Having considered the USD INR pair in detail during the preceding chapters, we shall now examine the EUR INR, GBP INR and JPY INR currency pairs traded on Indian markets. Similar to the USD INR, their functioning is comparable to that of the Nifty 50 contracts – if you understand the former, then you can master Bank Nifty as well.
Therefore, in this chapter we will quickly review the contract specifications of the three other crosses that can be traded. The second section will concentrate on some popular trading techniques using technical analysis. After that, we can move on to discussing commodities.
So let’s get started.
The EUR USD is one of the most heavily traded currencies in the world, with RBI giving permission for exchanges in India to list it. This will mean that soon traders here will be able to trade this cross as well as others such as GBP USD and JPY USD. At the moment, however, we do still have access to the EUR INR contract.
The EUR is the currency of the European Union; it’s distinctiveness lies in that it isn’t just supported by one economy, but rather a combination of many European countries’.
The EUR INR contract structure is similar to the USD INR. Here are the main points that should be kept in mind –
As you can observe, the contract specs of EUR INR are comparable to those of USD INR. The sole discrepancy is that while the lot size in USD INR is $1,000, it is € 1,000 in the former.
Let us consider the effect that this will have on our margins. We can get a glimpse of EUR INR futures here.
By looking at the last price of the contract, which is 74.8950, we can estimate the value of it.
Contract Value = Lot size * Contract price
= 1000 * 74.8950
The margin should be close to Rs.1,870/- if approximated at 2.5%.
The margins for this USD INR pair may be slightly better than the standard, but are still significantly below what would be needed for an equity derivative contract.
The GBP INR contract is the second most popular currency contract, following the USD INR pair. Its feature set, other than the underlying and lot size, remains identical to that of its peer. The underlying is the exchange rate for one GBP in Indian Rupees, with a lot size of £1,000; which brings its contract value to approximately Rs.89,345/- as of 5th August 2016 when it was trading at 89.3450.
As you can see, the margin for this contract is slightly higher than for the others we’ve mentioned.
Did you know that in the international markets, the GBP USD pair is referred to as “Cable?” Thus, if a currency trader mentions they are ‘short cable’, it means they are shorting the GBP USD cross.
JPY INR contracts require additional attention. Unlike other currency contracts, the lot size is 100000, with the underlying being the exchange rate for 100 Japanese Yen in Indian Rupees.
So when we look at this –
We are effectively examining the rate of 100 Japanese Yen expressed in Indian Rupees. This implies that it takes Rs.66.2750 to acquire 100 Japanese Yen. Since the lot size is 100,000, its associated contract value is –
= (100000 *66.2750) / 100
The P&L for one pip(tick) movement of the currency will be 0.0025*1000= Rs 2.5 which is the same for all INR pairs
The margin required for the JPY INR contract is Rs.2,808/-,, which translates to about 4.2%.
It is evident that JPY INR contract has the highest margin requirement in the currency segment. This could be down to its potentially high volatility as a result of lower liquidity. It would be wise for you to evaluate the actual value on Excel, so as to gauge the volatility of JPY INR correctly.
Spread contracts are available on all the currency pairs across all the expiries. Here is the snapshot of the same form NSE’s website –
But as you can see, the spread contracts (apart from USD INR) are not really liquid.
If you are considering contracts to trade based on liquidity, here is an order of preference for you to consider:
It is evident that we understand the principles behind currency trading. So now, let’s move on to creating a basic trading strategy.
Debate often surrounds seasonality related to currencies. An example of this is the presumption that USD INR will decrease in December or will increase a week before expiry. Surprisingly, some trade decisions are made based on such expectations without fact-checking for seasonality. In order to verify this, we decided to analyze the USD INR spot data and run the necessary tests.
This discussion may be technically complex, which is not intended for a casual Varsity reader. If you want a swift response to whether the USD INR pair experiences seasonality, the answer is straightforward – no, it does not occur across any timeframe. Thus, you could move on to the next section immediately. For those of you who value statistical approaches though, I will endeavour to present my views briefly.
