An AI approach to measuring financial risk

AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (lambda) of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly traded financial institutions. We demonstrate the suitability of this AI based risk measure by comparing the proposed FRM to other measures for systemic risk, such as VIX, SRISK and Google Trends. We find that mutual Granger causality exists between the FRM and these measures, which indicates the validity of the FRM as a systemic risk measure. The implementation of this project is carried out using parallel computing, the codes are published on www.quantlet.de with keyword FRM. The R package RiskAnalytics is another tool with the purpose of integrating and facilitating the research, calculation and analysis methods around the FRM project. The visualization and the up-to-date FRM can be found on hu.berlin/frm

Volatility modelling of CO2 spot prices

In this paper we analyse the short-term spot price of European Union Allowances (EUAs), which is of particular importance in the transition of energy markets and for the development of new risk management strategies. We use daily spot market data from the second trading period of the EU ETS. Emphasis is given to short-term forecasting of prices and volatility. Due to the characteristics of the price process, such as volatility modelling, breaks in the volatility process and heavy-tailed distributions, we investigate the use of Markov switching GARCH (MS-GARCH) models. We find that these models distinguish well between states, and that the volatility processes in the states are clearly different. Our findings support the use of MS-GARCH models for risk management, especially because their forecasting ability is better than other Markov switching or simple GARCH models

Volatility Modelling of CO2 Emission Allowance Spot Prices with Regime-Switching GARCH Models

We analyse the short-term spot price of European Union Allowances (EUAs), which is of particular importance in the transition of energy markets and for the development of new risk management strategies. Due to the characteristics of the price process, such as volatility persistence, breaks in the volatility process and heavy-tailed distributions, we investigate the use of Markov switching GARCH (MS-GARCH) models on daily spot market data from the second trading period of the EU ETS. Emphasis is given to short-term forecasting of prices and volatility. We find that MS-GARCH models distinguish well between two states and that the volatility processes in the states are clearly different. This finding can be explained by the EU ETS design. Our results support the use of MS-GARCH models for risk management, especially because their forecasting ability is better than other Markov switching or simple GARCH models

Realized volatility of CO2 futures

The EU Emission Trading System (EU ETS) was created to reduce the CO2 and other greenhouse gas emissions at the lowest economic cost. In reality market participants are faced with considerable uncertainty due to price changes and require price and volatility estimates and forecasts for appropriate risk management, asset allocation and volatility trading. Although the simplest approach to estimate volatility is to use the historical standard deviation, realized volatility is a more accurate measure for volatility, since it is based on intraday data. Besides the stylized facts commonly observed in financial time series, we observe long-memory properties in the realized volatility series, which motivates the use of Heterogeneous Autoregressive (HAR) class models. Therefore, we propose to model and forecast the realized volatility of the EU ETS futures with HAR class models. The HAR models outperform benchmark models such as the standard long-memory ARFIMA model in terms of model fit, in-sample and out-of-sample forecasting. The analysis is based on intraday data (May 2007-April 2012) for futures on CO2 certificates for the second EU-ETS trading period (expiry December 2008-2012). The estimation results of the models allow to explain the volatility drivers in the market and volatility structure, according to the Heterogeneous Market Hypothesis as well as the observed asymmetries. We see that both speculators with short investment horizons as well as traders taking long-term hedging positions are active in the market. In a simulation study we test the suitability of the HAR model for option pricing and conclude that the HAR model is capable of mimicking the long-term volatility structure in the futures market and can be used for short-term and long-term option pricing

FRM: a Financial Risk Meter based on penalizing tail events occurrence

In this paper we propose a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (Lambda) of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly traded financial institutions. We demonstrate the suitability of this risk measure by comparing the proposed FRM to other measures for systemic risk, such as VIX, SRISK and Google Trends. We find that mutual Granger causality exists between the FRM and these measures, which indicates the validity of the FRM as a systemic risk measure. The implementation of this project is carried out using parallel computing, the codes are published on www.quantlet.de with keyword FRM. The R package RiskAnalytics is another tool with the purpose of integrating and facilitating the research, calculation and analysis methods around the FRM project. The visualization and the up-to-date FRM can be found on http://frm.wiwi.hu-berlin.de

Dutch Shell Companies and International Tax Planning

This paper uses the financial statements of special purpose entities (SPEs) for explaining the origin and destination of dividend, interest, and royalty flows passing the Netherlands. We find that Bermuda is the most important destination for royalty flows. These flows come from Ireland, Singapore and the United States. For dividend and interest payments the geographical pattern is more widespread. We find a substantial tax reduction for royalties by using Dutch SPEs compared to a direct flow between the origin and destination country. However, we cannot find such tax savings for dividends and interest with an approximation based on statutory tax rates. When controlling for country characteristics in our regression analysis we do find that tax differentials partially explain the geographical patterns of income flows diverted through the Netherlands. This is the case for the likelihood that a route is used, as well as for the size of the flows. This paper is one of the first using bilateral income flows as dependent variables instead of bilateral FDI stocks or flows