Contributions published at Quantitative Finance (Erich Walter Farkas)

In this paper, we propose an easy-to-use yet comprehensive model for a system of cointegrated commodity prices. While retaining the exponential affine structure of previous approaches, our model allows for an arbitrary number of cointegration relationships. We show that the cointegration component allows capturing well-known features of commodity prices, i.e., upward sloping (contango) and downward sloping (backwardation) term-structures, smaller volatilities for longer maturities and an upward sloping correlation term structure. The model is calibrated to futures price data of ten commodities. The results provide compelling evidence of cointegration in the data. Implications for the prices of futures and options written on common commodity spreads (e.g., spark spread and crack spread) are thoroughly investigated.

International investment is a natural approach for any investor seeking diversification and potentially improved portfolio performance. Consequently, the foreign currency exposures need to be appropriately considered. We show that full hedging is not suitable and develop a model for finding the optimal hedging of FX exposure, minimizing an arbitrary risk measure (volatility, semivolatility, value at risk, expected shortfall etc.) in a general portfolio setting. Explicit formulas and quadratic programming methods based on the portfolio return variance-covariance structure are derived in order to minimize the volatility of returns and the existence and uniqueness of the solution are proved. For the minimization of a general risk measure, different Monte Carlo sampling techniques are used to generate the future empirical returns and numerical methods are applied to optimize the risk measures. A hedging strategy generalizing those methods into a dynamic and multi period setting is proposed and a back test on the historical market data is performed, showing that the derived approaches yield a robust, cost eficient and risk reductive hedging strategy. Key

Is the American put option in the Black-Scholes model simply an incognito European one? In this paper, we develop a numerical procedure, in the context of the Black-Scholes model, to approximate the payoff of a European type option that generates prices that are equal to the prices of the American put option in the continuation region. The resulting equivalent European payoff is a sum of power payoffs and therefore the price and the hedging indicators can be computed in closed form. For a given set of model parameters (interest rate, dividend rate and volatility) the computation of the equivalent European payoff reduces to solving a linear optimization problem. We conduct a numerical experiment spanning a wide range of model parameters and contract characteristics and, overall, the method produces American option prices with a relative RMSE less than 0.01% compared to a benchmark.

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Monetary risk measures are usually interpreted as the smallest amount of external capital that must be added to a financial position to make it acceptable. We propose a new concept: intrinsic risk measures and argue that this approach provides a direct path from unacceptable positions towards the acceptance set. Intrinsic risk measures use only internal resources and return the smallest percentage of the currently held financial position which has to be sold and reinvested into an eligible asset such that the resulting position becomes acceptable. While avoiding the problem of infinite values, intrinsic risk measures allow a free choice of the eligible asset and they preserve desired properties such as monotonicity and quasi-convexity. A dual representation on convex acceptance sets is derived and the link of intrinsic risk measures to their monetary counterparts on cones is detailed.

In this a paper a non-linear macro stress testing methodology with focus on early warning is developed. The methodology builds on a variant of Random Forests and its proximity measures. It is embedded in a framework, in which naturally defined contagion and feedback effects transfer the impact of stressing a relatively small part of the observations on the whole dataset, allowing to estimate a stressed future state. It will be shown that contagion can be directly derived from the proximities while iterating the proximity based contagion leads to naturally defined feedback effects. Since the methodology is Random Forests based the framework can be estimated on large numbers of risk indicators up to big data dimensions, fostering the stability of the results while reducing inaccuracies in estimated stress scenarios by only stressing a small part of the observations. This procedure allows accurate forecasting of events under stress and the emergence of a potential macro crisis. The framework also estimates a set of the most influential economic indicators leading to the potential crisis, which can then be used as indications of remediation or prevention.

Previous research uses positive and negative word-lists to quantitatively measure the general tone of financial articles and study the effect of these sentiment variables on financial time series. This paper introduces a novelty feature construction method of financial documents based on natural language processing techniques and appraisal theory from a psychological point of view. I find that uncertainty emotion expressed in news articles can reflect the stock trading volume of individual firms. Besides, sentiment features like surprise and uncertainty have significant influence on cumulative abnormal returns during a period after the earning announcement day. These features give valuable information of the post-earnings-announcement drift by associate the drift direction with emotions expressed in financial articles. Keywords: Financial Textual Analysis, Post-Earnings-Announcement Drift, Appraisal Theory, Natural Language Processing, Sentiment Analysis.

The role of a Central Counter-Party (CCP) in the aftermath of the Global Financial Crisis (GFC) in 2008 is studied in relation to the con ict of interests with their clearing members, the repo markets and the alignment of risk management practises with desired policy goals. Historic development of CCP's and securitization is presented on which we build a conceptual interpretation for the GFC and it's aftermath, including the response of central banks as the second major policy shift. The concepts of liquidity, leverage and informational-insensitivity are explored as the basis for this interpretation. We argue that the same structural deciencies which have led to the GFC are now being institutionalized in a form of a CCP. In addition, the similarities of a CCP with a credit rating agency and a central bank are presented to substantiate our concerns on the robust stability of such a form of systemic risk stabilizers. As an interesting outgrowth of our research the natural interpretation for the currently present negative interest rates is presented along with possible consequences for the developments of nancial markets looking through the prism of the above listed fundamental concepts.

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We will hear how a global bank from a corporate side and a leading wealth manager from a private banking side will see the risks and challenges the Swiss financial market and its participants faces in 2016 and beyond. Ms. Christine Novakovic and Mr. Barend Fruithof will present to the audience their views followed with an interesting Panel moderated by PWC and finished with a networking cocktail.

Abstract In a series of interviews, this master thesis investigates different risk classification approaches of structured products applied in several European countries and provides an analysis of potential shortcomings. Particularly, the focus is put on the countries Germany, Switzerland and the UK. However, the existing directive for Undertakings for Collective Investment in Transferable Securities (UCITS) and the currently discussed regulatory framework for Packaged Retail Investments and Insurance-based Products (PRIIPs) are also examined in order to provide a broad comparison. The results hint to the Value at Risk representing the most appropriate risk figure. It should be calculated by using the historical simulation with a 99% confidence interval, a holding period of 10 days, daily returns and an observation period of five years to properly account for the market risk and to ensure comprehensibility and comparability of different structured products. Moreover, the results reveal that an optimal risk classification methodology should incorporate seven risk classes with flexible boundaries. It should not only disclose the market risk but also the credit and liquidity risk, whereby for the two latter a qualitative risk rating seems most appropriate. The research raises awareness of enhancing the transparency with regard to the risk disclosure of structured products in order to effectively manage risk and to help investors with their investment decisions

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