Um die anderen Arten von Veröffentlichungen zu diesem Thema anzuzeigen, folgen Sie diesem Link: Rupee-Dollar exchange rate volatility.
Zeitschriftenartikel zum Thema „Rupee-Dollar exchange rate volatility“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit Top-50 Zeitschriftenartikel für die Forschung 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.
Sehen Sie die Zeitschriftenartikel für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.
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.
11
Choi, Nam-Jin. „An Analysis Factors in Exchange Rate Volatility and Effects of Exchange Rate Volatility on the Real Economy“. Northeast Asia Economic Association Of Korea 34, Nr. 2 (31.08.2022): 71–99. http://dx.doi.org/10.52819/jnes.2022.34.2.71.
Der volle Inhalt der QuelleAnnotation:
This study empirically analyzes factors that can affect the volatility of the domestic won/dollar exchange rate, which advocates a small open economy, focusing on the United States, a representative key currency country. In addition, the effect of exchange rate volatility on the domestic real economy was analyzed. First, the EGARCH model is estimated in order to extract won/dollar exchange rate volatility. The results show that there is a volatility clustering phenomenon in the won/dollar exchange rate volatility. Next, factors of exchange rate volatility are estimated through the regression analysis. The estimated results indicate that variables of the US currency amount and Korea-US interest rate spread show significant amounts of coefficients, and each variable increase causes the increase of won/dollar exchange rate volatility. This explains that the expansionary monetary policy of the US implemented for domestic business recovery raises won/dollar exchange rate volatility through direct currency channel and portfolio channel. On the other hand, the growth rate of the US shows significant negative coefficient, and the increase of relevant variables leads to the reduction of won/dollar exchange rate volatility. In the results of estimating the SVAR model in order to check the effects of exchange rate volatility on real economy of Korea, the shock of exchange rate volatility increase reduces trades between Korea and the US while limiting the growth rate of Korea. Through the above analysis results, it is expected that unexpected monetary policy(austerity monetary policy) of major key currency countries including the US might expand the won/dollar exchange rate volatility, which could function as an element to restrict domestic real economy.
12
Kar, Rituparna, und Nityananda Sarkar. „Mean and volatility dynamics of Indian rupee/US dollar exchange rate series: an empirical investigation“. Asia-Pacific Financial Markets 13, Nr. 1 (27.02.2007): 41–69. http://dx.doi.org/10.1007/s10690-007-9034-0.
Der volle Inhalt der Quelle
13
KIANI, KHURSHID M. „FORECASTING FORWARD EXCHANGE RATE RISK PREMIUM IN SINGAPORE DOLLAR/US DOLLAR EXCHANGE RATE MARKET“. Singapore Economic Review 54, Nr. 02 (Juni 2009): 283–98. http://dx.doi.org/10.1142/s0217590809003288.
Der volle Inhalt der QuelleAnnotation:
In this research, monthly forward exchange rates are evaluated for possible existence of time varying risk premia in Singapore forward foreign exchange rates against US dollar. The time varying risk premia in Singapore dollar is modeled using non-Gaussian signal plus noise models that encompass non-normality and time varying volatility. The results from signal plus noise models show statistically significant evidence of time varying risk premium in Singapore forward exchange rates although we failed to reject the hypotheses of no risk premium in the series. The results from Gaussian versions of these models are not much different and are in line with Wolff (1987) who also used the same methodology in Gaussian settings. Our results show statistically significant evidence of volatility clustering in Singapore forward exchange rates. The results from Gaussian signal plus noise models also show statistically significant evidence of volatility clustering and non-normality in Singapore forward foreign exchange rates. Additional tests on the series show that exclusion of conditional heteroskedasticity from the signal plus noise models leads to false statistical inferences.
14
Pandey, Trilok Nath, Nrusingha Tripathy, Sarbeswar Hota und Bichitrananda Patra. „Empirical analysis of machine learning techniques for prediction of indian exchange rate“. Journal of Statistics & Management Systems 26, Nr. 1 (2023): 13–22. http://dx.doi.org/10.47974/jsms-943.
Der volle Inhalt der QuelleAnnotation:
Throughout the past few decades, there has been a dramatic surge in the currency market. The changes play an important role in balancing the market’s characteristics. As a result, accurate change price forecasting is essential to improve the success rate of many businesses and fund managers. Despite the fact that the market is renowned for its erratic behavior and volatility, there are organizations like agencies, banks, and others. In order to estimate the extraneous interchange rate of the dollar against the rupee with a high degree of accuracy, we used three distinct types of methodologies in this article. This research uses three different types of neural network models: ANNs (Artificial Neural Networks), LSTMs (Long Short-Term Memory Networks), and GRUs (Gated Recurring Units). The results depict that GRU’s model is outperforming the other two models.
15
Chukwu Agwu, Ejem, und Ogbonna Udochukwu Godfrey. „Modeling Volatility and Daily Exchange Rate Movement in Nigeria“. International Journal of Economics and Financial Research, Nr. 511 (25.11.2019): 264–75. http://dx.doi.org/10.32861/ijefr.511.264.275.
Der volle Inhalt der QuelleAnnotation:
This study modeled volatility and daily exchange rate movement in Nigeria with daily exchange rate between Nigeria Naira and US Dollar from January 2, 2001 to May 20, 2019 collected from the Central Bank of Nigeria (CBN). The results of the estimated models revealed that conditional variance (volatility) has positive and significant relationship with exchange rate returns between Nigeria Naira and US Dollars, which corroborates the theory that predicts positive relationship between return and volatility for risk averse investors. Also found that exchange rate volatility between Naira / US Dollar is persistent. It was also discovered that goods news produces more volatility than bad news of equal magnitude. The researchers therefore suggested that the Central Bank of Nigeria should always proffer timely intervention to reduce the volatility persistence. This will go a long way to counteract or moderate the excess volatility between Naira and US Dollar transactions.
