Central-bank digital currencies will make the financial system more efficient. But will they make it safer? Maybe, but they could easily end up increasing systemic risk. Depends on the implementation.
As a part of its quest for independence, Britain decided to leave the Erasmus program. We now have Turing, the global version. So, will it lead us to the sunny uplands?
The Brexiteers and Karl Marx have more in common than often thought. Both were guided by ideology, and both refused to tell us how to get to the sunny uplands they promised.
Financial crises and bailouts of financial institutions are inevitable and can’t be prevented without paying too high a price. Diversity is the best way to minimise the frequency and severity of crises and ensure sustainable high economic growth.
Are cryptocurrencies and blockchains the solution to the problem laid bare by the Covid-19 bailout of the financial system? No.
A libertarian sees the Covid-19 bailout of the financial system as a predictable failure of regulations. Much better to have a laissez-faire economy and never, ever bail private firms out. But does the laissez-faire utopia survive contact with reality?
The state just saved the financial system from itself. What is the point of privately owned banks if they need to be bailed out every decade?
The financial authorities have just bailed the financial system out for the second time in a decade. While the authorities are proud of having prevented a financial crisis, are we really better off? No, one cannot judge the policy intervention a success if it is only due to the promise of the intervention that the problem arose in the first place.
The central banks bailed out the financial system in March 2020, the second time they have done so in 12 years. What is the point of privately owned banks if they require a bailout every decade?
The most widely used programming languages for economic research are Julia, Matlab, Python and R. This column uses three criteria to compare the languages: the power of available libraries, the speed and possibilities when handling large datasets, and the speed and ease-of-use for a computationally intensive task. While R is still a good choice, Julia is the language the authors now tend to pick for new projects and generally recommend.
Does it make sense to invest in low vol funds?
I just tried the same code in R’s data.tables and Julia’s DataFrames, and the results are a bit surprising.
It is easy to manipulate risk forecasts. If your regulator or compliance officer sets a risk target you don’t like, just tell them what they want to hear and continue taking the risk you like.
It is easy to criticise risk forecasting, but it’s rather pointless unless one can come up with proposals. Here are my five principles for the correct use of riskometers.
Backtesting is the magical technique that tells us how well a forecast model works. Test the model on history, and we have an objective way to evaluate how good the model is. But does it really work in practice?
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