Unfortunately, this internship position is already fulfilled and therefore applications on this position will not be considered. Still interested in an internship position? Please look at our other opportunities.

With the recent re-emergence of the cryptocurrency market, major financial institutions have joined the playing field and are looking for opportunities. Banks, hedge funds and even governments are testing the waters, some with a bigger investment than others. Grayscale, the world’s largest cryptocurrency asset manager, now has over 20$ billion worth of cryptocurrencies under management. However, cryptocurrencies are considered assets almost completely separated from the mainstream financial instruments. Previous research done by VB Risk Advisory has shown how cryptocurrencies have no dynamic correlations with mainstream instruments and are unsuitable as hedging instruments (K. van Wissen, D. Venekamp). Now, VB Risk Advisory is looking to develop a trading strategy for cryptocurrencies. Common trading strategies need to be re-evaluated for their use when trading cryptocurrencies. Currently the most commonly used trading strategy for cryptocurrencies is “Buy and Hold”, is it possible to outperform this by active trading? The unprecedented volatility definitely provides plenty of opportunities.

The goal of this internship is to create and implement an active trading strategy that is able to outperform the “Buy and Hold” strategy over time. Previous research by Koker et al. (2020) used machine learning methods whereas research by Rohrbach et al. (2017) used momentum and trend following trading strategies. The aim is to build and improve upon these methods by using new/extended models and/or usage of different signals.

Unfortunately, this internship position is already fulfilled and therefore applications on this position will not be considered. Still interested in an internship position? Please look at our other opportunities.

These days, headline E-sports tournaments fill major stadiums and draw media audiences like any offline sporting event. While multiple games are entering the E-sports genre, the game with the largest audience currently is League of Legends. The American League of Legends (LCS) competition is up there with the likes of the MLS, NBA, and the NFL as the third most popular major sport league among 18-34 year olds in the US. Moreover, the 2019 League of Legends World Championship brought in more than 100 million viewers peaking at 44 million concurrent viewers.

However, the world of esports isn’t without controversy. With the rise in popularity come the same issues that have arrived in legacy sports, such as cheating, player exploitation, and even performance-enhancing drugs. In addition, E-sports have also opened the door to a new blend of disruption, with cyber-attacks becoming more frequent along with overloaded servers and dropped connections. Furthermore, E-sports attracts regulatory scrutiny such as Visa issues and the World Health Organization recently adding a gaming disorder to the International Classification of Diseases.

Despite these growing pains, the popularity of E-sports shows no sign of slowing down. As shown by the Global Games Market Report market research, which forecasts 2.3 billion players across the globe will spend a whopping $137.9 billion on games by the end of the year, with an increase of 13% from previous year. Following the massive influx of money, betting offices are turning more and more towards E-sport as well. According to research done by NJGames.org, the total amount bet on esports competitions is expected to hit $12.9 billion by 2020. Accompanying this is the increasing demand for proper betting quotes. This is where we come into play.

Unfortunately, this internship position is already fulfilled and therefore applications on this position will not be considered. Still interested in an internship position? Please look at our other opportunities.

One of the major research questions of the past decade is whether climatological data -such as temperatures- show evidence of global warming through the presence of upward trends. Several econometric tools have been applied to the analysis of temperature data. Recent econometric and statistical research on trends focuses on nonparametric trend estimation that avoids one to have to make assumptions on the form of the trend.

Typically such methods are applied to individual time series though, which ignores the “global” in “global” warming. One can construct a time series consisting of average global data, but by this aggregation a lot of information can be lost. It is better to consider a panel of time series for different locations, and use this panel to extract the common trend from the data. Atak et al. (2011), Chen et al. (2012) and Robinson (2012) have proposed a method to estimate a common deterministic trend nonparametrically from panel data. For such analysis to make sense, the trend observed in the different series should actually be the same. Zhang et al. (2012) address this issue by proposing a method for testing whether the trend is common to all series considered.

Unfortunately, this internship position is already fulfilled and therefore applications on this position will not be considered. Still interested in an internship position? Please look at our other opportunities.

Lower funding ratios driven by lower interest rates and increasing life expectancy have forced pension funds and life insurance companies globally to re-think the asset liability management of their pension portfolios and to follow a de-risking strategy. The hedging of longevity risk through longevity swaps is a key part of that strategy. In addition to traditional longevity swaps there are also instruments which are linked to a mortality index (such as the mortality coefficients for the Dutch population published by the Central Bureau of Statistics). These have the potential for standardization and create a deep and liquid market for longevity risk. A drawback of these instruments is the basis risk between the mortality index and the actual portfolio to be hedged.

The aim of this research project is to extend the work of Cairns and El Boukfaoui (2017) and internal research on basis risk to include commutation risk, replicating portfolio risk and model risk. We have started by implementing a stochastic 2-population mortality model which allows evaluation of the basis risk of an index hedge. We then hope to extend existing models for basis risk to include new risk types. Eventually, after understanding all types of basis risk, changes could be made to the index hedge.

Unfortunately, this internship position is already fulfilled and therefore applications on this position will not be considered. Still interested in an internship position? Please look at our other opportunities.

Risk management will become the most important asset in financial institutions, as risk management is respected all over the world for their critical role in the support and implementation of innovative business projects. The market for renewable energy is growing as more people make use of renewable energy. The majority of renewable energy is generated by wind mills. Hedging solutions for wind volume risk can be executed in either derivative or (re)insurance formats; the underlying mechanism is similar. Derivatives (options, swaps, futures) derive value from underlying assets. With wind derivatives, the “underlying” used in pricing and structuring, is often an index which models a wind farm’s output based on turbine power curves and historical wind speed data. However, measuring the actual fair value of the derivate is challenging as the underlying asset is not traded. As a result, the impact of wind values on derivatives values is often misunderstood. This research aims to measure the fair value of for wind derivatives via weather station data and to develop an innovative pricing model for pricing these type of derivatives.

Unfortunately, this internship position is already fulfilled and therefore applications on this position will not be considered. Still interested in an internship position? Please look at our other opportunities.

Social media continues to impact industries in profound ways. One such industry is the cryptocurrency market and its corresponding blockchain technology. Much of cryptocurrency growth and popularity can be attributed to social media. From the social fora of digital currency’s humble beginnings to mainstream social media channels, social media and cryptocurrency have a very close relationship. A known drawback of current social media is the increase of bots. Dealing with this is one of the challenges that you will experience.

The aim of this internship is to expand the study of Mai et. al, (2018) to altcoins on which sufficient information is available. Additionally, we want to forecast prices of cryptocurrencies using data on social media and econometric regression models. Examples of explanatory social media variables can be; activity, social media type, active followers, positive / negative reactions.

Unfortunately, this internship position is already fulfilled and therefore applications on this position will not be considered. Still interested in an internship position? Please look at our other opportunities.

The main aim of this internship is to establish robust forecast soccer results. By exploring risk-rewarding team strategies and using discrete choice regression models, e.g. an ordered probit model, a variety of quantitative and data-driven models could be explored to model results directly rather than indirectly through scores. Earlier applied statistics literature investigated primarily models related to goal scoring (Crowder et al., 2002), where the aim of this research will be narrowed to direct results.