investimento e o crescimento económico é intemporal e controverso tendo assumido especial interesse, mais recentemente, com a crise económica e financeira iniciada em 2007. Para além das teorias económicas, são vários os trabalhos teóricos e empíricos que procuram analisar e explicar as relações de causalidade entre a poupança, o investimento e o crescimento das economias, mas são poucos os que estudam estas relações na Europa. Este trabalho procura assim, analisar as relações de causalidade estabelecidas entre a taxa de crescimento da poupança, a taxa de crescimento do investimento e a taxa de crescimento do PIB real para uma amostra de vinte e seis países europeus, entre 2002 e 2011. Os resultados obtidos através do teste de causalidade de Granger permitem-nos concluir que tanto a taxa de crescimento da poupança como a taxa de crescimento do investimento contribuem para o crescimento económico. A relação inversa também se verifica, embora estatisticamente menos significativa. Relativamente à relação de causalidade entre a taxa de crescimento da poupança e a taxa de crescimento do investimento observa-se que um aumento do investimento conduz à diminuição da poupança. Contudo...

Learning vector autoregressive models with focalised Granger-causality graphs

Gregorova, Magda; Kalousis, Alexandros; Marchand-Maillet, Stéphane; Wang, Jun
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 07/07/2015 Português
Relevância na Pesquisa
36.72%
While the importance of Granger-causal (G-causal) relationships for learning vector autoregressive models (VARs) is widely acknowledged, the state-of-the-art VAR methods do not address the problem of discovering the underlying G-causality structure in a principled manner. VAR models can be restricted if such restrictions are supported by a strong domain theory (e.g. economics), but without such strong domain-driven constraints the existing VAR methods typically learn fully connected models where each series is G-caused by all the others. We develop new VAR methods that address the problem of discovering structure in the G-causal relationships explicitly. Our methods learn sparse G-causality graphs with small sets of \emph{focal} series that govern the dynamical relationships within the time-series system. While maintaining competitive forecasting accuracy, the sparsity in the G-causality graphs that our methods achieve is far from reach of any of the state-of-the-art VAR methods.

Adaptive estimation of vector autoregressive models with time-varying variance: application to testing linear causality in mean

Patilea, Valentin; Raïssi, Hamdi
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
36.66%
Linear Vector AutoRegressive (VAR) models where the innovations could be unconditionally heteroscedastic and serially dependent are considered. The volatility structure is deterministic and quite general, including breaks or trending variances as special cases. In this framework we propose Ordinary Least Squares (OLS), Generalized Least Squares (GLS) and Adaptive Least Squares (ALS) procedures. The GLS estimator requires the knowledge of the time-varying variance structure while in the ALS approach the unknown variance is estimated by kernel smoothing with the outer product of the OLS residuals vectors. Different bandwidths for the different cells of the time-varying variance matrix are also allowed. We derive the asymptotic distribution of the proposed estimators for the VAR model coefficients and compare their properties. In particular we show that the ALS estimator is asymptotically equivalent to the infeasible GLS estimator. This asymptotic equivalence is obtained uniformly with respect to the bandwidth(s) in a given range and hence justifies data-driven bandwidth rules. Using these results we build Wald tests for the linear Granger causality in mean which are adapted to VAR processes driven by errors with a non stationary volatility. It is also shown that the commonly used standard Wald test for the linear Granger causality in mean is potentially unreliable in our framework. Monte Carlo experiments illustrate the use of the different estimation approaches for the analysis of VAR models with stable innovations.

Causality as a unifying approach between activation and connectivity analysis of fMRI data

Dubbini, Nevio
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 24/02/2011 Português
Relevância na Pesquisa
36.82%
This paper indicates causality as the tool that unifies the analysis of both activations and connectivity of brain areas, obtained with fMRI data. Causality analysis is commonly applied to study connectivity, so this work focuses on demonstrating that also the detection of activations can be handled with a causality analysis. We test our method on finger tapping data, in which GLM and Granger Causality approaches are compared in finding activations. Granger causality not only performs the task well, but indeed we obtained a better localization (i.e. precision) of activations. As a result we claim that causality must be the main tool to investigate activations, since it is a measure of "how much" the stimulus influences the BOLD signal, and since it unifies connectivity and activations analysis under the same area.

