In the previous chapters, we have concentrated on USD INR pairs. Now, we will look at other currency pairs traded in Indian markets. These include the EUR INR and GBP INR. The operation of the other currency pairs works in a similar way to the USDINR. You can think of it as this: If you understand how Nifty 50 contracts work, you will be able to know how Bank Nifty operates.
This is why the purpose of this chapter is to quickly go through the specifications for the three other crosses we can trade. The 2 section of this chapter will focus on common trading techniques. This will mainly include technical analysis. This concludes our discussion about currencies. Let's now focus our attention on commodities.
Let's get started.
The EUR USD is the most traded currency globally. We don't have this contract in India yet, but the RBI has approved listing these crosses. It is only a matter time before the EUR USD pair will be available alongside GBP USD, JPYUSD etc. We do have EUR USD to trade for now.
As we all know, the Euro is the currency of Europe. The EURO, unlike other currencies is supported by the economies of many European countries. It does not rely on one economy.
The EUR INR contract structure looks very similar to the USD INR. These are the essential details you should know:
|Lot Size||EUR 1000||The number of shares in equity derivatives is called lot. Here it's a Euro amount.|
|The Basis||Rate of Indian Rupee versus 1 EUR|
|Tick Size||0.25 Pounds or INR 0.0025 in Rupee terms|
|Trading hours||Monday through Friday, 9:00 AM to 5:00PM|
|Expiry Cycle||Contracts up to 12 months||Equity derivatives can expire up to 3 months.|
|Last trading day||Contracts can be traded until 12:30 PM on the 2nd day before the end of work.||Equity derivatives can continue trading until 3:30 PM on the expiry date.|
|Final Settlement day||Last day of the month|
|Margin||SPAN + Exposure||SAPN typically is approximately 1.5% and exposure around 1%. The overall margin requirement is therefore approximately 2.5%.|
|Settlement price||The day of Final Settlement, the RBI Reference Rate||Spot closing price|
As you can see, the contract specifications for the USD INR pair are identical. Only difference is that EUR INR has a lot size of EUR 1000, while USD INR has a lot size of $1,000.
Let's examine how this will impact margins. Here is a snapshot of EUR INR futures.
As you can see, 74.8950 was the last traded price for the contract. This allows us to estimate the contract's value.
Contract Value = Lot Size Multiplied by contract Price
= 1000 * 74.8950
If the margin is 2.5%, then the margin should be around Rs.1,870/=. In fact, Zerodha has a margin calculator that can help you determine the exact margin value.
The margins are slightly lower than the USD INR pair but still much lower than what is required for an equity derivative contract.
After the USD INR pair, the GBP INR contract is likely to be the 2 most popular currency. Everything else is the same as before except for the lot size or the underlying. The underlying is 1 GBP in Indian rupees. The lot size is PS1,000. This makes the contract worth approximately Rs.89.345/-, considering that futures trade at 89.3450 as per 5 August 2016.
As you can see, the margin required to do this is slightly higher than the margins required for the other two contracts that we have already discussed.
Did you know that the GBPUSD pair is also known as the 'Cable' in international markets? When a currency trader claims he is short cable, it means that he is short GBPUSD cross.
Contrary to other currency contracts, the JPY INR contracts can be a little more complicated than others. The lot size is 100000 units instead of the usual 1000 units. This is the exchange rate for 100 Japanese Yuen in Indian Rupees.
Here's how you can see it:
In Indian Rupees, we are basically looking at 100 Japanese Yen. It costs Rs.66.2750 for 100 Japanese Yen. The contract value is Rs.66.2750 for a 100,000 lot.
= (100000 *66.27505) / 100
P&L for one pip (tick) of currency movement will be 0.0025*1000= Rs 2. This is the same for all INR pairs
The margin required to execute the JPY INR contract, Rs.2,808 /-,, is approximately 4.2%.
The margins required to contract JPY INR are the highest in currency segment. This could be because the contract could be volatile due to its lower liquidity. This is just an observation. I encourage you to use Excel to calculate the actual value to gain a better understanding of the volatility of JPYINR.
Spread contracts can be purchased for all currency pairs and all expiries. This is a snapshot of the same form NSE website -
As you can see spread contracts, other than USD INR, are not liquid.
If you wanted to trade contracts based on liquidity, this is what I would suggest.
This concludes my explanation of the basics of currency trading. Now, we will focus on the development of a basic trading strategy.
It is not uncommon to have a lot of discussion about the seasonality of currencies. When I refer to seasonality, I mean something like "USDINR always falls in December" or "USDINR always rises a week before expiry". Many people trade based on this expectation, without validating for seasonality. We decided to check for seasonality in currencies. Needless to say, we chose the USD INR spot data as the test.