To check the seasonality in a time series, one can use Holt-Winters test, a statistical examination. This technique has three distinct components which are…
Level: this indicator measures the average change in USD INR on a YOY basis
Trend: This indicator measures the average change in USD INR on a month on month basis
Seasonality: Seasonality is an indicator which looks at whether or not there’s any kind of yearly cyclicality to price movements. For instance, USD INR typically appreciates in January, and depreciates in April, and so on.
There are two possibilities for components (level, trend, and seasonality)
I guess the details of this are beyond the scope of this discussion.
Holt-Winters test for seasonality:
In Holt-Winters test, we analyze a time series for seasonality by building two forecast models: an initial model (Model 1) without any seasonality elements and another (Model 2) with a seasonality component. We then compare the errors of Model 2 to assess the presence of any seasonal patterns.
We use the ‘Chi-Square’ test to compare the errors of both models and establish if Model 2 provides a superior forecast. If Model 2 is demonstrably better than Model 1, this suggests that there is a seasonal pattern in the data. Conversely, if accuracies are identical or Model 1 outperforms, then seasonality isn’t present.
Seasonality results for USD INR
Check for weekly seasonality:
Model 1 (without seasonality component): The best model is (M, N, N) with coefficients 0.9999
This model implies that for weekly figures, there is merely a constant component and nothing in terms of a trend. The coefficient for the “constant” term is 0.9999, which translates to next week’s price being approximately equal to this week’s price.
For readers who are familiar with the Random Walk Theory, this model postulates that the weekly USD INR price fluctuation is a random walk.
Model 2 (with seasonality component): The best model is (M, N, M) with coefficients 0.7 and 0.0786
This model suggests that weekly data possess a level and seasonality aspect. These components result in the prediction that the price of next week will be 0.7 times that of this week, with the remainder owing to seasonality effects.
The results of the Chi-square test demonstrate that utilizing a seasonality model on USD INR does not result in a greater accuracy when compared to the regular model. The probability is 100% that their accuracy will be identical.
Without seasonal variation, we are able to analyze the data on a weekly basis and reach conclusions.
Model 1: The best model is (A, N, N) with coefficients 0.9999
As with a weekly model, the data from a monthly model also suggests that prices follow a random walk.
Model 2: The best model is (A, N, A) with coefficients 0.9999 and 0.0001
The model suggests that there will be a slight variation of seasonality, but the next month’s closing price should remain close to this month’s.
The chi-square test indicated that there is a 20% probability of model 2 accuracy surpassing model 1 accuracy. Statistically speaking, certain conditions, such as the chosen window period and the sampled data, could account for this outcome being random.
In statistics, it is usually necessary to achieve at least a 95% certainty that model 2 is more accurate than model 1 before coming to the conclusion that there is seasonality in data. Thus, regarding USD-INR, it can be determined that neither monthly nor weekly seasonality exists.
The last 8 years USD INR spot data for this is taken from RBI’s website.
Next time you come across someone who makes a statement like “the USD INR pair almost always goes down before Christmas”, bear in mind that they’re just trying to appear knowledgeable without any factual evidence.
When we look at analysing stocks from Hindustan Unilever Limited using fundamental analysis, it is relatively simple. We look at its business model, financials, management and compare it to its peers. One dimension more complex to consider is when we investigate currency pairs, say USD INR. This involves looking at the macroeconomics of both countries and understanding how domestic and international factors affect them. Comparing each factor to the other can finally help in forming a view on the stock or currency pair.
This isn’t a task easily accomplished by many. To perform sophisticated fundamental analysis on currency pairs, an economist with a trader’s approach is required. It’s no wonder, then, that Technical Analysis is looked upon more favorably when trading currencies or commodities. As you know, Technical Analysis presumes the screen-price discloses all fundamentals views currently at play. From there, charts can be assessed and conclusions drawn.
The same principles of technical analysis apply to currencies, commodities and equities.
You can check more about TA in the TA module.