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The correlation coefficients Tables 5 to 7 among the exchange rates are not very large but offer some useful insights.
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Many readers from around the world have asked us what was the highest or lowest value of 1 USD to INR during each ear after i.
Our team works very hard to bring in detailed content for our readers. We all will be really glad if you could share the post if you liked it. Werner Antewelier, University of British Columbia.
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And thats what RBI is trying to do. You share your most intimate secrets with your search engine without even thinking: All of that personal information should be private, but on Google i Look at the charts. You will see that the dollar has fallen between Dec and Dec against all the other currencies. However, the rise in Indian rupee 0. The price of everything depends on the demand and supply.
In our case, you need to look at two things:. When the interest rates are increased, the liquidity in the economy falls. Two things happen simultaneously - 1 Savings look more lucrative since the bank deposit rates will rise; and 2 Borrowings look expensive which means people will have less money to spend. If you are living in the United States, what will you feel in this situation? You will not have a lot of money to spend so suddenly you will want to liquidate your investments.
This is one reason why investors of the United States would want to sell their investments all over the world including in India. When they sell their investments in India such as shares of an Indian company , they receive Indian rupees in return.
Now, they want to sell these rupees and buy dollars, because their home currency is US Dollar. Therefore, the supply of rupee will increase, thereby reducing its value. This is what happened in the week of US Fed rate hike. And this is exactly what is written in the article shared by you. See this - Dollar gains as risk appetite improves; yields rise after Fed rate hike news on Dec After the increase in Fed rates the value of Indian rupee fell, as we just saw. Whenever the rupee depreciates, imports get costly which then translates to a lot of pain for the citizens of the country although it is beneficial for exporters.
To avoid that pain, RBI wanted to ensure that the value of rupee rises. Hence, it started buying the Indian rupees by selling US dollars.
When this happened, the value of Indian rupees was rising again because now the demand has increased. However, what happened to the global price of US dollar? I have read the details to your question, and you specifically prohibited to use analogies. But this concept is not easy to understand.
Also remember that all answers to Quora questions are not only for the question asker, so I hope you forgive my mistake. Read on to understand the analogy! If I go to the market to buy socks, am I 'buying the socks' or am I 'selling my money'? If many people go and buy socks, the price of socks will rise.
However, this does not mean that the value of rupee will fall in the whole country. This is because the share of socks in the Indian economy is minuscule. It will not affect rupee as a whole, but it will affect the value of socks in relation to the rupee. In the same manner, if RBI buys rupees, the price of rupees will rise. However, this does not mean that the value of dollar will fall in the whole world.
This is because the share of rupees in the global economy is minuscule. It will not affect dollar as a whole, but it will affect the value of rupees in relation to the dollar. When we are dealing with currencies, we should always see one of them as the commodity and the other as currency.
In our example, the Indian rupee is like the socks. On the contrary, the meteor shower hypothesis is consistent with the idea of shock transmission between different markets, countries or regions. Under this hypothesis, volatility is autocorrelated across regions, i.
Although there are quite a number of studies in examining volatility transmission from global stock markets to the Indian stock markets, there is hardly any study relating to the Indian foreign exchange market. Therefore, the contribution of this paper to the existing literature is a thorough study of volatility transmission from major exchange rates to the INR using high frequency data. Motivated by the impact of the recent crisis, this study analyzes the dynamics of volatility transmission from foreign exchange markets in the EMEs and advanced economies AEs to the Indian foreign exchange market.
The findings are also corroborated by examining cross-correlations and running simple pair-wise Granger causality test. The empirical findings suggest that exchange rate of the Indian Rupee could not be insulated from volatilities in the exchange rate of leading currencies in the AEs as well as EMEs. The structure of the paper is the following.
Section II provides a brief review of the literature on volatility transmission focusing primarily on the contributions that are most relevant for this study.
The methodology is presented in Section IV. The empirical findings and its implications are discussed in Section V. The concluding section summarizes the findings of the paper and suggests some further extensions of the study. Ever since the advent of the ARCH model by Engle , research on the transmission mechanism of volatility between various segments of the financial market has been fast advancing. The application of ARCH and its generalized form, i. Studies on volatility transmission based on low-frequency foreign exchange data are, however, relatively sparse.