16
Caporale, Guglielmo Maria, und Luis Gil-Alana. „Long Memory and Volatility Dynamics in the US Dollar Exchange Rate“. Multinational Finance Journal 16, Nr. 1/2 (01.06.2012): 105–36. http://dx.doi.org/10.17578/16-1/2-5.
Der volle Inhalt der Quelle
17
Rofi'i, Yulianto Umar. „Pengaruh Indeks Harga Konsumen, Jumlah Uang Beredar, Produk Domestik Bruto, Suku Bunga, dan Neraca Pembayaran Terhadap Nilai Tukar Rupiah“. Jurnal EMT KITA 7, Nr. 4 (10.10.2023): 1139–48. http://dx.doi.org/10.35870/emt.v7i4.1568.
Der volle Inhalt der QuelleAnnotation:
This study aims to analyze the relationship between various assumed economic variables and exchange rate fluctuations of the Rupee against the US dollar. The variables considered include the logarithmic difference of the consumer price index of Indonesia and the United States, the logarithmic difference of the money supply of Indonesia and the United States, the logarithmic difference of the gross domestic product of Indonesia and the United States, the logarithmic difference of the Indonesian and US interest rates and the logarithm of the Indonesian balance of payments. The data analysis results show that only the difference between the logarithm of the gross domestic product of Indonesia and the US and the difference between the logarithm of the interest rates of Indonesia and the US have a significant influence on the change in the exchange rate of Indonesia rupiah. The R-squared of 61.6% shows that the observed variation in the dependent variable can be explained by the independent variables, while the remaining 38.4% is influenced by other factors. The conclusion of this study is that the difference in change in the logarithm of gross domestic product and the difference in the logarithm of interest rate have a significant impact on the volatility of the rupee exchange rate.
18
Ali, Nazar, und Ashok Mittal. „Nexus between Exchange Rate Volatility and Oil Price Fluctuations: Evidence from India“. Saudi Journal of Economics and Finance 7, Nr. 03 (15.03.2023): 135–46. http://dx.doi.org/10.36348/sjef.2023.v07i03.003.
Der volle Inhalt der QuelleAnnotation:
The price of crude oil has fluctuated in India over the past few decades which has drawn significant attention because of it impact on all economic sectors. The present study aims to identify how oil price volatility affects the real exchange rate in India from 1st July 2009 to 2nd January 2020. For short-run and long-run analysis, various econometric methods have been applied, including Granger Causality, ARDL Bound test, FEVD, and IRF. The study divided the entire sample into sub-samples based on Breakpoint analysis and then performed the ARDL Bound testing procedure in each sub-sample. Causality results revealed that most samples exhibited strong unidirectional causality from oil prices to exchange rates. However, the long-run and short-run results from the ARDL model failed to detect any cointegration among the underlying variables for the entire sample. The calculated F-statistics is 4.35, which is less than the lower and upper critical bound values provided by Pesaran, Shin, and Smith (2001). The GIRF has been used to calculate the dynamic marginal effect of a one-standard-deviation shock in oil prices on the current and future values of the Rupee-Dollar exchange rate. The exchange rate fell in the first three samples due to one standard deviation shock in oil prices. However, the contribution of oil prices to the exchange rate is positive in the fourth sample period.
19
Laopodis, Nikiforos T. „U.S. Dollar Asymmetry And Exchange Rate Volatility“. Journal of Applied Business Research (JABR) 13, Nr. 2 (07.09.2011): 1. http://dx.doi.org/10.19030/jabr.v13i2.5756.
Der volle Inhalt der QuelleAnnotation:
<span>he paper explores the stochastic behavior of six exchange rates three EMS and three non-EMS during the U.S. dollar appreciation (before 1985) and depreciation (after 1985) using Exponential GARCH-M model. The results showed that high volatility in all rates was present before 1985, increased dramatically thereafter, and decreased later for the non-EMS rates. In general, U.S. dollar depreciations increased the volatility more than appreciations did for the French franc, the Italian lira, and the German mark.</span>
20
Shahani, Rakesh, und Prateek Tomar. „An Empirical Investigation of the Volatility Spill-over and Asymmetries between Nifty Index and Rupee-Dollar Exchange Rate“. Journal of Business Thought 11, Nr. 1 (02.11.2020): 41–50. http://dx.doi.org/10.18311/jbt/2020/25712.
Der volle Inhalt der Quelle
21
Shahani, Rakesh, und Prateek Tomar. „An Empirical Investigation of the Volatility Spill-over and Asymmetries between Nifty Index and Rupee-Dollar Exchange Rate“. Journal of Business Thought 11, Nr. 1 (04.03.2017): 41–50. http://dx.doi.org/10.18311/jbt/20209/25712.
Der volle Inhalt der Quelle
22
Shin, Dong-Hoon, Seonhyeon Kim, Hojoon Kim und Daehwi Jung. „Psychological Barrier in Foreign Exchange Rate and Implied Volatility in Currency Exchange Option“. Journal of Derivatives and Quantitative Studies 22, Nr. 2 (31.05.2014): 309–29. http://dx.doi.org/10.1108/jdqs-02-2014-b0006.
Der volle Inhalt der QuelleAnnotation:
In this paper, we examine the existence of the psychological barriers in three foreign exchange rate, won/dollar, euro/dollar, yen/dollar, and test that the psychological barriers effect to the implied volatilities of the FX options. For each exchange rate, the existence and spots of the psychological barriers are estimated from roughly 10 years data for each currency rate, and GARCH (1, 1) model was applied to observe the momentum effect about the mean and variance of the conditional returns, and the implied volatility of the FX-options for each currency rate near the psychological barriers. Since this effect is more clearly observed on the implied volatility data, this fact supports that psychological barriers affects to the price of the FX-options.
