Inhaltsverzeichnis
Auswahl der wissenschaftlichen Literatur zum Thema „Rupee-Dollar exchange rate volatility“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit den Listen der aktuellen Artikel, Bücher, Dissertationen, Berichten und anderer wissenschaftlichen Quellen zum Thema "Rupee-Dollar exchange rate volatility" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Zeitschriftenartikel zum Thema "Rupee-Dollar exchange rate volatility"
1
T., Lakshmanasamy. „Relationship Between Exchange Rate and Stock Market Volatilities in India“. International Journal of Finance Research 2, Nr. 4 (08.11.2021): 244–59. http://dx.doi.org/10.47747/ijfr.v2i4.443.
Der volle Inhalt der QuelleAnnotation:
With increasing globalisation and integration of national stock exchanges, for the global investor, the portfolio risk increases not only from the local stock market volatility but also in the exchange rate risk. This paper examines the exchange rate volatility effect on volatility in stock market return from India’s perspective for the period January 2010 to December 2015, applying ARCH and GARCH estimation. The daily data of the BSE SENSEX returns, exchange rates of US dollar/rupee, British pound/rupee, Euros/rupee are used. It is estimated that the Euro/rupee exchange rate volatility has a significant positive effect on the BSE SENSEX return volatility, while the effect of the US dollar/rupee and British pound/rupee exchange rate the volatilities are insignificantly negative. The larger GARCH parameter over the ARCH term indicates that the own lagged values of the stock return cause more volatility in stock returns than the innovations. There exists a highly persistent effect of shocks to the BSE SENSEX return and the volatility effect wanes only slowly
2
Bhat, Aparna Prasad. „The economic determinants of the implied volatility function for currency options“. International Journal of Emerging Markets 13, Nr. 6 (29.11.2018): 1798–819. http://dx.doi.org/10.1108/ijoem-08-2017-0308.
Der volle Inhalt der QuelleAnnotation:
Purpose The purpose of this paper is to ascertain the pattern of the implied volatility function for currency options traded on the National Stock Exchange of India (NSE), identify its potential determinants and to investigate any seasonality in the pattern. Design/methodology/approach The paper examines four different specifications for the implied volatility smile of exchange-traded dollar-rupee options. These specifications are tested by running Ordinary Least Squares (OLS) regressions on a daily basis for all options over the entire sample period. Seven potential determinants for the shape of the volatility function are identified. Contemporaneous and lead-lag relationships between these determinants and the shape of the volatility function are examined using OLS and multivariate VAR. Impulse response functions are employed to test the strength and persistence of the lead-lag relations. Seasonality of the smile pattern is tested using OLS. Findings The study shows that the implied volatility function for dollar-rupee options is asymmetric and varies with the time to maturity of the option. Historical volatility, momentum and jumps in the exchange rate, time to maturity, traded volume of options and volatility in the stock market appear to Granger-cause the shape of the volatility smile. Feedback causality is observed from the shape of the smile to the volatility, momentum and jumps in the exchange rate and trading volume of currency options. A weak day-of-the-week effect is observed in the pattern of the volatility smile. Practical implications The study sheds light on the potential determinants of the smile and highlights the predictive power of the smile which findings can be useful to market practitioners for pricing and hedging of dollar-rupee options. The study has strong practical implications during a period of increased volatility in the dollar-rupee pair. Originality/value Most of the existing literature regarding implied volatility smiles has focused either on the volatility smile of US equity index options or that of major liquid currencies. There is a need for such studies in the context of options on emerging market currencies such as the Indian rupee which are characterized by thin trading and frequent central bank intervention and signaling. To the best of the author’s knowledge this study is the first to focus on the volatility smile of exchange-traded options on the US dollar–Indian rupee.
3
Mohanty, Debasis, Amiya Kumar Mohapatra, Sasikanta Tripathy und Rahul Matta. „Nexus between foreign exchange rate and stock market: evidence from India“. Investment Management and Financial Innovations 20, Nr. 3 (31.07.2023): 79–90. http://dx.doi.org/10.21511/imfi.20(3).2023.07.
Der volle Inhalt der QuelleAnnotation:
This study examines the impact of foreign exchange rate fluctuations on various NSE capitalized indices of India. Five exchange rates were chosen based on trading contracts in the currency derivative segment of NSE. These exchange rates are US Dollar-Indian Rupee (USD/INR), Euro-Indian Rupee (EUR/INR), Great Britain Pound-Indian Rupee (GBP/INR), Chinese Yuan-Indian Rupee (CNY/INR) and Japanese Yen-Indian Rupee (JPY/INR), which are used as a regressor in this study. The data of NSE Nifty large-cap 100, Nifty mid-cap 100 and Nifty small-cap from December 1, 2012 to December 1, 2022 was considered for the study. GARCH (1, 1) model was used to analyze the nexus between exchange rate fluctuations and capitalized indices, and it was further validated by DCC GARCH to evaluate the volatility spillover. The result shows that exchange rate fluctuations have a positive effect on stock market volatility along with a varying degree of incidence on small-cap, mid-cap, and large-cap. DCC α has been found to be significant in USD & GBP for small-cap, and GBP & CNY for mid-cap. On the other hand, USD, Euro, CNY and JPY have a significant impact on the large-cap index in the short-run. Further, it is found that there is long-run spillover effect (DCC β) of exchange rates on all capitalized indices of the Indian stock market, and it is highest in in the large-cap case.
4
Qabhobho, Thobekile. „Assessing the Asymmetric Effect of Local Realized Exchange Rate Volatility and Implied Volatilities in Energy Market on Exchange Rate Returns in BRICS“. International Journal of Energy Economics and Policy 13, Nr. 2 (24.03.2023): 231–39. http://dx.doi.org/10.32479/ijeep.13685.
