Financial markets are dynamic and changes in the value of financial products are driven by many factors including economic growth, geopolitical news, scarcity, corporate earnings, demographic trends as well as different perspectives on future risk and uncertainty and, at the most basic level, the trade-off between fear and greed.
There are millions of financial products including many thousands of publicly listed companies, hundreds of thousands of ETFs, mutual funds and other investment products and millions of bonds and derivatives such as options, futures and warrants. Combined with that there are hundreds of thousands of macro-economic statistics time series including GDP, unemployment and inflation that can feed into research and policy.
When it comes to modelling financial risk or conveying a summary view of financial markets, there is a much smaller number of time series that is frequently used as a model input or simply as an overall bellwether. Alveo’s data management solution handle time series of every asset class or macro-economic category, in every frequency and for every length. Alveo’s Ops360 user interface includes data derivation to construct new time series or proxy missing values, data quality management, workflow configuration to onboard and distribute new data sets, extensive search capabilities and integration with external libraries.
In the first episode of this blog series, we looked at some of these risk factors covering gold, foreign exchange, volatility and inflation. In this second episode we take a closer look at some other key time series including interest rates, the property market, the equity market and crude oil.
The future will be different with the electrification of transport and the adoption of non-fossil power sources but oil has ruled the planet for the last century and still rules large chunks of the world. Key benchmarks include Brent Crude and WTI Crude with many different variations and spreads based on grade.
The history of the crude oil price is also the history of geopolitical developments. In the chart below we show the crude oil price showing upheavals around the first and second Gulf Wars and deep drops during recessions.
In the other half of the chart we highlight the evolution of the oil price during a turbulent period in 2008: oil prices went up during the first half of 2008 to come spectacularly down when the financial crisis hit later that year.
Interest rates convey the price of money and can apply to periods ranging from the overnight rate to the 30-year rate – and sometimes beyond that – together making up an interest rate curve.
Looking at the chart below, we plot ~60 years of history of the 10-year yield. We see the long boom in the bond market due to steadily falling interest rate following the inflation upheavals of the 1970s. We see that coming to an end with rates hitting zero in 2021 – when at one point the entire German bund curve was below zero – following the massive stimulus programs during corona. We find ourselves now in the – for many – unfamiliar territory of steadily rising interest rates.
Grouping interest rates of different tenors for the same asset type gives us an interest rate curve. There are many different interest curves, ranging from curves tracking the yield on government bonds to different credit curves reflecting the borrowing costs for firms with a specific credit rating or in a specific industry sector. Below we plot a yield curve for set of consecutive days.
Other than government bond yields, there are different interest rate benchmarks. Paraphrasing a popular saying, it can be said that the hand that sets the benchmark, is the hand that rules the world.
Another way to look at it is to track the history of individual tenor points side by side which can give another view of whether curves are steepening or flattening.
Like many other asset prices, that of property is linked to the interest rate climate. If we look at the time series of the residential property index of the Euro19 countries (prior to the accession of Croatia) we see a steady rise followed by a prolonged slump for 8 years after 2008 digesting the crisis. We then see strong growth from 2016 onwards culminating in 2021. This has recently reversed due to a fast-changing financing climate.
This is an aggregate index and there will be many variations in development between countries and cities. Other property indices focus on residential or on commercial real estate.
For our equity example we show a subset of the long history of one of the most well-known indices of all time: the Dow Jones Industrial Average. Somewhat unusually, the Dow Jones is a price-weighted equity index, meaning its value is derived from the price per share for each stock divided by a common divisor. This simply means that stocks with the highest share price receive the greatest weighting. More common in equity indices is to weigh the different constituents differently as determined by their market capitalization or the free-float part thereof.
The history of the DJIA goes back to 1884 when it largely covered railway companies. The first published value of the index was 40,94 points – since then it has multiplied about 800 times. We show the evolution of the index in the early part of the 20th century up to the strong post-war growth in the 1950s and 1960s and the slump in the 1970s – before the long boom in equity prices starting in the 1980s. You can see the strong growth in the 1920s followed by a deep decline after the 1929 market crash.