We are driven by science with purpose, pushing the academic frontier by publishing at the highest level in the top international journals in our field and collaborating with outside partners to link academic advancement with real world problems.
For a full list of our publications, see the VU Research Portal.
There are three research groups within our department:
- Mathematical Economics
- Operations Research
The department participates in the Tinbergen Institute, one of Europe's leading graduate schools and research institutes in economics, econometrics and finance.
Can a central bank *lower* its balance sheet by *extending* the scope of its operations?
During the euro area sovereign debt crisis between 2010 and 2012, severe liquidity squeezes and market malfunctions forced the European Central Bank (ECB) and its 17 national central banks at the time, to act as a lender of last resort to the entire financial system. In addition, the Eurosystem also acted as an investor-of-last-resort in stressed markets, for example when it purchased government bonds in illiquid secondary markets within its Securities Markets Programme (SMP) between 2010 and 2012. This prompts the questions: Can central bank liquidity provision or asset purchases during a financial crisis reduce risk in net terms? This could happen if risk taking in one part of the balance sheet (e.g., more asset purchases) de-risks other balance sheet positions (e.g., the collateralized lending portfolio) by an equal or even larger amount. How economically important can such risk spillovers be across policy operations? And were the Eurosystem's financial buffers at all times sufficiently high to match its portfolio tail risks? The authors address these questions by studying monetary policy exposures taken from the Eurosystem's weekly consolidated balance sheet between 2009 and 2015.
How to best create structure in more than 1500 stocks
Why does a child play with a toy car, while most grown-ups fancy a real car instead? Obvious as this may seem, most academics play around with financial models involving a handful of asset prices (or more recently "large" dimensions up to 100), whereas real asset managers have to manage asset portfolios with more than a 1,000 asset prices. Are academics like children, and is it maybe time they grow up and study financial phenomena at the scale that is empirically more relevant?
Can the strong persistence in (realized) dependence for stock prices help for forecasting at longer horizons?
Stock prices typically move together. For instance, two stock prices can be affected similarly by common news about the industry they are in, or about the local or global economy. Different methods have been proposed to describe this time variation. A recent method that appears to work well in many settings exploits stock price variations within the day: for instance, every minute, or even more frequent.
Forecasting crash numbers on German roads using meteorological variables
At the end of each year, the German Federal Highway Research Institute (BASt) publishes the road safety balance of the closing year. They describe the development of accident and casualty numbers dis-aggregated by road user types, age groups, type of road and the consequences of the accidents. However, at the time of publishing, these series are only available for the first eight or nine months of the year. To make the balance for the whole year, the last three or four months are forecasted. In this study the accuracy of these forecasts is improved by applying structural time series models that include effects of meteorological conditions.
What are the rankings of over 500 ATP tennis match players on hard court, clay and grass surfaces?
It is widely acknowledged in the literature that time variation in the strength level of tennis players is one of the key ingredients to properly describe the outcome of tennis matches. The strength of a player typically increases from a young age and reaches a certain peak when he/she is in his/her twenties, followed by a decline until he/she ends his/her career. However, in all studies so far time variation has not been modeled explicitly by means of a fully specified probability measure for the outcome of a tennis match at some time period. Given that the outcome of a match relies mainly on the abilities of the two players, it is necessary to model the strength of each player explicitly. Furthermore, since the strength of a player can vary considerably with the court surface type, the model also needs to identify strength levels for different surfaces. In this paper 17 years of ATP (Association of Tennis Professionals) tennis matches for a panel of over 500 players is analyzed, and it is found that time varying player-specific abilities for different court surfaces are of key importance for analyzing the matches.
The illusive wisdom of crowds: Why the many are not smarter than the few
What is an optimal communication architecture for finding out the ‘truth’? Consider, for instance, people trying to estimate the reliability of a product or whether some news items are fake. Here, everyone has some individual information that forms some individual belief, but nobody knows for sure what the truth is. Now assume people talk such that, over time, this belief gets updated by communication in the social network. What kind of social networks generate consensus, i.e. all people ending up with the same beliefs? If there is consensus, is it the truth? In other words, for what social networks can we observe wisdom of the crowds?
Speedy dimensionality reduction without tossing coins
Often we are faced with data with a large number of attributes ("dimensions"); think, for example, of stock prices where for each stock, we have the price at many different times. While it may seem that more data is always better, it brings with it many algorithmic challenges, colloquially referred to as the "curse of dimensionality". An important tool to deal with these difficulties in many applications is to reduce the dimensionality of the problem while preserving the key information in the data as well as possible. Improving our algorithms for such "dimensionality reduction" is an important area of study.
How to improve the long-lead predictability of El Niño
El Niño (EN) is a dominant feature of climate variability driving changes in the climate throughout the globe, and having wide-spread natural and socioeconomic consequences. Its forecast is therefore an important task, and predictions are issued on a regular basis by a wide array of prediction schemes and climate centers around the world. This study explores a novel and improved method for EN forecasting.
Making good peer groups for banks when the world moves fast: a marriage of financial econometrics and machine learning
How to identify the apple, the orange, and the banana if the juggler tosses everything around at high speed? Or differently, how to identify which groups of banks and other financial institutions are alike in a situation where new regulation kicks in fast, fintech companies are rapidly changing the scene, and central banks are implementing non-standard policies and keep interest rates uncommonly low for uncannily long? In this paper the authors investigate how to come up with groups of peer banks in such a volatile environment. Identifying peer groups is extremely important for regulators to create a level playing field. Similar banks should be charged similar capital buffers to safeguard the financial system and to retain fair competition at the same time.
Do negative interest rates make banks safe?
The European Central Bank has implemented a number of unconventional monetary policies since the financial crisis and the subsequent sovereign debt crisis. One of the policies involves setting the official rate at which banks can park money at the ECB close to zero, or even below zero.
Was this a wise decision?
Understanding the time-varying dynamics of traffic equilibria
Modelling traffic congestion and understanding its behaviour is a challenging, multidisciplinary endeavour. One approach is to look at simplified, abstracted models of traffic, and to use mathematical tools from game theory and optimization to obtain insights that still hold in the more complicated reality. For example, Braess's paradox says that in some situations, closing a road can improve the traffic situation! A counter-intuitive result that was then investigated and confirmed in reality.