Der volle Inhalt der QuelleAnnotation:
This paper investigates the leverage effect of local realised exchange rate volatility and implied volatilities in energy market on exchange rate returns in BRICS for the period May 7, 2012 to March 31, 2022, using the quantile regression technique. This paper reveals that oil implied volatility shocks (OVX changes) have a significant negative impact on Russian-U.S. Dollar exchange rate returns in all quantiles. When it comes to the Indian rupee and Chinese RMB returns/Dollar, the adverse effects of OVX are most apparent in both normal and booming market conditions. Although South Africa's currency rate returns are affected by both slump- and bust-market situations, Brazil also tends to be in higher quantiles. The implied volatility indices in the energy market have a substantial and considerable negative impact on the BRICS currencies, with the exception of China, where the effect is only noticeable in the upper extreme quantiles. The policy implications and suggestions are discussed.
5
Khan, Abdul Jalil, und Parvez Azim. „One-Step-Ahead Forecastability of GARCH (1,1): A Comparative Analysis of USD- and PKR-Based Exchange Rate Volatilities“. LAHORE JOURNAL OF ECONOMICS 18, Nr. 1 (01.01.2013): 1–38. http://dx.doi.org/10.35536/lje.2013.v18.i1.a1.
Der volle Inhalt der QuelleAnnotation:
This study aims to capture volatility patterns using GARCH (1,1) models. It evaluates these models to obtain one-step-ahead forecastabilities by employing four major forecasting evaluation criteria, and compares two different currencies— the Pakistan rupee and the US dollar—as domestic and foreign currency-valued exchange rates, respectively. The results show that using an international vehicle currency is favorable in Pakistan’s context. However, the Kuwaiti dinar, Canadian dollar, US dollar, Singapore dollar, Hong Kong dollar, and Malaysian ringgit are found to be preferable when performing direct international transactions. Using the root mean square errors and mean absolute errors techniques, the study also assess the robustness of measuring one-step-ahead forecasts.
6
Patnaik, Anuradha. „International Transmission of Monetary Policy: The Usa to India“. International Letters of Social and Humanistic Sciences 54 (Juni 2015): 53–62. http://dx.doi.org/10.18052/www.scipress.com/ilshs.54.53.
Der volle Inhalt der QuelleAnnotation:
The present study attempts measure the transmission of monetary impulse from the USA to India by trying to quantify the extent of volatility spillover from the US monetary policy to the exchange rate and interest rate of India. By applying a t-DCC MGARCH model to daily data on Fed Funds Rate, Rupee Dollar Exchange Rate and the Call Money rate of India it was found that there is considerable volatility spillover from the Fed Rate to the exchange rate. Spillover is also clearly evident in case of the call rate. The extent of spillover is higher for the foreign exchange rate than the call money rate. However, it was also noticed that the spillover is asymmetric in either of the cases and is higher during phases of high volatility. In an era of flexible exchange rates excessive dependence of the Indian Economy on short term capital flows to finance the current account deficits which raises the dollar demand and exposes the Indian economy to the Monetary Policy of the US, needs to be reduced. Reforms in the nature of capital flows is also the need of the hour.
7
Sharma, Chandan, und Rajat Setia. „Macroeconomic fundamentals and dynamics of the Indian rupee-dollar exchange rate“. Journal of Financial Economic Policy 7, Nr. 4 (02.11.2015): 301–26. http://dx.doi.org/10.1108/jfep-11-2014-0069.
Der volle Inhalt der QuelleAnnotation:
Purpose – This paper aims to examine the relationship between Indian rupee-US dollar exchange rate and the macroeconomic fundamentals for the post-economic reform period. Design/methodology/approach – The authors have used an empirical model which includes a range of important macroeconomic variables based on the basic monetary theories of exchange rate determination. At the first stage of the analysis, they have tested structural break in the data. Subsequently, they have employed the fully modified ordinary least square, Wald’s coefficient restriction and impulse response functions (IRF) to estimate the monetary model in the long- and short-run horizons. Findings – Results of analyses indicate that the macroeconomic fundamentals determine exchange rate in a significant way, but their effect varies sizably across the periods. The IRF illustrate the importance of interest rate in controlling exchange rate volatility. Practical implications – The analysis of the behavior of inter-relationship among macroeconomic variables will help policymakers in a deep-rooted understanding of this complex and time-varying relationship. Originality/value – Most of the existing studies have tested the impact of a single or a few macroeconomic fundamentals on exchange rate. But in the present study, we have tested the impact of a range of important variables, i.e. money supply, real income or output, price level and trade balance. Further, considering the importance of structural breaks in data, they authors have employed standard tests of structural break and incorporated the issue in the cointegration analysis.
8
Aravind M. „FX Volatility Impact on Indian Stock Market: An Empirical Investigation“. Vision: The Journal of Business Perspective 21, Nr. 3 (10.07.2017): 284–94. http://dx.doi.org/10.1177/0972262917716760.
Der volle Inhalt der QuelleAnnotation:
Examining the interrelationship between currency market volatility and stock market volatility will create abundant trading opportunities to the investors irrespective of whether the return of one market is moving up or down. This research work intended to examine how the exchange rate volatility between Indian rupee and foreign currencies, such as US dollar, euro, Japanese yen and British pound, can influence the return and volatility of the Indian stock market. The research data extensively cover daily price observations of foreign currencies as well as Nifty index for 1500 days. The generalized autoregressive conditional heteroskedasticity (GARCH) is used for modelling foreign exchange (FX) rates volatility and its impact across Indian stock market. The mean equation of the model confirms that any appreciation in Indian rupee will lead to channelization of more funds towards stock market. Further, it is validated that the volatility shocks between the stock market and currency market are quite persistent. Besides the model also points that the volatility attributes are very strong between US dollar and Nifty. The Granger causality test wrap up with a finding that the volatility shocks of British pound have a causal relation with Nifty return. The result of this study will help the domestic as well as foreign investors in favour of portfolio diversification decisions. The study also spots that the policymakers can indirectly intervene into stock market through monitory policy measures.