They employed a vector autoregressive VAR model as a basis for the variance decomposition of forecast error variances in order to measure the magnitude of return and volatility spillovers in the foreign exchange market.
Bollerslev used a model with time-varying conditional variances and covariances, but constant conditional correlations, to model a set of five nominal European-US Dollar exchange rates in the period before and after the inception of the European Monetary System EMS.
Their empirical evidence was generally against the heat wave hypothesis. Thereafter, Kearney and Patton employed a series of multivariate GARCH models to analyze the volatility transmission between the members of the EMS prior to their complete monetary unification. They provided many interesting findings on the exchange rate volatility transmissions within the EMS including the effect of time-aggregation on volatility transmission.
In fact, less volatile weekly data was found to exhibit a significantly smaller tendency to transmit volatility compared to the more volatile daily data. This finding was consistent with the fact that markets have a greater propensity to transmit volatility in active as opposed to tranquil periods, as shown by Andersen and Bollerslev His findings suggested only simultaneous interaction between the two exchange rates when it comes to causality in the mean and both simultaneous and one-way DEM-JPY interactions regarding the causality in the variance.
Chowdhury and Sarno also applied multivariate stochastic volatility models to analyze volatility spillovers across exchange rates.
They formulated a flexible yet parsimonious parametric model in which the daily realized volatility of a given exchange rate depends both on its own lags as well as on the lagged realized volatilities of the other exchange rates.
They found evidence of statistically significant intra-regional volatility spillovers among the Central European foreign exchange markets. Lee employed multivariate GARCH model to test for cross-country mean and volatility transmission among ten emerging foreign exchange markets in Asia and Latin America while allowing for possible risks, leverage and persistence effects.
The findings suggested presence of both regional spillovers and the transmission of shocks from external stock and foreign exchange markets. The spillovers from external markets were larger to Asian than Latin American currency markets. In the Indian context, there is hardly any study examining the spillover effect of foreign exchange market volatility. There are, however, a few studies employing GARCH model to estimate volatility of the exchange rate of the Indian rupee, per se , while analyzing the effectiveness of central bank intervention.
For example, Goyal and Arora examined the impact of conventional monetary policy measures such as interest rates, intervention and other quantitative measures, compared to central bank communication on the exchange rate level and volatility.
They found that quantitative intervention was the most effective among all the central bank instruments. Their findings were in conformity with most of the earlier studies that found Reserve Bank intervention decreases volatility Pattanaik and Sahoo, ; Edison et al. Since March , India has been operating with a managed flexible regime, where the objective is not to achieve any explicit or implicit target for the exchange rate but to contain volatility by ensuring orderly market conditions.
The Reserve Bank, however, does not target the level of exchange rate, nor it has a fixed band for nominal or real exchange rates to guide interventions, the capital account management framework helps in the bounded float.
There are few controls on capital account such as imposition of limits on foreign direct investment on specific sectors and on portfolio investment in equities.
However, there are controls on debt inflows, driven by considerations of external stability and they are altered relatively infrequently in response to changing macroeconomic conditions and not with a view to impacting the daily movement of the exchange rate. Within these overall boundaries, the exchange rate is determined by daily variations in demand and supply. Therefore, the objective of exchange rate management is to find a balance between the short-term risk of the Indian Rupee spiraling downwards and the medium-term risk of a loss of confidence in meeting external obligations.
Before we move to examine the spillover effects of volatility in other currencies to the Indian Rupee, it is imperative to look into the movements in the major international currencies in the recent years. The Euro traded within a relatively narrow range in the first half of and stayed range-bound until late July and then began a run of steady depreciation.
The later period involved the flight-to-quality associated with the post-Lehman Brothers debacle and a strong sell-off of emerging markets, which benefited the US Dollar.
But after Bear Stearns sale and the appearance of more normal market conditions, the Yen underwent a period of depreciation that ended in September In the post-Lehman world, the Yen benefited from unwinding of carry trades where investors were short selling Yen futures, and also formed a view that the Yen was a safe-haven currency as Japanese banks did not suffer from US subprime exposure as did their competitors in Europe and the US.
However, as the news on the macro economy in Japan became progressively worse beginning early , the safe-haven notion disappeared. This trend changed in the summer of as the depth of the problems in British banks was revealed and the market began to price in the deterioration in UK economic conditions resulting from the magnitude of the unemployment and fiscal issues.