Measures of Causality in Complex Datasets with application to financial data

Zaremba, Anna; Aste, Tomaso
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
36.8%
This article investigates the causality structure of financial time series. We concentrate on three main approaches to measuring causality: linear Granger causality, kernel generalisations of Granger causality (based on ridge regression and the Hilbert--Schmidt norm of the cross-covariance operator) and transfer entropy, examining each method and comparing their theoretical properties, with special attention given to the ability to capture nonlinear causality. We also present the theoretical benefits of applying non-symmetrical measures rather than symmetrical measures of dependence. We apply the measures to a range of simulated and real data. The simulated data sets were generated with linear and several types of nonlinear dependence, using bivariate, as well as multivariate settings. An application to real-world financial data highlights the practical difficulties, as well as the potential of the methods. We use two real data sets: (1) U.S. inflation and one-month Libor; (2) S$\&$P data and exchange rates for the following currencies: AUDJPY, CADJPY, NZDJPY, AUDCHF, CADCHF, NZDCHF. Overall, we reach the conclusion that no single method can be recognised as the best in all circumstances, and each of the methods has its domain of best applicability. We also highlight areas for improvement and future research.; Comment: 40 pages; 13 figures

Vector Autoregressive Models With Measurement Errors for Testing Ganger Causality

Patriota, Alexandre G.; Sato, Joao R.; Blas, Betsabe G.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 30/11/2009 Português
Relevância na Pesquisa
36.66%
This paper develops a method for estimating parameters of a vector autoregression (VAR) observed in white noise. The estimation method assumes the noise variance matrix is known and does not require any iterative process. This study provides consistent estimators and shows the asymptotic distribution of the parameters required for conducting tests of Granger causality. Methods in the existing statistical literature cannot be used for testing Granger causality, since under the null hypothesis the model becomes unidentifiable. Measurement error effects on parameter estimates were evaluated by using computational simulations. The results show that the proposed approach produces empirical false positive rates close to the adopted nominal level (even for small samples) and has a good performance around the null hypothesis. The applicability and usefulness of the proposed approach are illustrated using a functional magnetic resonance imaging dataset.; Comment: manuscript submitted for possible publication

Synergy and redundancy in the Granger causal analysis of dynamical networks

Stramaglia, Sebastiano; Cortes, Jesus M.; Marinazzo, Daniele
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
37.07%
We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. Whilst we show that fully conditioned Granger causality is not affected by synergy, the pairwise analysis fails to put in evidence synergetic effects. In cases when the number of samples is low, thus making the fully conditioned approach unfeasible, we show that partially conditioned Granger causality is an effective approach if the set of conditioning variables is properly chosen. We consider here two different strategies (based either on informational content for the candidate driver or on selecting the variables with highest pairwise influences) for partially conditioned Granger causality and show that depending on the data structure either one or the other might be valid. On the other hand, we observe that fully conditioned approaches do not work well in presence of redundancy, thus suggesting the strategy of separating the pairwise links in two subsets: those corresponding to indirect connections of the fully conditioned Granger causality (which should thus be excluded) and links that can be ascribed to redundancy effects and, together with the results from the fully connected approach...