9
Shah, Mohammad Samal. „Analysing the Factors Behind Exchange Rate Fluctuations in India“. International Journal for Research in Applied Science and Engineering Technology 12, Nr. 4 (30.04.2024): 969–92. http://dx.doi.org/10.22214/ijraset.2024.59951.
Der volle Inhalt der QuelleAnnotation:
Abstract: The study gives an overview of the various determinants of the exchange rate movements in India. Out of the multiple factors affecting the Rupee-Dollar value the impact of Interest rate differential, Trade deficit of India, Foreign Net investment inflows to India, Oil prices, and Gold prices (in the short term) on the exchange rate has been studied using Regression analysis and correlation and the role they played by the above mentioned variables in determining the exchange rate during the Global Financial Crisis of 2008-2010 and during the Covid-19 Period from 2020-2023. Exchange rate fluctuations play a crucial role in shaping the economy of a country, and India is no exception. The exchange rate of the Indian rupee against major world currencies is subject to constant fluctuations, influenced by a multitude of domestic and global factors. These fluctuations have significant implications for various stakeholders, including businesses, consumers, investors, and policymakers. Several factors contribute to the exchange rate fluctuations in India. One of the primary factors is the demand and supply dynamics of foreign exchange. Factors such as trade balance, foreign direct investments, portfolio investments, remittances impact the demand for and supply of foreign currency, Crude oil prices, Gold prices, and thereby affecting the exchange rate. In addition, macroeconomic variables like inflation, interest rates, and economic growth also play a crucial role in determining exchange rate movements. For instance, a high inflation rate in India compared to trading partner countries can lead to a depreciation of the rupee. Global economic conditions and geopolitical events are other significant factors influencing exchange rate fluctuations in India. Economic developments in major trading partners, changes in global commodity prices, and geopolitical tensions can all impact investor sentiment and capital flows, leading to fluctuations in the exchange rate. For example, uncertainties related to Brexit or trade tensions between major economies can trigger volatility in currency markets. Monetary policy decisions by the Reserve Bank of India (RBI) also play a crucial role in influencing exchange rates. Interest rate changes, open market operations, and forex interventions by the central bank can impact the value of the rupee visa-vis other currencies. The RBI often intervenes in the foreign exchange market to stabilize the rupee or prevent extreme volatility. Apart from these factors, market speculation, investor sentiment, and technological advancements in the financial markets can also contribute to exchange rate fluctuations. High-frequency trading, algorithmic trading, and the use of complex financial instruments by market participants can amplify exchange rate movements and increase volatility
10
Akhtar, Sohail, Maham Ramzan, Sajid Shah, Iftikhar Ahmad, Muhammad Imran Khan, Sadique Ahmad, Mohammed A. El-Affendi und Humera Qureshi. „Forecasting Exchange Rate of Pakistan Using Time Series Analysis“. Mathematical Problems in Engineering 2022 (24.08.2022): 1–11. http://dx.doi.org/10.1155/2022/9108580.
Der volle Inhalt der QuelleAnnotation:
Exchange rates are crucial in regulating the foreign exchange market's dynamics. Because of the unpredictability and volatility of currency rates, the exchange rate prediction has become one of the most challenging applications of financial time series forecasting. This study aims to build and compare the accuracy of various methods. The time series model Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) are utilized to forecast the daily US dollar to Pakistan rupee currency exchange rates (USD/PKR). Lagged observations of the data series and moving average technical analysis are used in both models. Explanatory factors were used as indicators, and the prediction performance was assessed using a variety of commonly known statistical metrics. These statistical metrics suggested the presence of conditional heteroscedasticity. Thus, the process turns to capture the volatility effect of conditional heteroscedasticity through GARCH modeling. It may be inferred based on the results of tentative models; that the ARCH model outperforms the GARCH model in terms of predicting the USD/PKR exchange rate.
Dissertationen zum Thema "Rupee-Dollar exchange rate volatility"
1
Sikdar, Suman. „Rupee-Dollar exchange rate volatility and uncovered interest rate parity doctrine- A time -series econometric study with beveridge Nelson decomposition“. Thesis, University of North Bengal, 2017. http://ir.nbu.ac.in/handle/123456789/2803.
Der volle Inhalt der Quelle
2
Twahirwa, Eunice Ishimwe Mariella. „Internal Versus External Reasons for the Rand-Dollar Exchange Rate Volatility“. University of the Western Cape, 2016. http://hdl.handle.net/11394/5738.
Der volle Inhalt der QuelleAnnotation:
Magister Commercii - MCom (Economics)Increased exchange rate volatility is an impediment to the health of the economy of a country. Following the 1995 policy shift made by the South African Reserve Bank, from a fixed exchange rate regime to a free floating exchange rate regime; the rand/dollar exchange rate became volatile. The aim of the study was to investigate the forces that lead the exchange rate volatility. In more details, the study looked at the relationship between the rand/dollar exchange rate and its determinants. In terms of the methodology, a Structural Vector Autoregressive (SVAR) model was used to analyse the relationship between the rand/dollar exchange rate and its determinants. In the short run, the impulse response function results showed that there were no strong bidirectional relationships between the rand/dollar and its determinants between 1995 and 2014. The only significant relationship, in the short run, was found to be between the exchange rate and nominal variables. Another significant impact was that of the exchange rate on the 10-year bond spread. The long-run test results suggested that there is a unilateral relationship between the rand/dollar exchange rate and the 10-year bond spread. The long-run tests results indicated that the rand/dollar exchange rate is indeed an �equity� currency, and is mostly driven by changes in the financial variables.
