Analysing the Swedish krona Short-Term Rate (SWESTR) development using Power BI

The Swedish krona Short-Term Rate (SWESTR) is a reference rate, meaning that it can be used as a reference for setting rates between banks. The SWESTR is completely based on transactions happening in the market and is published every bank day at Riksbanken’s website. It is intended to be used for loans with the shortest term, for example as a so called over-night rate (O/N rate) or tomorrow-next rate (T/N rate). For more information about SWESTR, please refer to this interview with a Riksbank representative.

Reading data to Power BI using a REST API

The SWESTR went live in September 2021. Data is published each bank day and contains for example the current SWESTR rate, the volume and number of transactions. The data can be read by utilising the Riksbank’s REST API. In a previous blog post, we guided readers on how to read SWESTR data to Power BI via the REST API. 

Our report, called Report of Swedish krona Short-Term Rate (SWESTR), reads the data directly to Power BI by using the REST API. Below is a picture of the report, available in our Portfolio, which we will now analyse further.

It can be seen that since the launch in Q3 2021, the rate has increased much. In order to reduce the noise of potentially low/high values, we present averages in our charts. For example the 1 week average rate, seen in the chart below, is the average rate for that week. The 1 week average, hence roughly also the SWESTR itself, has increased from -0.1% at the end of Q3 2021 to 3.9% at the end of Q4 2023. That is a dramatic increase of around 400 basis points (bps) in approximately 2 years.

We also present the latest published SWESTR rate – at the time of writing this, it is 3.898%. That is 150 bps higher than 1 year ago, which is also a quite dramatic increase. Similarly to the 1 week average, we also present the averages calculated over longer time periods (1 – 6 monhts), which can be seen in the chart below. Naturally, the 6 months average will be “slower” and have less noise. It can be seen that all the four averages converged at the end of 2023, which means that the SWESTR value has been flat for some time.

SWESTR performance metrics

The volume, in terms of SEK per day, and the number of transactions per day, are also presented as can be seen in the chart below.

It seems that the SWESTR is gaining in popularity. It started out in Q3 2021 with on average 42 transactions per day. The average volume per day was 36.6 billion SEK. On average, this gives approximately 870 million SEK per transaction. The Riksbank then considers all of these transactions and calculates the rate to be used the next day for new transactions. In Q1 2024, there are so far 127 transactions per day – an increase of more than 200% since the launch. The average volume per day is 48.0 billion SEK, which is an increase of approximately 30% since the launch. However, this corresponds to on average 378 million SEK per transaction, which is a decrease of 57% since the launch. To summarise, the volume performance since launch is:

• Transactions per day: 42 to 127 (appr. +202%)
• Volume per day: 36.6 BSEK to 48.0 BSEK (appr. +31%)
• Volume per transaction: 870 MSEK to 378 MSEK (appr. -57%)

So, to summarise, many more but smaller transactions happen each day. The overall daily volume has substantially increased (+11.4 BSEK) as well.

Going back to the chart with the (average) rate at the end of each quarter, we see a massive rate increase in Q3 2022, which is approximately when central banks started to raise interest rates. The SWESTR went from 0.1 to 1.6% in just one quarter. The next quarter it increased by 0.8%, which is also a large increase. In the recent quarters, however, the rate is becoming more stable. In Q4 2023, it just increased by 0.2%. We can also note that the latest SWESTR (3.898%) is actually below or the same as the average of 3.9% at the end of 2023. So, if this continues for the rest of the quarter, we will see a “peak” of the rate in Q1 2024.

In this post, we have effectively used fundamental data analytics principles, to look at the SWESTR performance from many angles – latest rate (“price”), monetary volume and number of transactions. For example the monetary volume is important both for large and small financial actors and investors. If the volume is too large for an actor, the actor might not be confident that the SWESTR is an appropriate rate. On the other hand, if there is too little volume, large actors/investors might not want to use it for their transactions as it gives too little insight to their pricing case. Their transactions could also make up a large portion of the SWESTR volume which is not a desirable property of a reference rate. Finally, we have also reviewed several averages from 2021 to 2024, and the overall volume development since launch, to get some historical background as well.

