The financial markets did not have a good 2018 as the media kept on reminding us:

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The riskiest year in human history was 1962. The year of the Cuban missile crisis, the closest we ever came to a nuclear war. The mother of all tail events, where all prices go to zero. Volatility that year was average — 16.5%

How can market risk be average when tail risk is at its highest?

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*Perceived risk* is risk predicted by models and *actual risk* is the fundamental underlying risk. We measure perceived risk and care about actual risk. Unfortunately, those two are negatively correlated.

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We compare the suitability of the four most used numerical languages, Julia, MATLAB, Python or R, for the type of economic and financial research we do. R remains best, but Julia has the highest potential.

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One can endlessly criticise risk models, but that is just too nihilistic. So, what are the good for? There are three camps, the model believers, the rejectionists and the healthy skeptics. I’m going to make the case for the last below.

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Medieval mapmakers noted the risk of an unknown kind by “here be dragons”. Attempts at measuring extreme risk should come with a similar warning. Just like the sailors of yesteryear, financial institutions will go into unknown territories and, just like the map makers of the earlier era, modern risk modellers have little to say.

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Reliable indicators of future financial crises are important for policymakers and practitioners. While most indicators consider an observation of high volatility as a warning signal, this column argues that such an alarm comes too late, arriving only once a crisis is already under way. A better warning is provided by low volatility, which is a reliable indication of an increased likelihood of a future crisis.

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The stock market had a mini crash yesterday. So how big was that in a historical context?

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The European Central Bank has an indicator of systemic risk called the Composite Indicator of Systemic Stress , CISS. So what sort of signal does it send and what is it to be used for?

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Suppose one cares about tail risk, what is the best way to estimate it? There are two, not mutually exclusive, ways; *statistical* and *structural*. Which is right?

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Why do the regulatory authorities seemingly fall into the category of model believers, if not quite to the view that there must be one true model? Well, it is sort of inevitable the way the regulatory process works.

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There a lot of evidence that models are less than perfectly reliable. Why then do we rely so much on models in decision-making, and especially financial regulations? Because there are three types of people: Believers in true model, skeptics who accept model risk and nihilistic rejectionists.

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When designing models, the underlying assumption is often that the model captures the true data generating process. Does a true model exist? To me, the question is completely irrelevant.

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Last January I looked at how the Swiss FX shock affected the most popular risk measures. Events of the past week give us another interesting test. My daily risk forecast shows the various risk measures for a number of assets, but focus on the SP-500, and the following picture taken from the site today:

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**May 14, 2015**

Bloomberg today had an interesting piece, called Market Moves That Are Supposed to Happen Every Half-Decade Keep Happening. Here is their self-described “terribly simplistic list”

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So, does ES capture tail risk, but VaR not? Therefore the Basel committee is correct, and we all should use ES. Is that true?

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Regulators and financial institutions increasingly depend on statistical risk forecasting. This column argues that most risk modelling approaches are highly inaccurate and confidence intervals should be provided along with point estimates. Two major approaches, value-at-risk and expected shortfall are compared, and while the former is found to be superior in practice, it is also easier to be manipulated by forecasters.åÊ

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Just looked again at the what I did on the Swiss FX shock, looking at how the various risk measures performed in the days after the event, and also looking at the risk of the inverse FX.

The original analysis just looked at the risk of the Franc appreciating, but why not look at the risk of the euro appreciating.

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The Swiss central bank last week abandoned its euro exchange rate ceiling. This column argues that the fallout from the decision demonstrates the inherent weaknesses of the regulator-approved standard risk models used in financial institutions. These models under-forecast risk before the announcement and over-forecast risk after the announcement, getting it wrong in all states of the world.

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Risk forecasting is central to financial regulations, risk management, and macroprudential policy. This column raises concerns about the reliance on risk forecasting, since risk forecast models have high levels of model risk - especially when the models are needed the most, during crises. Policymakers should be wary of relying solely on such models. Formal model-risk analysis should be a part of the regulatory design process.

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Basel III is coming into focus. The fundamental logic of the regulatory changes seems sensible, but the devil is in the detail - empirical implementation. This column discusses a detailed quantitative study, incorporating analytical calculations, Monte Carlo simulations and results from observed data. It concludes that the Basel Committee has taken three and a half steps backwards and half a step forward. If implemented, the framework is likely to lead to less robust risk forecasts than current methodologies.

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Financial risk models have been widely criticised for both theoretical and practical failures, especially during the recent financial crisis. In the second of two columns, the authors outline why the shortcomings of risk models matter before making suggestions for how the financial industry and supervisors should use models in practice.

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Risk models are at the heart of the financial sector’s self-monitoring as well as supervision by regulators. This column, the first of two, addresses the question of how risk models are misused in practice by practitioners and supervisors alike. This misuse causes risk management to fail when it is most needed.

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By incorporating endogenous risk into a standard asset-pricing model, this column shows how banks’ capacity to bear risk seemingly evaporates in the face of market turmoil, pushing the financial system further into a tailspin. It suggests that risk-sensitive prudential regulation, in the spirit of Basel II, makes systemic financial crises sharper, larger, and more costly.

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Much of today’s financial regulation assumes that risk can be accurately measured so that financial engineers, like civil engineers, can design safe products with sophisticated maths informed by historical estimates. But, as the crisis has shown, the laws of finance react to financial engineers’ creations, rendering risk calculations invalid. Regulators should rely on simpler methods.

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In response to financial turmoil, supervisors are demanding more risk calculations. But model-driven mispricing produced the crisis, and risk models don’t perform during crisis conditions. The belief that a really complicated statistical model must be right is merely foolish sophistication.

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© All rights reserved, Jon Danielsson, 2019