23
Tsuji, Chikashi. „Spillovers and Dynamic Correlations between REITs, Exchange Rates, and Equities in Japan“. Accounting and Finance Research 10, Nr. 4 (26.09.2021): 13. http://dx.doi.org/10.5430/afr.v10n4p13.
Der volle Inhalt der QuelleAnnotation:
This paper investigates return transmission, volatility spillovers, and dynamic correlations between the Tokyo Stock Exchange (TSE) Real Estate Investment Trust (REIT) index, the Nikkei 225 index, and the yen/dollar exchange rate. As a result, we find many new findings and these all show our significant contributions as follows. First, there is return transmission from the Nikkei 225 to the TSE REIT index. Second, there is bidirectional return transmission between the Nikkei 225 and the yen/dollar exchange rate. Third, there are bidirectional volatility spillovers between the Nikkei 225 and the TSE REIT index. Fourth, there are volatility spillovers from the Nikkei 225 to the yen/dollar exchange rate. Fifth, dynamic conditional correlations (DCCs) between TSE REIT returns and Nikkei 225 returns are not low. Moreover, DCCs between Nikkei 225 returns and yen/dollar exchange rate changes are not high. Furthermore, DCCs between TSE REIT returns and yen/dollar exchange rate changes are quite low. These our new findings shall be useful for not only deepening our understanding of financial markets but also our related future research.
24
Gupta, Sanjeev, und Sachin Kashyap. „Modelling volatility and forecasting of exchange rate of British pound sterling and Indian rupee“. Journal of Modelling in Management 11, Nr. 2 (09.05.2016): 389–404. http://dx.doi.org/10.1108/jm2-04-2014-0029.
Der volle Inhalt der QuelleAnnotation:
Purpose The paper aims to analyse the extent of volatility and generating forecasts of exchange rates of British pound and Indian rupees in US terms. Design/methodology/approach This study applies different combinations of GARCH and EGARCH models suggested in the Econometric literature to capture the extent of volatility. The forecast of exchange rates of British Pound and Indian Rupees in US terms are generated applying artificial neural network (ANN) technique using different combination of networks with hyperbolic tangent function at hidden and output stage of the model. Findings The presence of volatility depicts that there is noise and chaos in the forex market. Prediction of exchange rate of the respective currencies underscores that exchange rates will increase marginally in near future. Practical Implications The results proposed in this study will be benchmark for the hedgers, investors, bankers, practitioners and economists to foresee the exchange rate in the presence of volatility and design policies accordingly. Originality/value In literature, no study has applied ANN for forecasting exchange rate after measuring the extent of volatility. The present study is a unique contribution in the existing pool of literature to forecasts the concerned variable(s) after ascertaining the noise and chaos in the data by applying GARCH family models.
25
Wei, Ching-Chun. „Empirical Analysis of “Volatilitysurprise” between Dollar Exchange Rate and CRB Commodity Future Markets“. International Journal of Economics and Finance 8, Nr. 9 (24.08.2016): 117. http://dx.doi.org/10.5539/ijef.v8n9p117.
Der volle Inhalt der QuelleAnnotation:
This paper used the five multivariate GARCH models (including BEKK, CCC, DCC, VARMA-CCC and VARMA-DCC) to analyze the mean and volatility interaction of volatility surprise between US dollar exchange and CRB future index (including agricultural, energy, commodity and precious metal equity index). The empirical findings exhibit that significant own short and long-term persistence effects and the cross-markets volatility surprise spillover short and long-term persistence effects between dollar exchange rate and CRB commodity future equity index markets in five multivariate GARCH models. Besides that, the residual diagnostic test indicated that VARMA-DCC models is the best suitable model to modeling the dollar exchange rate with CRB commodity equity index.
26
Suhendra, Indra, Cep Jandi Anwar, Navik Istikomah, Eka Purwanda und Lilis Nur Kholishoh. „The Short-Run and Long-Run Effects of Central Bank Rate on Exchange Rate Volatility in Indonesia“. International Journal of Innovative Research and Scientific Studies 5, Nr. 4 (28.10.2022): 343–53. http://dx.doi.org/10.53894/ijirss.v5i4.851.
Der volle Inhalt der QuelleAnnotation:
This research measures the short and long-run effects of central bank policy rate on the volatility of the exchange rate in Indonesia using the quarterly data from Q1 1992 to Q4 2019. The process involves applying an Autoregressive Distribution Lag estimation to investigate the effects of the variables. The exchange rate volatilities include Indonesia Rupiah to US Dollar (IDR-USD), Indonesia Rupiah to Singapore Dollar (IDR-SGD), Indonesia Rupiah to Australia Dollar (IDR-AUD), Indonesia Rupiah to British Pound Sterling (IDR-GBP), and Indonesia Rupiah to Euro (IDR-EURO). Several results were obtained and the first to show the adjustment time for exchange rate volatility to achieve long-run equilibrium was 1.77 quarters to 2.26 quarters using the ARDL estimation. Secondly, a decrease in the central bank rate was found to significantly reduce the exchange rate volatility in the short run and long run. These results are robust since Full Modified Ordinary Least Square (FMOLS) estimation was applied for all five models. Furthermore, it was found that in the long run, the central bank policy rate had a significant positive effect on the volatility of the Indonesia Rupiah against five foreign exchange rates. Therefore, it was suggested that the policymakers need to keep the interest rate of the central bank low and stable to ensure the Rupiah exchange rate stability.