A closer look at the NOK exchange rate development using Power BI

In this blog post, we will elaborate on the Norwegian krone (NOK) exchange rate development by using Power BI. In our Portfolio, we have built a report which presents NOK exchange rates and bond yields. The data is read from Norges Bank directly to Power BI using their REST API. REST API stands for REpresentational State Transfer API, and it is a common way of structuring and accessing data in a programmatic way.

Introduction to the Power BI report

Our report, called Norwegian krone (NOK) exchange rate & bond yields, presents the NOK performance against an array of currencies. For example, the NOK exchange rate against Canadian Dollar (CAD), Euro (EUR) and US Dollar (USD) are presented. In addition, the NOK bond yields for the government bonds are presented. Finally, the so called “bond spreads” are presented. Bond spread refers to the difference in yield between a short-term bond and a long-term bond. More information about bond spreads is found below.

To start with, the user can compare the percentual and actual development of exchange rates. It is possible to select which currency to compare with, and the time interval can be selected. In our report, we have decided to read data starting from Jauary 2010, but Norges Bank provides data even earlier than that. Monthly and quarterly changes are presented in the matrix for each currency which can be seen below.

NOK performance highlights

The total development since 2010 is presented and some highlights are:

• NOK has strengthened vs BRL (Brazilian real) by 36%
• NOK has weakened vs GBP by 44%
• NOK has weakened vs USD by 82%

To further zoom in on the performance, in the picture below, we have filtered the page on the year 2023 and only selected EUR, GBP and USD for analysis.

To give the user some background on the exchange rates development, we also present bond yields and bond spreads. For example, increases in the NOK government bond yields might correlate with a stronger NOK and vice versa.

Bond spreads

The bond spread is an important measure, as it typically tells how much of a premium you can get by lending your money for a longer time span. For example, if the 3 years bond yield is 3.0% and the 10 years yield is also 3.0%, investors might think there is no reason to “lock” the money for 10 years and could rather go for the shorter time span. On the other hand, a large bond spread, which could happen if the 3 years yield is at 3.0% and the 10 years yield is at 5.0%, could indicate that there are large risks with the 10 years yield, which could scare investors.

Summary page in Power BI

We present a summary page with NOK exchange rates and an extensive summary of NOK bond yields. The durations of the bonds span from 3 months all the way to 10 years. Finally, we present bond spreads in terms of differences between the 10 years yield and the 3/5 years yields. The development is evaluated in many different time periods, for example quarter-to-date (QTD), vs 5 years ago and vs 10 years ago. Hence, it gives a compressed historical view as well as an emphasis on the current values. It is an efficient Power BI visualisation which provides an overview of the most important measures with regards to exchange rates and bond yields development.

For more information on how Norges Bank shares open data, for example related to exchange rates, securities and interest rates, please see their website.

How to read Norwegian krone (NOK) exchange rate data to Power BI

In this blog post, we are going to explain how to read data from Norges Bank to Power BI. We have done so in one of our reports, available in our Portfolio. The report reads data from Norges Bank directly to Power BI. We do so by utilising their REST API, which provides open data. REST API stands for REpresentational State Transfer API, and it is a common way of structuring and accessing data in a programmatic way.

Sometimes, there can be security measures applied to the REST API. In the case of Norges Bank’s API, there are no security measures so the API is fully open. If you are an API developer and wants to earn money by providing an API, you typically make the users pay for access to the API, and security measures make it impossible to reach for non-paying users.

The user starts by going to Get Data in Power BI, which is going to look like this. Then the user selects “Web” in the list presented.