3
Ojebiyi, Ademola, und Wilson David Olugbenga. „Exchange rate volatility : an analysis of the relationship between the Nigerian naira, oil prices, and US dollar“. Thesis, Högskolan på Gotland, Institutionen för humaniora och samhällsvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hgo:diva-912.
Der volle Inhalt der QuelleAnnotation:
This study seeks to assess the correlation which exists between exchange rate of Nigerian naira and Unites States dollar and oil price on the basis of monthly data from 1999-2009. The research employ the fundamental variables which were assumed to be the monthly spot crude oil price, monthly exchange rate of Nigeria naira and monthly exchange rate of United States dollar. The empirical result adopted the ordinary least square using regression analysis and also the correlation model which shows that there is a weak/negative relationship between exchange rate and oil price as there are other factors that brings about changes in oil price other than the exchange rate. The activities of cartel pricing policy and oil speculators too have come to greatly affect the price of crude oil, and it will be interesting to examine the impact speculators have on the change in price of crude oil against the normal drivers of crude oil price.
4
Ben, Romdhane Hajri Aymen. „Fondamentaux macroéconomiques, flux d'ordre et dynamique du taux de change : cas de l'Euro-Dollar“. Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0353.
Der volle Inhalt der QuelleAnnotation:
Cette thèse s’inscrit dans la ligne directrice des travaux cherchant à discuter/expliquer les déterminants des taux de change dans un contexte de régime flexible. En particulier, elle se focalise sur l’examen de la dynamique du taux de change Euro-Dollar Américain en se référant en particulier aux fondamentaux et aux flux d’ordre, en lien avec les approches monétaristes et microstructurelles. Deux aspects du comportement du taux de change sont explorés : sa dynamique et sa volatilité. A ces fins, cette étude accorde une importance particulière au fait que le nouveau concept à savoir le flux d’ordre est une approximation fiable de fondamentaux macroéconomiques inobservables et/ou non quantifiables. Les résultats mettent en évidence que la dépréciation initiale de l’Euro face au Dollar découle principalement d’une forte expansion monétaire en Europe et d’une sortie massive de capitaux vers les Etats-Unis. Par ailleurs, cette étude montre que les instabilités des modèles monétaires détectées empiriquement sont le résultat d’une spécification inappropriée des déterminants du taux de change et que le niveau de stabilité de la relation de long terme va de pair avec le degré de désagrégation des flux d’ordre. Quant à l’étude des déterminants macroéconomiques de la volatilité du taux de change, cette thèse a revisité, sur un plan théorique, l’approche GARCH-MIDAS de Engle, GhysEls et Sohn (2006,2013). Ensuite, sur un plan empirique, cette étude a comparé les estimations et les prévisions fournies par les deux approches (classique et renforcée) où il a mis en avant la supériorité, en termes de qualité explicative prévisionnelle, du GARCH-MIDAS augmenté par une marche aléatoireThis thesis is part of the guideline of works seeking to discuss / explain the determinants of exchange rates in a flexible regime context. In particular, it focuses on examining the behavior of the Euro-US dollar exchange rate by referring in particular to fundamentals and order flows, in connection with monetarist and microstructural approaches. Two aspects of exchange rate behavior are explored: its dynamics and volatility. For these purposes, this study places particular emphasis on the fact that the new concept of order flow is a reliable approximation of unobservable and / or unquantifiable macroeconomic fundamentals. The results show that the initial depreciation of the Euro against the US Dollar stems mainly from a strong monetary expansion in Europe and a massive capital outflow to the United States. Moreover, this study shows that the instabilities of the empirically detected monetary models are the result of an inappropriate specification of the determinants of the exchange rate and that the level of stability of the long-term relationship goes hand in hand with the degree of disaggregation of the flows. order. As for the study of the macroeconomic determinants of exchange rate volatility, this thesis revisits, on a theoretical level, the GARCH-MIDAS approach of Engle, GhysEls and Sohn (2006,2013). Then, on an empirical level, this study compared the estimates and forecasts provided by the two approaches (classical and reinforced), where it highlighted the superiority, in terms of forecast quality, of GARCH-MIDAS augmented by a random walk
5
Custódio, Raquel Inês Martins. „O impacto das notações soberanas na taxa de câmbio euro-dólar“. Master's thesis, Instituto Superior de Economia e Gestão, 2013. http://hdl.handle.net/10400.5/11154.
Der volle Inhalt der QuelleAnnotation:
Mestrado em Economia Monetária e FinanceiraO objetivo do trabalho proposto é analisar o impacto das alterações de notação soberana atribuídas pelas agências de rating Moody's, Standard and Poors e Fitch na volatilidade do retorno das taxas de câmbio Euro-Dólar. A volatilidade foi definida através de especificações EGARCH e, com o intuito de adicionar uma ponderação a determinadas alterações de notação, foi construída uma variável específica - variável ponderada. Este estudo analisa o período que decorreu entre 1999:1 e 2013:5 e concluiu-se que os choques negativos têm um impacto superior face aos choques positivos, que o outlook tem um impacto superior em relação ao upgrade ou downgrade e a Standard and Poors é a agência que apresenta maior impacto na volatilidade do retorno da taxa de câmbio.