27
Siami-Namini, Sima. „Volatility Transmission Among Oil Price, Exchange Rate and Agricultural Commodities Prices“. Applied Economics and Finance 6, Nr. 4 (10.06.2019): 41. http://dx.doi.org/10.11114/aef.v6i4.4322.
Der volle Inhalt der QuelleAnnotation:
The aim of this article is to examine the interdependence relationship among the volatilities of crude oil price, U.S. dollar exchange rate, and a set of agricultural commodities prices. An autoregressive (AR) with an exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model or AR-EGARCH process and vector error correction model (VECM) approach was used on monthly data spanning from Jan 1986 to Dec 2005 as the pre-crisis period and from Jan 2006 to Nov 2015 as the post-crisis period. The results show that volatility in the agricultural commodity returns for most cases are affected by the volatility of the crude oil returns in the post-crisis period. Also, the volatility of the U.S. dollar exchange rate highly affects the agricultural commodities returns in the pre-crisis than the post-crisis periods. Furthermore, crude oil returns volatility does affect the U.S. dollar exchange rate volatility in the post-crisis period, which in turn affects the volatility of the agricultural commodities returns through changes in prices. The results of impulse response function (IRFs) are significant for most agricultural commodities volatility in the post-crisis period than the pre-crisis period.
28
WILSON, PETER, und HENRY SHANG REN NG. „MANAGING EXCHANGE RATE VOLATILITY: A COMPARATIVE COUNTERFACTUAL ANALYSIS OF SINGAPORE, 1994–2003“. Singapore Economic Review 54, Nr. 04 (Dezember 2009): 543–68. http://dx.doi.org/10.1142/s0217590809003525.
Der volle Inhalt der QuelleAnnotation:
This paper looks at how Singapore's exchange rate regime has coped with exchange rate volatility, by comparing the performance of Singapore's actual regime in minimizing the volatility of the nominal effective exchange rate (NEER) and the bilateral rate against the US dollar with some counterfactual regimes and the corresponding performance of eight other East Asian countries. In contrast to previous counterfactual exercises, we apply a more disaggregated methodology for the trade weights, a larger number of trade partners and ARCH/GARCH techniques to capture the time-varying characteristics of volatility. Our findings confirm that Singapore's managed floating exchange rate system has delivered relatively low currency volatility. Although there are gains in volatility reduction for all countries in the sample from the adoption of either a unilateral or a common basket peg, particularly post-Asian crisis, these gains are relatively low for Singapore, largely because of low actual volatility. There are additional gains for non-dollar peggers from stabilizing intra-east Asian exchange rates against the dollar if they were to adopt a basket peg, especially post-crisis, but the gains for Singapore are again relatively modest. Finally, the conclusion from previous counterfactual studies that there is little difference between a unilateral basket peg and a common basket peg in terms of stability reduction is confirmed.
29
Astuty, Pudji. „DETERMINANTS OF THE VOLATILITY OF THE RUPIAH EXCHANGE RATE AGAINST THE DOLLARSAMERICA IN THE MIDDLE OF THE COVID-19“. JABE (Journal of Applied Business and Economic) 9, Nr. 1 (25.12.2022): 25. http://dx.doi.org/10.30998/jabe.v9i1.14438.
Der volle Inhalt der QuelleAnnotation:
<em>Liberalization, regionalization, and globalization have made the inflow of goods, services, and capital easier to break through the regional boundaries of a country, and the barriers have continued to decrease for global trade or business. cross border. The consequences of liberalization, regionalization, and globalization demand the existence of a country's economic transparency which can cause concerns for every country, including Indonesia, especially regarding the stability of its currency exchange rate. During the Covid-19 Pandemic, at the end of March 2020, the Rupiah exchange rate against the US Dollar weakened by 18% compared to its position in December 2019 (before the Covid-19 Pandemic). The exchange rate of the Rupiah against the US Dollar is continuously fluctuating and tends to weaken. The volatility of the Rupiah exchange rate against the US Dollar and its large volatility over a long period can disrupt the economy as a whole. The purpose of this research is to analyze the factors that influence the volatility of the Rupiah exchange rate to the US Dollar. This research uses inferior information on duration (time series) from 2009 to 2020 (information per year). The results of the research can be concluded that the JCI, Trade Balance, Foreign Loans, Inflation, and Interest Rates have an important influence on the volatility of the Rupiah exchange rate to the US Dollar either simultaneously or partially</em>
30
Panda, Ajaya Kumar, Swagatika Nanda, Vipul Kumar Singh und Satish Kumar. „Evidence of leverage effects and volatility spillover among exchange rates of selected emerging and growth leading economies“. Journal of Financial Economic Policy 11, Nr. 2 (07.05.2019): 174–92. http://dx.doi.org/10.1108/jfep-03-2018-0042.
Der volle Inhalt der QuelleAnnotation:
Purpose The purpose of this study is to examine the evidences of leverage effects on the conditional volatility of exchange rates because of asymmetric innovations and its spillover effects among the exchange rates of selected emerging and growth-leading economies. Design/methodology/approach The empirical analysis uses the sign bias test and asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) models to capture the leverage effects on conditional volatility of exchange rates and also uses multivariate GARCH (MGARCH) model to address volatility spillovers among the studied exchange rates. Findings The study finds substantial impact of asymmetric innovations (news) on the conditional volatility of exchange rates, where Russian Ruble is showing significant leverage effect followed by Indian Rupee. The exchange rates depict significant mean spillover effects, where Rupee, Peso and Ruble are strongly connected; Real, Rupiah and Lira are moderately connected; and Yuan is the least connected exchange rate within the sample. The study also finds the assimilation of information in foreign exchanges and increased spillover effects in the post 2008 periods. Practical implications The results probably have the implications for international investment and asset management. Portfolio managers could use this research to optimize their international portfolio. Policymakers such as central banks may find the study useful to monitor and design interventions strategies in foreign exchange markets keeping an eye on the nature of movements among these exchange rates. Originality/value This is one of the few empirical research studies that aim to explore the leverage effects on exchange rates and their volatility spillovers among seven emerging and growth-leading economies using advanced econometric methodologies.
