Modeling Risk in an Unstable Environment

The global economy has been affected the last two decades with severe crashes in the financial system. The 1997-1998 Asian financial crisis, the 1998-2000 US dotcom bubble, the global financial crisis of 2008, the Chinese stock market bubble, and Brexit all show that the economy is subject to large and sudden changes in regimes. A distinctive feature of crashes is that these are episodes during which previously unrelated financial markets can almost overnight become extremely dependent. This so called systemic risk makes the global economy very risky for all stakeholders. A better understanding of such episodes of the economy is hence of primary importance. We develop tools that explicitly take into account the sometimes abruptly changing nature of the economic environment and improve our understanding of crisis periods.

Price of volatility risk project

The variation over time of the magnitude of price movements of financial assets (variance risk) represents a source of uncertainty that agents are subject to. Consistently, risk adverse agents should  require a compensation for bearing the randomness of future variance, i.e., a variance risk premium. Despite the number of empirical studies, there is no clear consensus about sign and magnitude of the premium and its linkages with the economy. We propose a new way of measuring the variance risk premium using a new model which describes clearly the link between physical and risk-neutral variance.

Breakmetrics project

The general objective of this proposal is to improve economic forecasts with the help of evolutionary econometric models, i.e. models that adapt to abrupt (or structural) changes in the economic environment, also called structural break or change-point models. The need for adaptive modelling is obvious in the light of the current economic conditions. Economic forecasting is essential for decision making with respect to fiscal and monetary policy, public spending, and investment. For example, the monetary transmission mechanism has long and uncertain lags. Therefore, monetary policy should be forward-looking. To effectively ensure price stability, the central bank for example needs to make forecasts about the evolution of prices and output among others. When a structural break happens, conventional forecasting models can produce severe forecast errors since the forecasts do not comply with the new environment as given by the new data.


Support for ESOBE conference

Labex MME-DII supports the ESOBE conference organized in November 2014

Semester in Financial Econometrics

Joint funding has been obtained for a variety of conferences, workshops and courses on financial econometrics. Partners are CREST, Dauphine,  ENSAE, ESSEC, IdR QMI, Université d’Orléans, Université d’Aix-Marseille.

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