The main purpose of this study is to measure the impact of sovereign credit rating announcements from Moody's, Standard and Poors and Fitch on Euro-Dollar exchange rate return. The volatility was defined with EGARCH specifications and in order to add more weight to certainty sovereign rating changes, a specific variable was constructed. This study covers the period from 1999:1 until 2013:5 and we find that negative announcements have a bigger impact than the positive, outlook have bigger impact than upgrade or downgrade and, Standard and Poors is the agency with the highest impact on the exchange rate return.
6
Maitra, Biswajit. „Excess variability of rupee/dollar exchange rate : an econometric and time series analysis (1975-2003)“. Thesis, University of North Bengal, 2008. http://hdl.handle.net/123456789/1274.
Der volle Inhalt der Quelle
7
Gordon, Ross Patrick. „The effect of strike action on the value and volatility of the South African Rand“. Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1020018.
Der volle Inhalt der QuelleAnnotation:
This study analyses whether the advent of strike action has an effect on the value and volatility of the South African Rand compared with the US Dollar. The literature suggests that strike action can have a significant effect on the exchange rate in terms of either value or volatility, and consequences can result that cause inefficiencies in the economy; inhibiting employment and economic growth. Strike action has become common place in South Africa, with 2012 alone recording 99 strikes, 45 of which were “wildcat” or unprotected strikes. This study uses GARCH and Intervention Analyses to determine what the resulting effects of the strikes might be on the exchange rate. The analysis used ZAR/USD exchange rate data for the period January 2000 to October 2013, and covered 72 of the most significant strikes in terms of lost man-days. The results are mixed, suggesting that the effects of strikes do not always conform to expectations (increased volatility and a depreciation in the Rand), and that outside factors affecting the global economy may have a more significant effect on the exchange rate than strikes on their own.
8
Filippova, Daria. „Effect of foreign exchange interventions on volatility of dollar/yen exchange rate“. Master's thesis, 2017. http://www.nusl.cz/ntk/nusl-357617.
Der volle Inhalt der QuelleAnnotation:
Japanese monetary authorities used to employ various intervention techniques to adjust the level of the dollar/yen exchange rate and reduce its volatility. Application of the GARCH-in- mean model for estimation of the effect of these operations demonstrates that depreciating interventions reduced volatility effectively from 1995 until 2002. Frequent interventions of the small scale had a tendency to increase volatility during period 1991-1995. Foreign exchange interventions conducted by US Fed have increasing, means negative, effect, on the conditional variance. Frequent interventions of the great scale do not affect the volatility; it is determined mostly by the persistent level of the conditional variance from the latter periods. Recent interventions conducted by the Bank of Japan after the financial crisis do not show any considerable effect on both the volatility and the level of the exchange rate.
9
Liao, Chia-chen, und 廖家甄. „The Impact of Foreign Investment Opening Policy on Volatility of NT dollar Exchange Rate“. Thesis, 1997. http://ndltd.ncl.edu.tw/handle/03197521011735950177.
Der volle Inhalt der QuelleAnnotation:
碩士國立中興大學
企業管理學系
85
The impact of foreign investment opening policy on the volatility of NTdollar excange rate has been seriously debated recently. This paper uesesintervention model to investigate how the opening policy will affect exchangerate of NT to $US,Pounds, Yen,Mark and H.K. dollar. The conclusion are as follows: 1.Foreign investment holding ratio has influence on Yen with 5-period lagged effect. 2.Different opening periods have different long-term and short-term effect on exchange rate. 3.Outliers do exist. 4.Foreign investment opening policy do not lead to more volatile foreign exchang rate.
10
Lee, Meng-che, und 李孟哲. „A RESEARCH ON THE INTERACTION BETWEEN US/ EURO DOLLAR EXCHANGE RATE VOLATILITY AND TAIWAN WEIGHTED STOCK INDEX VOLATILITY“. Thesis, 2006. http://ndltd.ncl.edu.tw/handle/64551130426832559206.
Der volle Inhalt der QuelleAnnotation:
碩士南華大學
財務管理研究所
94
Previous researches have been mostly focus on analyzing the effect by single exchange rate. However, as the European Union emerged, Euro Dollars has become a significant currency of the world. The variation between the exchange rate of Euro Dollars and US Dallars more and more influences the international market and stock price every day. This research uses the direct rate of US Dollars to Euro Dollars and the weighted stock index in Taiwan from January 1st, 2002 to June 30th, 2005 to analyze the interaction between US/EURO Dollar exchange rate volatility and Taiwan weighted stock index volatility. The bivariate EGARCH models are employed to investigate the interaction between US/Euro Dollar exchange rate volatility and Taiwan weighted stock index volatility and the effect of stock returns and volatility spillovers as well. The results are as followed： The EGARCH mean equation shows that Taiwan s stock market return is positive and is affected by itself and earlier stage of the variation of the exchange rate between US Dollars to Euro Dollars. The variation of the exchange rate of US Dollars to Euro Dollars is positive affected by itself and Taiwan s stock market return at earlier stage. The EGARCH variance equation shows that the variation of the Taiwan s stock market return is positive which is affected by earlier stage the variation of the Taiwan s stock market return and earlier stage the variation of the exchange rate of US Dollars to Euro Dollar. The variation of the exchange rate of US Dollars to Euro Dollars is positive affected by itself and Taiwan s stock market return at earlier stage. Taiwan s stock market return and the variation of the exchange rate between US Dollars to Euro Dollar show a significant GARCH effect, and the volatility is affected by the variation of the stock price and the variation of the exchange rate at earlier stage.
Bücher zum Thema "Rupee-Dollar exchange rate volatility"
1
Reinhart, Carmen M. What hurts most?: G-3 exchange rate or interest rate volatility. Cambridge, MA: National Bureau of Economic Research, 2001.