This discussion is not intended for Varsity readers. It can be a little technical. The direct answer to the question whether there is seasonality in the USD-INR pair is no. There is seasonality across all time frames. You can now jump to the next section after you have reached this conclusion. If you are a statistician, you might want to continue reading. I will try to keep this brief, but it is possible.
Also, this section is contributed by our good friend Prakash; any queries regarding this should be directed to Prakash.firstname.lastname@example.org.
A statistical test is known as the "Holt-Winters test" can check for seasonality in any series of time. Three components make up the Holt-Winters method.
NiveauThis indicator measures the average USD INR change on a YOY base
TrendThis indicator measures the monthly average USD INR change
SeasonalityThis indicator measures the seasonal effect on price changes. USD INR, for example, almost always increases in January and falls almost every April.
There are two options for components: level, trend, or seasonality.
It is not possible to discuss the details.
Holt-Winters seasonality test:
Holt-Winters test is a method of checking for seasonality in time series. We build a forecast model (let's call it Model 1), and then study its residuals. Model 1 doesn't have a seasonality component. Then, we build another forecast model (Model 2) with a seasonality element and then check for errors.
We then compare the errors from both models to see if Model 2 provides a better forecast than Model 1. To determine if accuracy is better, we use the 'Chi-Square test. If Model 2 is statistically more accurate than Model 1, we conclude that there is a seasonal pattern in the data. If the accuracy of both models is the same or Model 1 has a better accuracy, then there is no seasonality to the data.
Seasonality Results for USD INR
Check out the weekly seasonality:
Model 1 (without seasonality): The best model (M,N,N) has coefficients of 0.9999.
This model shows that weekly data only has a level component, and not a trend component. The coefficient of "level", i.e. The next week's prices are approximately 0.9999 times the current week's.
These parameters will be easy to understand for readers who are familiar with the Random Walk Theory. This model suggests that each week's USD INR price change is random.
Model 2 (with seasonality): This model is the best with coefficients of 0.7 and 0.0786
This model shows that weekly data contain both a seasonality and a level component. This model suggests that the next week's prices will be 0.7 times this week's, and that seasonality will contribute to the rest of the price.
Conclusion: The Chi-square test found that the accuracy of model 2 is approximately equal to model 1. The accuracy of a seasonality model based on USD INR is not increased by forcing it.
This is possible only if there is no seasonality. We can conclude that the weekly analysis of the data shows no signs of seasonality.
Model 1: This is the best model with coefficients of 0.9999.
As in the case with a weekly model and a random walk, a model based on monthly data can also be used.
Model 2: This is the best model with coefficients of 0.9999 and 0.01
This model shows that the next month's closing prices are almost equal to this month's, with a slight seasonality effect.
Conclusion: The Chi-square test found that 20% of model 2 accuracy is greater than model 1. This improvement in accuracy could be due to randomness (e.g., the window period, the sample data, etc.
In statistics, it is common to expect at least 95% accuracy from model 2 to determine that there is seasonality. In the case of USD/INR, it is clear that there is no monthly or weekly seasonality.
This is the RBI's website. It contains data on USD INR spot prices for the last 8 years.
The next time someone makes a random statement such as "The USD INR pair almost always falls before Christmas", you will know that he is trying to sound smart but has no real insight.
You might think about doing a fundamental analysis on a company like Hindustan Unilever Limited. You would typically study the company's business, financial statements, corporate governance and peer companies to determine if it is worth investing in. Fundamental analysis of equities is straightforward. But if you take a look at currency pairs (USD INR), you'll see that they have much more fundamental dimensions. These include the macroeconomics in the USA, which are dependent on many domestic and global factors, and the Indian macroeconomics, which are dependent again on multiple domestic or international factors. Once you have a better understanding of each, you can compare them and create a relative view.
This is not an easy task and not everyone can do it. To do a quality fundamental analysis on currency pairs, it would be helpful if you were an economist who is also a trader. Technical Analysis (TA), which is used to trade currencies and commodities, is becoming more popular. Technical Analysis assumes that all prices are discounted, even complex fundamental views, as you probably know. This assumption is used to analyze the charts and form a view.
TA for commodities and currencies works the same way as it does for equities. This Module on Technical Analysis will teach you how to use Technical Analysis.
A few photos of TA-based trade setups will be posted.
These two candles are encircled and form a classic candlestick pattern known as the 'Piercing Pattern'. The piercing pattern suggests that the trader should be long USD INR. The trade went well, as you can see.
Here's a bearish Marubozu for GBP INR
With the bearish Marubozu suggesting you short the underlying, with the expectation that it will continue to slide downwards,
Trade setups are endless. Many people believe that commodities and currency require different technical analysis. However, this is false. TA works on all time series data, stocks, commodities, currencies or bonds.
With this, we will be closing our discussion about Currencies. We will now discuss the 2 and parts of this module. Commodity trading.