Models and Risk - Blog

The prevailing view seems to be that financial risk is a fate not a choice.

The way financial risk is measured is by a device I have called the riskometer.

Cryptocurrencies have enjoyed a remarkable run from obscurity to a trillion dollar valuation in just over a decade.

It is easy to manipulate risk forecasts.

Perceived risk is risk predicted by models, and actual risk is the fundamental underlying risk.

Global macroprudential and microprudential regulatory changes since 2008 have been designed to make a repeat of a systemic crisis much less likely.

We missed the excessive build-up of risk before 2007 despite having all the numbers and models right in front of us.

Seldom.

Systemic risk and climate change strongly affect each other.

Democracy works.

I have long argued that risk, especially extreme risk, cannot be measured.

Financial regulations are vital.

The perceived wisdom maintains that systemic risk was much higher in 2008 than it is today.

The risk we measure is usually not the risk we care about.

Economic researchers can choose between four general purpose programming languages – Julia, MATLAB, Python, and R.

I just read "Nuclear folly, a new history of the Cuban missile crisis" by Serhii Plokhy.

A lot of turmoil in crypto.

I ran a poll on LinkedIn and Twitter, asking who is to blame for the rising systemic risk.

Bitcoin is often sold as protection against adverse macroeconomic outcomes.

Terrible events dominate the news.

The way the financial authorities address threats to financial stability is by resilience.

The cryptocurrency exchanges have only done what is legally required of them when sanctioning Russia for its invasion of Ukraine, unlike the mainstream financial institutions whose restrictions on the Russians generally exceeds what is required by law.

The Western countries have sanctioned Russia in a way not applied to any globally integrated major power in over a century, ever since 1914.

The relationship between financial risk and economic growth is complex.

Inflation is exploding.

Half a decade after the Brexit vote, it is clear that the classical liberal intellectual case for Brexit has a fatal flaw.

How far into the future should we be able to execute computer code written today?

A common misconception maintains that different notions of risk capture the same concept: VaR, ES and volatility all measure the same "true" risk".

Financial risk management has become ritualised.

Do all the talented engineers working on cryptocurrencies ensure crypto success?

There are many measurements of systemic risk and a huge number of papers depend on those measurements.

Risk measurements are quite inconsistent.

As the price of bitcoin continues to rise, this column argues that most of us would not want to live in a society where bitcoin succeeds.

There are a million ways to measure financial risk, and if one wants to calculate global risk, it requires a truly heroic set of assumptions.

As Amazon continues its exponential growth, where will it all end?

The new Apple M1 processor has gotten excellent reviews for speed.

While the direct economic consequences of Covid-19 have been significant, the impact on the financial markets has been more nuanced.

Central-bank digital currencies will make the financial system more efficient.

As a part of its quest for independence, Britain decided to leave the Erasmus program.

The Brexiteers and Karl Marx have more in common than often thought.

Financial crises and bailouts of financial institutions are inevitable and can't be prevented without paying too high a price.

Are cryptocurrencies and blockchains the solution to the problem laid bare by the Covid-19 bailout of the financial system?

A libertarian sees the Covid-19 bailout of the financial system as a predictable failure of regulations.

The state just saved the financial system from itself.

The financial authorities have just bailed the financial system out for the second time in a decade.

The central banks bailed out the financial system in March 2020, the second time they have done so in 12 years.

The most widely used programming languages for economic research are Julia, Matlab, Python and R.

Amazon AWS has been recommending ARM Graviton2 as a cost-effective alternative to Intel/AMD instances.

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.