Den vollen Inhalt der Quelle finden
2
Hwan, Yoo Tae, Choi Yoon Jung und Taeoe Kyŏngje Chŏngchʻaek Yŏnʾguwŏn (Korea), Hrsg. Exchange rate system in India: Recent reforms, central bank policies and fundamental determinants of the rupee-dollar rates. Seoul, Korea: Korea Institute for International Economic Policy, 2005.
Den vollen Inhalt der Quelle finden
3
Andersen, Torben G. DM-dollar volatility: Intraday activity patterns, macroeconomic announcements, and longer run dependencies. Cambridge, MA: National Bureau of Economic Research, 1996.
Den vollen Inhalt der Quelle finden
4
Maveé, Nasha, Axel Schimmelpfennig und Roberto Perrelli. Surprise, Surprise: What Drives the Rand / U. S. Dollar Exchange Rate Volatility? International Monetary Fund, 2016.
Den vollen Inhalt der Quelle finden
5
Maveé, Nasha, Axel Schimmelpfennig und Roberto Perrelli. Surprise, Surprise: What Drives the Rand / U. S. Dollar Exchange Rate Volatility? International Monetary Fund, 2016.
Den vollen Inhalt der Quelle finden
6
Maveé, Nasha, Axel Schimmelpfennig und Roberto Perrelli. Surprise, Surprise: What Drives the Rand / U. S. Dollar Exchange Rate Volatility? International Monetary Fund, 2016.
Den vollen Inhalt der Quelle finden
7
Bao, Yun, Carl Chiarella und Boda Kang. Particle Filters for Markov-Switching Stochastic Volatility Models. Herausgegeben von Shu-Heng Chen, Mak Kaboudan und Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.9.
Der volle Inhalt der QuelleAnnotation:
This chapter proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain. It proposes an ongoing updated Dirichlet distribution to estimate the transition probabilities of the Markov chain in the auxiliary particle filter. A simulation-based algorithm is presented for the method that demonstrates the ability to estimate a class of models in which the probability that the system state transits from one regime to a different regime is relatively high. The methodology is implemented in order to analyze a real-time series, namely, the foreign exchange rate between the Australian dollar and the South Korean won.
8
Gooch, Thomas John. Volatility and predictability of exchange rates in an equilibrium model: A case study of the deutsche mark/U.S. dollar and the Canadian dollar/U.S. dollar. 1995.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Rupee-Dollar exchange rate volatility"
1
Ndou, Eliphas, Nombulelo Gumata und Mthuli Ncube. „Does the Rand Per US Dollar Exchange Rate Volatility Impact on Net Asset Purchases by Non-residents?“ In Global Economic Uncertainties and Exchange Rate Shocks, 359–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62280-4_19.
Der volle Inhalt der Quelle
2
Sen, Saurabh, und Ruchi L. Sen. „An Empirical Analysis of FII Movement and Currency Value in India“. In Strategic Infrastructure Development for Economic Growth and Social Change, 207–17. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7470-7.ch014.
Der volle Inhalt der QuelleAnnotation:
India opened its stock market to foreign investors in September 1992 and has received portfolio investment from foreigners in the form of foreign institutional investment in equities and other markets including derivatives. It has emerged as one of the most influential groups to play a critical role in the overall performance of the Indian economy. The liberalization of FII flows into the Indian capital market since 1993 has had a significant impact on the economy. With increased volatility in exchange rate and to mitigate the risk arising out of excess volatility, currency futures were introduced in India in 2008, which is considered a second important structural change. This chapter examines the impact of the Foreign Institutional Investors (FIIs) on the exchange rate and analyzes the relationship between FII and Indian Rupee-US Dollar exchange rates.
3
Abutaleb, Ahmed, und Michael Papaioannou. „Malliavin Calculus for the Estimation of the U.S. Dollar/Euro Exchange Rate When the Volatility is Stochastic“. In Global Information Technology and Competitive Financial Alliances, 71–101. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-881-9.ch005.
Der volle Inhalt der QuelleAnnotation:
The tendency of exchange rates to fluctuate markedly and regularly is often referred as currency market volatility. The extent of currency market volatility is a major element of market risk. For financial transactions, volatility represents both costs and profit opportunities. Increased currency market volatility implies higher currency option premia and, therefore, higher hedging costs for investors and importers/exporters. However, for banks and other investment houses dealing in options, an increase in option prices may contribute to higher profits. It has been well established that the volatility of exchange rates changes with time. In recent years, various stochastic volatility models have been proposed in the literature that try to capture the exchange-rate volatility dynamics. In turn, several methods have been developed to estimate the parameters of such stochastic volatility models, with varying results. In this chapter, we propose another method for the estimation of the parameters of an exchange rate function when the volatility follows a stochastic process. Stochastic volatility is represented by a geometric Brownian motion. Using Malliavin calculus, we are able to find an explicit expression for the likelihood function of the observations. Numerical integration methods (Monte-Carlo simulations) and numerical optimization methods (generic algorithms) enable us to find an estimate for the unknown parameters and the volatility. This estimation method is then applied to the U.S. dollar/euro exchange rate. Specifically, first we formulate a U.S. dollar/euro exchange rate equation with a stochastic volatility model. We assume that the observed U.S. dollar/euro exchange rate follows a stochastic differential equation with random volatility, while the unobserved volatility follows a different stochastic differential equation. Then, we obtain the likelihood function of the observations by applying Malliavin calculus. The estimation of the unknown parameters is achieved through the maximization of the likelihood function. Using weekly U.S. dollar/euro exchange rates for the period April 28, 2000, to March 26, 2001, we obtain estimates of the parameters of the U.S. dollar/euro exchange rate function (i.e., the constant of the drift) and the assumed stochastic volatility model (i.e., the constants of the diffusion process). Application of the estimated model to out-of-sample data for the U.S. dollar/euro exchange rate shows a significantly high accuracy of the proposed method, as indicated by the very low root mean square error for the estimated exchange rate. This method can also be applied to other models of financial variables that follow similar processes.