31
Raizada, Gaurav, und SVD Nageswara Rao. „Interaction of Onshore and Offshore Rupee Markets“. Journal of Prediction Markets 16, Nr. 3 (17.02.2023): 17–40. http://dx.doi.org/10.5750/jpm.v16i3.1950.
Der volle Inhalt der QuelleAnnotation:
The study investigates the trading in onshore and offshore Rupee Futures trading on exchanges focusing on the deviations from the equilibrium. Both onshore and offshore rates fundamentally represent the same economic asset and should have similar price dynamics; however, they deviate significantly. We model the interaction of the onshore-offshore rupee market using Continuous Futures Rupee Data. The differential in the prices of onshore and offshore Rupee Futures are analyzed with respect to the volatility and interest rates factoring in the capital and trading controls. An extended GARCH(1,1) with Relative Equity and Commodity Index along with VIX in the mean and conditional variance fit the differential of the onshore-offshore Rupee Futures. The understanding of the behavior of onshore-offshore markets is essential for Policymakers to adopt a successful exchange rate policy and traders and institutions to make informed decisions.
32
Kongwiriyapisal, Piyasiri. „Modeling Exchange Rate Volatility of ASEAN Member Countries“. International Journal of Applied Mathematics, Computational Science and Systems Engineering 5 (12.07.2023): 84–92. http://dx.doi.org/10.37394/232026.2023.5.8.
Der volle Inhalt der QuelleAnnotation:
This study investigates the volatility of exchange rates in nine selected ASEAN member countries, using five forms of the GARCH model. Daily data was sourced from the Bank of Thailand website database, as Baht per foreign currency, over the period from October 2, 2018 to October 7, 2022. This data included Malaysia Ringgit, Singapore Dollar, Brunei Darussalam Dollar, Philippines Peso, Indonesia Rupiah, Myanmar Kyat, Cambodia Riel, Laos Kip, and Vietnam Dong. According to the findings of this study, only eight of the exchange rates were suitable for analysis. In addition, the GARCH ( 1,1) , TGARCH ( 1,1) , and PGARCH ( 1,1) models were determined to be the most applicable, and leverage effects were observed in certain exchange rates. To mitigate the risk associated with trade and investment activities, investors should closely monitor news that is likely to affect the value of exchange rates. In order to design actions that promote exchange rate stability, government agents, on the other hand, must ensure they are current on such news.
33
Edwards, Sebastian. „Keynes and the dollar in 1933: the gold-buying program and exchange rate gyrations“. Financial History Review 24, Nr. 3 (10.11.2017): 209–38. http://dx.doi.org/10.1017/s096856501700018x.
Der volle Inhalt der QuelleAnnotation:
In December 1933, John Maynard Keyes published an open letter to President Roosevelt, where he wrote: ‘The recent gyrations of the dollar have looked to me more like a gold standard on the booze than the ideal managed currency of my dreams.’ This was a criticism of the ‘gold-buying program’ launched in October 1933. In this article I use high-frequency data on the dollar–pound and dollar–franc exchange rates to investigate whether the gyrations of the dollar were unusually high in late 1933. My results show that although volatility was pronounced, it was not higher than during some other periods after 1921. Moreover, dollar volatility began to subside towards the end of the period alluded to by Keynes.
34
Mishra, Amritkant. „Investigation of volatility and spillover in foreign ex-change return in Indian Chinese & Malaysian market“. International Journal of Accounting and Economics Studies 5, Nr. 2 (05.10.2017): 150. http://dx.doi.org/10.14419/ijaes.v5i2.8302.
Der volle Inhalt der QuelleAnnotation:
In this paper it is tried to make the comparison the foreign exchange return volatility in the three emerging economies of Asia. It is also endeavored to investigate the return co-movement and the volatility spillover between the foreign exchange markets of India, China and Malaysia with reference of US dollar, Indian Rupees, Chinese Yuan and Malaysian Ringgit in each other foreign exchange market to. The daily data have collected from Federal Reserve data base from April 2012 to March 2017. For analysis MGARCH model, the GARCH DCC as well as VAR model applied. The empirical result of volatility spillover effect shows that in Indian and Malaysian foreign exchange market the US dollar seems as shock transmitter. It also shows that the influence of US dollar in Chinese foreign exchange market is very low as compare to the Indian and Malaysian exchange rate market. In Chinese market Malaysian ringgit is dominant currency and it transmits the shocks to the US dollar. The conditional volatility result shows that among all the foreign exchange market, Indian market has high volatility return of foreign currency as compare to other market.
35
Bhuvaneshwari, D., und K. Ramya. „Cointegration and Causality between Stock Prices and Exchange Rate: Empirical Evidence from India“. SDMIMD Journal of Management 8, Nr. 1 (17.04.2017): 39. http://dx.doi.org/10.18311/sdmimd/2017/15720.