The frequency domain causality analysis between energy consumption and income in the United States; The frequency domain causality analysis between energy consumption and income in the United States

Tiwari, Aviral Kumar
Fonte: Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade de RP Publicador: Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade de RP
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; ; ; Formato: application/pdf
Publicado em 01/03/2014 Português
Relevância na Pesquisa
36.77%
Através do teste de casualidade de Granger, nós investigamos o domínio de frequência entre o consumo primário de energia/eletricidade e o produto interno bruto (PIB) dos Estados Unidos; aplicando a abordagem de Lemmens et al. (2008) e cobrindo o período entre Janeiro de 1973 a Dezembro de 2008. Nós achamos relações causal e causal reversa entre o consumo primário de energia e PIB, e o consumo de eletricidade e PIB variam através das frequências. Nossa contribuição única na literatura existente reside na decomposição da causalidade com base em horizontes de tempo e demonstração bi-direcional de causalidade de curto prazo, médio-prazo e longo-prazo entre PIB e consumo primário de energia/eletricidade e assim provendo evidência para a "feedback hypothesis". Estes resultados têm importantes implicações para o planejamento energértico de curto, médio e longo prazo dos Estados Unidos e políticas relacionadas ao crescimento econômico.; We investigated Granger-causality in the frequency domain between primary energy consumption/electricity consumption and GDP for the US by employing approach of Lemmens et al. (2008) and covering the period of January, 1973 to December, 2008. We found that causal and reverse causal relations between primary energy consumption and GDP and electricity consumption and GDP vary across frequencies. Our unique contribution in the existing literature lies in decomposing the causality on the basis of time horizons and demonstrating bidirectional the short-run...

Measures of causality in complex datasets with application to financial data

Zaremba, Anna; Aste, Tomaso
Fonte: Multidisciplinary Digital Publishing Institute (MDPI) Publicador: Multidisciplinary Digital Publishing Institute (MDPI)
Tipo: Article; PeerReviewed Formato: application/pdf
Publicado em //2014 Português
Relevância na Pesquisa
36.8%
This article investigates the causality structure of financial time series. We concentrate on three main approaches to measuring causality: linear Granger causality, kernel generalisations of Granger causality (based on ridge regression and the Hilbert-Schmidt norm of the cross-covariance operator) and transfer entropy, examining each method and comparing their theoretical properties, with special attention given to the ability to capture nonlinear causality. We also present the theoretical benefits of applying non-symmetrical measures rather than symmetrical measures of dependence. We apply the measures to a range of simulated and real data. The simulated data sets were generated with linear and several types of nonlinear dependence, using bivariate, as well as multivariate settings. An application to real-world financial data highlights the practical difficulties, as well as the potential of the methods. We use two real data sets: (1) U.S. inflation and one-month Libor; (2) S&P data and exchange rates for the following currencies: AUDJPY, CADJPY, NZDJPY, AUDCHF, CADCHF, NZDCHF. Overall, we reach the conclusion that no single method can be recognised as the best in all circumstances, and each of the methods has its domain of best applicability. We also highlight areas for improvement and future research.

Modelling the rand and commodity prices: A Granger causality and cointegration analysis

Schaling,Eric; Ndlovu,Xolani; Alagidede,Paul
Fonte: South African Journal of Economic and Management Sciences Publicador: South African Journal of Economic and Management Sciences
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2014 Português
Relevância na Pesquisa
46.5%
This paper examines the 'commodity currency' hypothesis of the Rand, that is, the postulate that the currency moves in line with commodity prices, and analyses the associated causality using nominal data between 1996 and 2010. We address both the short run and long run relationship between commodity prices and exchange rates. We find that while the levels of the series of both assets are difference stationary, they are not cointegrated. Further, we find the two variables are negatively related, with strong and significant causality running from commodity prices to the exchange rate and not vice versa, implying exogeneity in the determination of commodity prices with respect to the nominal exchange rate. The strength of the relationship is significantly weaker than other OECD commodity currencies. We surmise that the relationship is dynamic over time owing to the portfolio-rebalance argument and the Commodity Terms of Trade (CTT) effect and, in the absence of an error correction mechanism, this disconnect may be prolonged. For commodity and currency market participants, this implies that while futures and forward commodity prices may be useful leading indicators of future currency movements, the price risk management strategies may need to be recalibrated over time.