4
Andersen, Torben G., Tim Bollerslev, Francis X. Diebold, und Paul Labys. „The Distribution of Realized Exchange Rate Volatility“. In Stochastic Volatility, 451–79. Oxford University PressOxford, 2005. http://dx.doi.org/10.1093/oso/9780199257195.003.0016.
Der volle Inhalt der QuelleAnnotation:
Abstract Using high-frequency data on deutschmark and yen returns against the dollar, we construct model-free estimates of daily exchange rate volatility and correlation that cover an entire decade. Our estimates, termed realized volatilities and correlations, are not only model-free, but also approximately free of measurement error under general conditions, which we discuss in detail. Hence, for practical purposes, we may treat the exchange rate volatilities and correlations as observed rather than latent. We do so, and we characterize their joint distribution, both unconditionally and conditionally. Noteworthy results include a simple normality-inducing volatility transformation, high contemporaneous correlation across volatilities, high correlation between correlation and volatilities, pronounced and persistent dynamics in volatilities and correlations, evidence of long-memory dynamics in volatilities and correlations, and remarkably precise scaling laws under temporal aggregation.
5
Diebold, Francis X., und Marc Nerlove. „The Dynamics of Exchange Rate Volatility: A Multivariate Latent Factor Arch Model“. In Stochastic Volatility, 130–55. Oxford University PressOxford, 2005. http://dx.doi.org/10.1093/oso/9780199257195.003.0006.
Der volle Inhalt der QuelleAnnotation:
Abstract We study temporal volatility patterns in seven nominal dollar spot exchange rates, all of which display strong evidence of autoregressive conditional heteroskedasticity (ARCH). We first formulate and estimate univariate models, the results of which are subsequently used to guide specification of a multivariate model. The key element of our multivariate approach is exploitation of factor structure, which facilitates tractable estimation via a substantial reduction in the number of parameters to be estimated. Such a latent-variable model is shown to provide a good description of multivariate exchange rate movements: the ARCH effects capture volatility clustering, and the factor structure captures commonality in volatility movements across exchange rates. In this paper we specify and estimate a multivariate time-series model with an underlying latent variable whose innovations display autoregressive conditional heteroskedasticity (ARCH). Various aspects of this factor-analytic approach are sketched in Diebold (1986) and Diebold and Nerlove (1987); here we provide a more complete exposition, propose a new estimation procedure, and present a detailed substantive application to movements in seven major dollar spot exchange rates. To guide multivariate specification, we begin with a univariate analysis and relate the results to apparent random walk behaviour, leptokurtic unconditional distributions and convergence to normality under temporal aggregation.
6
Mary Bello, Kehinde, David Oluseun Olayungbo und Benjamin Ayodele Folorunso. „Exchange Rate Volatility and Macroeconomic Performance in Nigeria“. In Macroeconomic Analysis for Economic Growth. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.100444.
Der volle Inhalt der QuelleAnnotation:
The study examined the asymmetric relationship between exchange rate volatility and macroeconomic performance in Nigeria covering the period between 1986Q1 and 2019Q4. The Non-linear Generalised Autoregressive Distributive Conditional Heteroscedasticity (GARCH) model was employed. The study was motivated as a result of periodic increase in exchange rate of naira to a dollar and instability of macroeconomic variables in the economy. The presence of Autoregressive Distributive Conditional Heteroscedasticity (ARCH) effect established the use of non-linear GARCH models which showed that volatility was persistent over the period of study. Consequently, the result revealed that exchange rate volatility exhibited a positive relationship with trade balance, industrial output and inflation in the study period. Thus, good news prevailed more over bad news in the foreign exchange market. The study therefore recommended that monetary authorities in Nigeria should regulate exchange rate and macroeconomic variables in order to control the general price level in the economy.
7
Rivero, R., und G. David. „Modeling structural breakpoints in volatility of Philippine Peso-US Dollar currency exchange rate“. In Empowering Science and Mathematics for Global Competitiveness, 413–17. CRC Press, 2019. http://dx.doi.org/10.1201/9780429461903-59.
Der volle Inhalt der Quelle
8
Cornia, Giovanni Andrea. „Modelling the Open Economy, Devaluation, and the Exchange Rate in Developing Countries“. In The Macroeconomics of Developing Countries, 277–306. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198856672.003.0015.
Der volle Inhalt der QuelleAnnotation:
The chapter first examines the limitations of conventional open-economy macro models, such as the Mundell–Fleming model, when they are applied to developing countries. It discusses the Swan–Salter model and the three-sector dependent-economy model that better capture the reality of the external sector in poor countries. It then discusses the impact of devaluation under conditions of closed and open capital accounts and shows the limitation of a devaluation unaccompanied by structural measures in little diversified poor economies and in economies with large dollar liabilities. In this regard, it examines the results of the empirical literature on the contractionary or expansionary effect of devaluation in developing countries. Finally, it reviews the pros and cons of alternative exchange rate regimes, the impossible trinity theorem, and measures to control exchange rate volatility through capital controls.
9
Ndlovu, Thabani, und Delson Chikobvu. „Estimating Extreme Value at Risk Using Bayesian Markov Regime Switching GARCH-EVT Family Models“. In Cryptocurrencies - Financial Technologies of the Future [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1004124.