Der volle Inhalt der QuelleAnnotation:
Predicting the exchange rate fluctuations and volatility is possibly one of the very toughest exercises in economics as it affects the market movement. The dynamic relationship between stock prices and exchange rate have drawn the attention of many economists for both theoretical and empirical reasons and plays an important role in influencing the development of a country’s economy (Nieh & Lee, 2001). Therefore, the present study is focusing on stock market prices and exchange rate, which in theory, is expected that one affects the other. The US Dollar (USD)-Indian Rupee (INR) exchange rates and stock market prices of India from January 2006 to December 2015 are considered as sample data for this study. In this research, Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests are applied to test stationarity of data and the data was found stationary at first difference. Karl Pearson correlation test was used to find the correlating relationship between the variables and it is found that both the variables are significantly correlated. Johansen’s cointegration test is applied to determine the long-run equilibrium relationship between the study variables and identified that the variables are not cointegrated. Granger causality test is employed to determine the causality and short-run relationship between the variables and the result revealed bidirectional causality between variables.
36
Bhama, Vandana. „Macroeconomic variables, COVID-19 and the Indian stock market performance“. Investment Management and Financial Innovations 19, Nr. 3 (12.07.2022): 28–37. http://dx.doi.org/10.21511/imfi.19(3).2022.03.
Der volle Inhalt der QuelleAnnotation:
India witnessed the first major wave of COVID-19 in 2020. The second major wave during April 2021 caused a higher number of infected cases across the country. These waves of COVID-19, rising cases and lockdown announcements severely impacted the Indian economy. Moreover, huge volatility was observed in the prices of oil and exchange rates during the similar period. Thus, this study tests the effect of selected macroeconomic variables and the COVID-19 pandemic on the performance of the Indian stock market. Using co-integration and the vector error correction model on the NIFTY 100 firms, the findings suggest co-integration and long-term association among variables. The Indian stock market experienced an inverse connection with the exchange rate volatility; the coefficient value is 57.582. The exchange rates rose heavily (with a value of Indian rupee being 76.95 against US dollar) with the onset of COVID-19 cases. Further, these cases do hurt the sentiments of the stock market; however, the relationship is relatively infirm (the value is 0.22) as compared to that of the exchange rate. The accumulated major negative influence of COVID-19 on the economy had a weak impact on the stock market. In conclusion, it should be noted that after the first wave, businesses were more prepared and therefore incorporated the required changes that saw them through the second wave. AcknowledgmentThe infrastructural support provided by the FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.
37
Ogbulu, Onyemachi Maxwell. „Oil Price Volatility, Exchange Rate Movements and Stock Market Reaction: The Nigerian Experience (1985-2017)“. American Finance & Banking Review 3, Nr. 1 (12.11.2018): 12–25. http://dx.doi.org/10.46281/amfbr.v3i1.200.
Der volle Inhalt der QuelleAnnotation:
Given the observed volatility in crude oil prices in the international oil market and the role which oil and gas play in the Nigerian economy, this paper is an attempt to investigate the impact of crude oil prices and foreign exchange rate movements on stock market prices in Nigeria. In addition, the paper examined whether there is any volatility pass-through between the dollar price of Nigerian crude oil, foreign exchange rate of the Naira and stock market prices respectively. Data employed for the study are monthly values of the Nigerian Stock Exchange (NSE) All-Share Index (ASI), Dollar price of Nigerian Crude Oil (DPO) and the Official Exchange Rate of the Naira to the US Dollar (FXR) from January, 1985 to August, 2017. The methodology adopted for the study include the ADF unit root tests, Johansen co-integration tests, the ECM technique, Granger causality tests, variance decomposition as well as the GARCH(1,1) to model the volatility relationships among the variables. Findings reveal that there is one long-run dynamic co-integrating relationship among the variables ASI, DPO and FXR while the ECM results indicate that Crude oil price (DPO) significantly impact on Stock market prices. The Granger causality test reports a bi-directional causality relationship between ASI and DPO and a unidirectional causality running from FXR to ASI. The ARCH-GARCH volatility analysis demonstrates vividly that stock market prices in the NSE exhibit ARCH effect with a significant and positive first order ARCH term. The GARCH term is also positive and significant indicating that previous month’s stock market price volatility significantly influences current stock market volatility in the NSE. In addition, findings show that the volatility of dollar price of Nigerian oil (DPO) in the world oil market is significantly transmitted to the volatility of stock market prices in Nigeria. The pass-through effect of the volatility of exchange rate (FXR) to the volatility of stock market prices is also positive and significant. These findings offer significant informational signal to policy makers, portfolio managers/advisors and the investing public in achieving optimal asset and portfolio profile.
38
Ohwadua, E. O., und A. R. Akanji. „Dual Foreign Exchange Rate in Nigeria: Stylised Facts and Volatility Modelling“. Journal of Advances in Mathematics and Computer Science 38, Nr. 9 (28.07.2023): 81–97. http://dx.doi.org/10.9734/jamcs/2023/v38i91806.
Der volle Inhalt der QuelleAnnotation:
This study examines the dual dynamics of Interbank Foreign Exchange Market (IFEM) and Bureau De Change (BDC) Market rates between the Nigerian Naira and the US Dollar over a ten-year period from 2012 to 2022. We investigate the dual foreign exchange rates – Interbank Foreign Exchange Market (IFEM) and Bureau De Change (BDC) Market rates between the Nigerian Naira and the US Dollar for ten years from 2012 to 2022. By employing MGARCH (multivariate generalized autoregressive conditional heteroscedasticity), we analyse the volatility of the naira in the dual foreign exchange windows and examine the stylised facts as it affects forex management in Nigeria. Our findings confirm and extend the results of previous research, emphasizing the role of market segmentation, information asymmetry, autocorrelation, stationarity, volatility clustering, correlation dynamics, and spillover effects in the foreign exchange markets.