From Norges Bank’s website, we select that we want to read the USD exchange rate from 1st January 2023 until 24th January 2024. We also select that we want to read by API, and we then get the following message.

The URL listed is:

https://data.norges-bank.no/api/data/EXR/B.USD.NOK.SP?format=csv&startPeriod=2023-01-01&endPeriod=2024-01-24&locale=en

Here, EXR means Exchange rate and USD.NOK means that we want to read the USD-NOK exchange rate. We have selected a csv format and the start period and end period are visible in the URL. We simply add this URL to the URL field in Power BI which will look like below.

The user now gets the data from January 2023 in return. Each row of the data is the data for that day, in this case it is the exchange rate at that day.

Depending on the use case, it might be better to formulate requests that do not read all the data at once. For example, the report can have a button to refresh data depending on user selection. Then the user might select 2015 – 2024, and that request can then be sent to the API. This can be a nice solution if there are many potential API requests. Instead of reading all of these, they can be created dynamically. For example, Norges Bank has a section in their data warehouse where they provide Norwegian government securities – Prices and yields. Here, there are many Securities which can be seen below.

They can also be read with different Frequencies and Measurement units. It creates many combinations, so it can be a good idea to dynamically create such request depending on the user selection, rather than to pre-read all of them. In this report, however, it is straightforward and all the currency data is read from 2010.

Finally, the data will look like this in Power BI and now it is time to visualise the data in a meaningful way.

For more information on how Norges Bank shares open data, for example related to exchange rates, securities and interest rates, please see their website.

A closer look at the SEK exchange rate development using Power BI

In this blog post, we are going to go behind the scenes of the development of two reports available in our Portfolio. These reports read data from the Swedish central bank – the Riksbank – directly to Power BI. We do so by utilising the REST APIs provided by the Riksbank. REST API stands for REpresentational State Transfer API, and it is a common way of structuring and accessing data in a programmatic way.

Background on REST APIs

Some REST APIs have security measures in place, so that only authorised users can access the data. The user then has to send credentials by using the API, and if they are verified correctly, data can be read. With the Riksbank’s API it is not required with credentials. Hence, this guide is going to skip that part, and we are only reading, not writing, data when using the API. We are going to access interest rates for the SWESTR which is a reference rate with short duration (overnight rate). For more information about SWESTR, please see the Riksbank’s website.

Our Power BI report, called Swedish krona (SEK) exchange rates & bond yields, presents the SEK performance against an array of currencies. It also presents the SEK bond yields as well as the bond yields for select currencies. The data is read from the Swedish central bank – the Riksbank – and it goes back to 2010. We will here review the SEK exchange rate performance.

To start with, the report has a table with quarterly development since 2010. The user can expand to see the performance either per quarter or per month.

The total development since 2010 is presented and some highlights are:

• SEK has strengthened vs JPY by 7%
• SEK has strengthened vs NOK by 20%
• SEK has weakened vs CHF by 75%
• SEK has weakened vs EUR by 10%

We also present a chart and a filter possibility with respect to the date range and currencies of interest. Here, the user wants to review SEK performance vs JPY, NOK, CHF and EUR since 2018.

To further help the user understand possible reasons behind the development, the report presents the bond yields for the currencies where such data is available. For example, here the user reviews JPY and SEK bond yields since 2019. Also the so called “bond spreads”, in this case the difference between 10 years yield and 5 years yield, are presented. If, for example 5 years yield is much higher than the 10 years yield, one scenario could be that investors might skip investments in the 10 years as the 5 years offers higher yield with shorter “lock-up” period. In addition, the exchange rate development is overlaid in two of the charts, in order to show the correlation between bond yields and exchange rate for the selected currencies.

Finally, the report ends with an extensive summary page. The development is evaluated in many different time periods, for example quarter-to-date (QTD), vs 1 year ago, vs 5 years ago and vs 10 years ago. This gives both an emphasis on the more recent values but also gives some history without the need of adding charts.