Der volle Inhalt der QuelleAnnotation:
In this study, the performance of the Bayesian Markov regime-switching GARCH-EVT in the estimation of extreme value at risk in the BitCoin/dollar (BTC/USD) and the South African Rand/dollar (ZAR/USD) exchange rates is investigated. The goal is to capture regime switches and extreme returns to exchange rates, all to explain and compare the riskiness of BitCoin and the Rand. The Markov chain Monte Carlo method is used to estimate parameters for the GARCH family models. Using the deviance information criterion, the two regime-switching GARCH models perform better than the single-regime GARCH model when modelling volatility of the two currencies’ returns. Based on the estimated value at risk figures, BitCoin is riskier than the Rand. At both 95% and 99% levels of significance, the results suggest that the MS(2)-gjrGARCH(1,1)-GEVD7 and MS(2)-sGARCH(1,1)-GPD7 are the best fitting models for both BTC/USD and ZAR/USD respectively, at both significance levels. The backtest confirms model adequacy. This information is useful to local and foreign currency traders and investors who need to fully appreciate the risk exposure when they convert their savings or investments to BitCoin instead of the South African currency, the Rand.
Konferenzberichte zum Thema "Rupee-Dollar exchange rate volatility"
1
Ma, Marggie, Jiangze Du und Kin Keung Lai. „Modeling Volatility of Exchange Rate of Chinese Yuan against US Dollar Based on GARCH Models“. In 2013 Sixth International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2013. http://dx.doi.org/10.1109/bife.2013.63.
Der volle Inhalt der Quelle
2
Czech, Katarzyna. „Is a Japanese yen a safe haven? Relationship between Japanese currency and financial market uncertainty“. In 3rd International Conference on Administrative & Financial Sciences. Cihan University - Erbil, 2021. http://dx.doi.org/10.24086/afs2020/paper.353.
Der volle Inhalt der QuelleAnnotation:
Japan's low-interest rates made the country's currency the primary funding currency in carry trade speculative strategies. Investors' activity in carry trade strategies has an enormous impact on the foreign exchange market volatility. A large inflow of capital to countries with higher interest rates contributes to their currency appreciation, and, in turn, a large outflow of capital from countries with a low-interest rate leads to a significant depreciation of their currency. However, in times of crisis and high uncertainty in the financial markets, investors massively withdraw from the carry trade. They sell financial assets purchased in a country with higher interest rates and then repay loans taken in a country with low-interest rates. A sudden increase in the supply of a country's currency with higher interest rates leads to its depreciation. On the other hand, the rise in demand for a country's currency with low-interest rates leads to its appreciation. The Japanese yen is one of the most popular funding currency in the carry trade and thus tends to appreciate during crisis periods. The paper aims to investigate the relationship between Japanese yen value and financial market uncertainty measured by the Volatility Index VIX and St. Louis FED Financial Stress Index. Based on the component generalized autoregressive conditional heteroscedasticity model CGARCH with asymmetric threshold term, it has been shown that the increase in financial markets uncertainty contributes to significant appreciation of the Japanese yen against the US dollar. It implies that the Japanese currency is an example of a safe-haven currency and can be applied to hedge financial stress for global equity investors.
Berichte der Organisationen zum Thema "Rupee-Dollar exchange rate volatility"
1
Parra-Polania, Julian A., Andrés Sánchez-Jabba und Miguel Sarmiento. Oral FX Interventions in Emerging Markets: the Colombian case. Banco de la República, Februar 2022. http://dx.doi.org/10.32468/be.1194.
Der volle Inhalt der QuelleAnnotation:
Do oral FX interventions (i.e. announcements made by central bank officials and economic authorities) influence the exchange rate behavior in emerging economies? Following an event study approach, we evaluate whether this type of interventions in the Colombian FX market have an impact on the level or volatility of the exchange rate (U.S Dollar / Colombian peso). We find there is no conclusive evidence of a statistically significant impact. This finding consistently arises across different subsamples and parameters. Robustness tests based on the exchange rate authority that makes the announcement or the mechanism used for actual interventions yield the same conclusion. We interpret these findings as possible evidence of the fact that higher levels of uncertainty (and hence lower credibility levels) or the predominance of global over domestic factors may reduce the effectiveness of oral interventions in emerging economies.
2
Rincón-Torres, Andrey Duván, Kimberly Rojas-Silva und Juan Manuel Julio-Román. The Interdependence of FX and Treasury Bonds Markets: The Case of Colombia. Banco de la República, September 2021. http://dx.doi.org/10.32468/be.1171.
Der volle Inhalt der QuelleAnnotation:
We study the interdependence of FX and Treasury Bonds (TES) markets in Colombia. To do this, we estimate a heteroskedasticity identified VAR model on the returns of the COP/USD exchange rate (TRM) and bond prices, as well as event-analysis models for return volatilities, number of quotes, quote volume, and bid/ask spreads. The data under analysis consists of 5-minute intraday bid/ask US dollar prices and bond quotes, for an assortment of bond species. For these species we also have the number of bid/ask quotes as well as their volume. We found, also, that the exchange rate conveys information to the TES market, but the opposite does not completely hold: A one percent COP depreciation leads to a persistent reduction of TES prices between 0.05% and 0.22%. However, a 1% TES price increase has a very small effect and not entirely significant on the exchange rate, i.e. a COP appreciation between 0.001% and 0.009%. Furthermore, TRM return volatility increases do not affect bond return volatility but its liquidity, i.e. the bid/ask quote number and volume. These results are coherent with the fact that the FX market more efficiently reflects the effect of shocks than the TES market, which may be due to its low liquidity and concentration on a specific habitat. These results have implications for the design of financial stability policies as well as for private portfolio design, rebalancing and hedging.