39
Kaur, Mandeep, und Navkiranjit Kaur Dhaliwal. „Volatility in Exchange Rate of Indian Rupee in Pre and Post Market-Determined Exchange Rate Regime“. Abhigyan 38, Nr. 4 (30.03.2021): 1–9. http://dx.doi.org/10.56401/abhigyan/38.4.2021.1-9.
Der volle Inhalt der Quelle
40
Kaur, Mandeep, und Navkiranjit Kaur Dhaliwal. „Volatility in Exchange Rate of Indian Rupee in Pre and Post Market-Determined Exchang Rate Regime“. Abhigyan 38, Nr. 4 (März 2021): 1–9. http://dx.doi.org/10.56401/abhigyan_38.4.2021.1-9.
Der volle Inhalt der Quelle
41
Laopodis, Nikiforos. „The Stochastic Character of Japanese Exchange Rates“. Journal of International Business and Economy 5, Nr. 1 (01.12.2004): 77–90. http://dx.doi.org/10.51240/jibe.2004.1.5.
Der volle Inhalt der QuelleAnnotation:
The paper explores the stochastic character of six yen exchange rates with respect to the Canadian dollar, French franc, Italian lira, German mark, British pound and the US dollar for the 1973-2002 periods. The methodological design is the multivariate Exponential GARCH model, which is capable of capturing asymmetries in the exchange rate volatility transmission mechanism. The results point to significant reciprocal and positive volatility spillovers after the Plaza Accord of 1985. Furthermore, the finding of absence of asymmetry in the same period implies that bad and/or good news in a particular market positively and equally affects volatility in the next market.
42
Yoon, Seok, und Ki Seong Lee. „The Volatility and Asymmetry of Won/Dollar Exchange Rate“. Journal of Social Sciences 4, Nr. 1 (01.01.2008): 7–9. http://dx.doi.org/10.3844/jssp.2008.7.9.
Der volle Inhalt der Quelle
43
Sun, Changyou, und Daowei Zhang. „The Effects of Exchange Rate Volatility on U.S. Forest Commodities Exports“. Forest Science 49, Nr. 5 (01.10.2003): 807–14. http://dx.doi.org/10.1093/forestscience/49.5.807.
Der volle Inhalt der QuelleAnnotation:
Abstract This article addresses the impact of exchange rate volatility on U.S. exports of four forest commodities. Exchange rate volatility is measured by the standard deviation of the growth rate of real effective exchange rate of the U.S. dollar. The nonstationarity of individual time series is explicitly taken into account by employing multivariate cointegration analysis and error correction models. The results show that exchange rate volatility has a negative impact on U.S. exports in the long term, but short-term dynamics vary for different commodities. A stable currency policy in the long run helps promote U.S. exports of forest commodities, although some commodities may benefit from exchange rate volatility in the short term.
44
Deng, Shuxin. „The Time-Varying Impact of Exchange Rate Changes on Disney Stock Returns and Volatility: Evidence from the Fed's Rate Hike“. BCP Business & Management 31 (05.11.2022): 369–77. http://dx.doi.org/10.54691/bcpbm.v31i.2652.
Der volle Inhalt der QuelleAnnotation:
In the world economy and financial markets, exchange rates fluctuate continuously over time. This paper assesses the effects of exchange rate fluctuations on the return and volatility of Disney's stock. A VAR model and an ARMA-GARCH model were developed to analyze the changes in stock prices in terms of value and volatility. This paper finds that changes in exchange rates have a limited impact on Disney stock prices and have no significant effect on the daily volatility of its returns. However, because the appreciation of the U.S. dollar triggered by the Fed's rate hike will offset this positive and negative effect, investors should keep their perspective elsewhere without caring about the volatility of Disney stock prices due to exchange rate changes.
45
Febiyansah, Panky Tri. „Exchange Rate Responses and Volatility Spillover Effects during Exchange Rate Responses and Volatility Spillover Effects during the COVID-19 Pandemic in Indonesia the COVID-19 Pandemic in Indonesia“. Economics and Finance in Indonesia 69, Nr. 2 (01.12.2023): 87–97. http://dx.doi.org/10.47291/efi.2023.01.
Der volle Inhalt der QuelleAnnotation:
This paper aims to assess the impact of the confirmed COVID-19 cases, the timing of the outbreak, and physical measures on the returns and spillover effects of exchange rate in Indonesia. The model will be tested by the exponential generalized autoregressive conditional heteroskedastic (EGARCH) process and the spillover volatility index. The study discovers that the confirmed cases, outbreak news, and the implementation of large-scale social restrictions simultaneously contribute to a leverage effect on the volatility of a direct quote of Indonesian Rupiah to Australian Dollar, Euro, US Dollar, Singapore Dollar, and Great British Pound. To a certain extent, the heat-wave as well as the meteor-shower effects as a result of clustering events and intense spillover effects in the currency market of Indonesia are observed.
46
Ibrahim, Mamuda Kukasheka, M. Tasi’u und H. G. Dikko. „A STUDY ON THE VOLATILITY SPILLOVER BETWEEN NIGERIAN AND BRICS ECONOMIES USING MULTIVARIATE GARCH MODELS“. FUDMA JOURNAL OF SCIENCES 8, Nr. 2 (30.04.2024): 170–79. http://dx.doi.org/10.33003/fjs-2024-0802-2270.
