Why are financial markets reacting to the Czech National Bank’s measures with a delay? Because of frictions. Vlastimil Reichel, recent recipient of the Dean’s Award for the Outstanding Dissertation Thesis defended in 2021, talks about them in this interview.
Think of the real estate market as a bicycle. When the bike is newly assembled, clean, and well oiled, it works perfectly. Unfortunately, you don’t get that perfect functioning in real life because once you start riding it, you collect dust, dirt, maybe get the bike wet, maybe get it muddy. So, from the first moment, the bike won’t be working as perfectly as the engineer who designed it would have wanted it to. The functioning of a clean, new bike is like the theoretical functioning of a certain market in an economy. You can think of friction as dust, dirt, and wear and tear on the individual parts that make the wheel harder to turn, or something squeak now and then. In other words, various frictions prevent the property market, let’s say, from functioning as economic theory would predict (i.e. under some imaginary perfect conditions), for example, by making it adapt more or less slowly to various sudden changes in the economy.
Macroeconomic models, as they are mostly used now, combine information from data and theory. The significance of many economic relationships is very difficult to observe purely from data, so we have to use a theoretical framework – in my case, the dynamic stochastic general equilibrium model, or DSGE model. If we’re able to show that our theoretical model is able to replicate the behaviour of the data well, we can use it to predict future movements in macroeconomic variables. And if frictions are important to the model’s ability to replicate the behaviour of macroeconomic variables or to predict future movements, then it’s appropriate to include them in the model structure.
In my doctoral thesis I was primarily concerned with modelling the property market and therefore chose frictions that were relevant to that market’s setting. So, I was modelling an economy in which people take out loans very often. The Czech National Bank has a macroprudential policy, which means that it tries to stabilise the market and make it more resilient using various instruments. It uses the loan to value ratio, or LTV for short, to influence the credit channel. This determines how much money a household can borrow if it takes out a mortgage on a property. Different LTV ratios create one of the potential frictions in the markets.
Suppose a central bank decides to increase the money supply by lowering the interest rate. By doing so, it encourages households to take out loans in a given period because they are cheaper. If we add to this decision a macroprudential decision to increase the LTV, we’re able to bring about a state of affairs in which credit can be drawn down not only in greater quantities but also in greater volumes. In layman’s terms, if I want to take out a mortgage on a flat and decide to guarantee it, it will be easier for me if the bank lends me more and sets me up with cheaper repayments. We saw a similar combination of macro-prudential and monetary policy measures at the start of the pandemic. Loose credit conditions then led to a huge uptake in new mortgage loans.
No, in general, I don’t see friction as something negative. It’s a phenomenon closely linked to the functioning of markets. Something that exists and which will always exist. It’s like the example with the bicycle, you can get rid of the dirt and replace old parts with new ones every now and then, but you’ll never get the bike to perform at 100%. You just work with what is. In the case of economic markets, frictions will help certain economic actors at one moment and aggravate them at another – it depends on the situation.
However, when modelling, we should be aware of whether frictions are present or not in a given economy and be able to account for them. If, for example, the Czech National Bank’s forecasting team is able to correctly reflect the current set-up of the economy – including frictions – in its forecasts, then it’s also able to provide the Bank Board with good evidence in the form of alternative scenarios for the economy, depending on what decisions the Bank Board decides to make. This should then enable the Board to make a better decision on, for example, how to change interest rates to combat high inflation.
One of the most important results is that, under normal circumstances, monetary and macroprudential policy do not interact much. After all, it makes sense that if interest rates are fairly stable and I want to buy a million-dollar property, and I already have five hundred thousand available, then it doesn’t matter whether LTV ratios are high and the bank is willing to lend me up to nine hundred thousand, or they’re lower, and the bank is willing to lend me up to six hundred thousand. Either way, I’ll probably only borrow the five hundred thousand I need. But this may be different in periods when the economy is facing more significant fluctuations, and it is the macroprudential policy settings that can significantly strengthen or weaken the effects of monetary policy.
Vlastimil Reichel is a member of the research team working on quantitative methods and macroeconomic modelling at the Department of Economics at FEA MU. Currently, his main research focus is the predictive ability of dynamic stochastic general equilibrium (DSGE) models and estimating economic policy impacts in European countries.
With the end of the year approaching, we bring you a summary of the most interesting events and achievements in the field of science and research at ECON MUNI. Our scientists have been involved in projects with a direct impact on society, celebrated success in grant competitions and helped politicians and public administration to make better decisions through the new Policy Reports Series.
The Dean's Award for Outstanding Dissertation Thesis was awarded in September to Katarína Čellárová, a graduate of doctoral studies at ECON MUNI and an experimental economist. In her doctoral research she found, among other things, that exploitation is less common when people are aware that they may switch roles with the victim in the future. It also revealed that winners who sacrificed more to win felt they deserved a better reward.
