Programme Risk Day 2019

Abstracts

Marloes Maathuis
Causality: theory and applications

Causal questions are fundamental in all parts of science. Answering such questions from non-experimental data is notoriously difficult, but there has been a lot of recent interest and progress in this field. I will discuss current approaches to this problem and outline their potential as well as their limitations. The concepts and methods will be illustrated by several examples.

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Carole Bernard
Option implied dependence and the correlation risk premium

We propose a new model-free method to obtain the full joint risk-neutral distribution among assets that is consistent with all option prices on these assets and on indices involving these assets. In the empirical application, we implement our approach using options on the S&P 500 index and its nine sectors. We compare it with parametric approaches based on multivariate Gaussian or skewed Gaussian and test for asymmetry. In particular, we find that the option-implied dependence is highly non-normal, asymmetric and time-varying. Using the estimated dependence, we then study the correlation conditional on the market going down or up. We find that the risk premium for the down correlation is strongly negative, whereas it is positive for the up correlation. These findings are consistent with the economic intuition that the investors are particularly concerned with the loss of diversification when financial markets fall. As a result, they are willing to pay a considerable premium to hedge against increases in correlation during turbulent times. However, the investors actually prefer high correlation when markets rally.

This is joint work with Oleg Bondarenko (University of Illinois at Chicago).

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Sebastian Ebert
Skewness preferences – the human attitudes toward rare, high-impact risks

I review empirical evidence on skewness preferences – the human attitudes toward rare, high impact risks – from various fields of economics, finance, and insurance. The leading normative model of decision-making under risk, expected utility theory, has trouble with accommodating this evidence. I argue that much of the descriptive success of behavioral theories of decision-making under risk (e.g., prospect theory, regret theory, salience theory) roots in the fact that they induce a strong sensitivity to skewness. To support this view, I present formal results that compare the skewness sensitivity induced by leading theories of choice under risk. While behavioral theories, even though having different psychological and mathematical foundations, share the property of strong sensitivity to skewness, expected utility theory does not.

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Birgit Rutishauser
Does financial supervision have to heat up?

The climate is changing, and without a change in the current emission path, research indicates that 3-4°C warming above pre-industrial levels by 2100 is most likely. The later the transition to a lower carbon economy takes place, the more likely an abrupt turnaround in climate policy becomes. For the financial sector, changes in the climate entail physical risks, transition risks and liability risks. The question how these risks can be identified, disclosed, measured and controlled is increasingly discussed among the industry and regulators and is of great relevance to FINMA. Insurance companies are exposed on the liability as well as on the asset side to climate change risks. The SST and Solvency II in the EU aims to assess the financial health of insurance companies by quantifying capital adequacy based on a risk evaluation of the market consistent balance sheet modelled after one year. Related to that many immediate questions arise. E.g. how can risks from climate change be reflected in the capital requirements of insurance companies? How likely is an abrupt turnaround and what would be the consequences for the financial sector? Depending on the concrete insurance products and investments in green assets the development and also the supervision of suitable models could become particularly challenging, possibly being related to substantial model and parameter risks. Hence, FINMA is confronted with many tricky questions and challenges, which could be the topic of further highly relevant applied research. Corresponding results could pave the way to decisions which are in the best interest of policy-holders and investors and at the same time ensure that financial markets function properly.

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Jürg Schelldorfer
Recent achievements and perspectives in actuarial data science

In 2017, the Swiss Association of Actuaries (SAA) founded the working group "Data Science" with the mandate to contribute to a better understanding of using machine learning techniques for the statistical modelling of actuarial tasks (external pageWebsitecall_made).
In the first part of the talk, I discuss the latest achievements in actuarial modelling of machine learning, focusing on the topics with contributions from the working group. In the second part, I present an outlook on where the field of actuarial data science should or will develop. In the last part, I address some non-quantitative aspects in the area of actuarial data science.

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Martin Burgherr
How tokenization is redefining financial markets

Sygnum授权机构和私人qualified investors, corporates, banks and other financial institutions to invest in the digital asset economy with complete trust. The presentation looks at the broad spectrum of assets that can be tokenized, and the trusted, scalable and regulatory compliant infrastructure required to realise the potential of the digital asset economy.

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Christian Bluhm
Transforming the risk function in a rapidly transforming financial industry

The financial industry currently undergoes rapid change and transformation due to technological evolution, significant regulatory and cost pressure as well as challenging markets with high interdependencies and geopolitical instability. In order to keep pace with the industry, the risk function in banks and insurance companies has to rapidly change too, including the adoption of automation techniques, embracing new technologies like artificial intelligence and machine learning, paying even more attention to risk culture, talent retention and people development and, in general, taking risk management to the next level. Since new approaches and a transforming industry also incorporate new risks, enhanced focus on emerging risks and challenges is required. The presentation outlines UBS' current journey in an elevated world of risks and chances.

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