Der volle Inhalt der QuelleAnnotation:
BRIC-African relation has been of interest to key stakeholders especially given the inclusion of South Africa. In the existing literature some researchers hypothesized inclusion of Nigeria will accelerate BRICS objective of enhancing market access to ensure rapid economic growth among other objectives. This study utilized daily exchange rates of Naira/Dollar together with BRICS Dollar exchange rate for a period of 18 years. The study aimed to determine the volatility spillover between Nigerian and BRICS nations via Multivariate GARCH family: VECH, DBEKK and CCC Models. The result of VECH and DBEKK Models showed that all parameters were significant at 5% level, indicating clearly that there is positive impact of Exchange Rate shocks of Nigeria on the Exchange Rate Volatility of the BRICS economies, while for the CCC model only one parameter was significant at 5% level. This clearly indicated the existence of positive impacts of Exchange rates shocks of Nigeria on the Exchange Rate Volatility of the BRICS economies. On the other hand, only VECH model was able to capture the volatility spillover (own and cross) both on negative direction, suggesting a causal relationship between past volatility shocks in Nigeria and current volatility in the BRICS economies. Conclusively based on the information above VECH model was found to be appropriate to capture the volatility spillover between Nigerian exchange rate and that of the BRICS nations.
47
Guyot, Opale, Heather Montgomery und Dachen Sheng. „The effectiveness of foreign exchange interventions in Japan“. Journal of Infrastructure, Policy and Development 7, Nr. 2 (11.09.2023): 2171. http://dx.doi.org/10.24294/jipd.v7i2.2171.
Der volle Inhalt der QuelleAnnotation:
This study informs the academic and policy debate on the policy effectiveness of exchange rate interventions on exchange rate levels and volatility. Using a constructed data set comprising daily data on exchange rates, monetary policy fundamentals, exchange rate intervention dates and magnitudes of those interventions as well as financial news speculation of such interventions, we empirically estimate the policy effectiveness of Bank of Japan interventions in the exchange rate over the 12-year period between 2010 and 2022. This allows us to investigate the policy effectiveness of a variety of exchange rate interventions, or news of exchange rate interventions, across different time-horizons. We find that policy interventions in the yen exchange rate are more effective over short-horizons than long-horizons, more effective when the policy objective is a competitive devaluation of the yen rather than a revaluation, and more effective at influencing the level of the yen against major world currencies other than the US dollar. In fact, for the yen-dollar rate, we find that policy interventions may have the unintended consequences of weakening the yen (when the policy intention is to strengthen it) and increasing volatility in the yen-dollar exchange rate.
48
Nekoei, Arash. „Immigrants' Labor Supply and Exchange Rate Volatility“. American Economic Journal: Applied Economics 5, Nr. 4 (01.10.2013): 144–64. http://dx.doi.org/10.1257/app.5.4.144.
Der volle Inhalt der QuelleAnnotation:
Are an immigrant's decisions affected in real time by her home country's economy? I examine this question by exploiting exchange rate variations as exogenous price shocks to immigrants' budget constraints. I find that in response to a 10 percent dollar appreciation, an immigrant decreases her earnings by 0.92 percent, mainly by reducing hours worked. The exchange rate effect is greater for recent immigrants, married immigrants with absent spouses, Mexicans close to the border, and immigrants from countries with higher remittance flows. A neoclassical interpretation of these findings suggests that the income effect exceeds the cross-substitution effect. Remittance targets offer an alternative explanation. (JEL F24, F31, J22, J61)
49
Augustine Kutu, Adebayo, und Harold Ngalawa. „Exchange rate volatility and global shocks in Russia: an application of GARCH and APARCH models“. Investment Management and Financial Innovations 13, Nr. 4 (29.12.2016): 203–11. http://dx.doi.org/10.21511/imfi.13(4-1).2016.06.
Der volle Inhalt der QuelleAnnotation:
This study examines global shocks and the volatility of the Russian rubble/United States dollar exchange rate using the symmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH), and Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) models. The GARCH and APARCH are employed under normal (Normal Gaussian) and non-normal (Student’s t and Generalized Error) distributions. Using monthly exchange rate data covering January 1994 – December 2013, the study finds that the symmetric (GARCH) model has the best fit under the non-normal distribution, which improves the overall estimation for measuring conditional variance. Conversely, the APARCH model does not show asymmetric response in exchange rate volatility and global shocks, resulting in no presence of leverage effect. The GARCH model under the Student’s t distribution produces better fit for estimating exchange rate volatility and global shocks in Russia, compared to the APARCH model. Keywords: exchange rate volatility, global Shocks, GARCH and APARCH models. JEL Classification: F30, F31, P33
50
Akanni, Lateef Olawale. „Returns and volatility spillover between food prices and exchange rate in Nigeria“. Journal of Agribusiness in Developing and Emerging Economies 10, Nr. 3 (30.04.2020): 307–25. http://dx.doi.org/10.1108/jadee-04-2019-0045.
Der volle Inhalt der QuelleAnnotation:
PurposeEmpirical studies have documented the linkage between exchange rate movement and food prices. However, the purpose of this study is to investigate the degree and direction of returns and volatility spillover transmission between exchange rate and domestic food prices in Nigeria.Design/methodology/approachThe study uses weekly data from January 2010 to January 2019. Also, the study adopts the improved Diebold and Yilmaz (2012) approach to evaluate the return and volatility spillover between food price and naira to dollar exchange rate. The study also account for 2016 exchange rate crash in the interconnectedness between food prices and naira to dollar exchange rate.FindingsThe paper finds evidence of directional interdependence among the considered food prices and exchange rate based on the obtained spillover indexes. In addition, exchange rate returns and volatility transmission to food prices is more than it receives, particularly after the exchange rate crash.Research limitations/implicationsThe high consumption of staple foods requires policies on price stabilisation such as massive investment in local production and reduction in import dependence, in order to cushion the effects of exchange rate depreciation on domestic prices of food.Originality/valueThis study is the first empirical study to investigate the interconnectedness between exchange rate and domestic food prices for a food import–dependent developing country using the Diebold and Yilmaz approach.