Harald Kinateder

Basel II Versus III: A Comparative Assessment of Minimum Capital Requirements for Internal Model Approaches

This paper provides a comparative assessment of the minimum capital requirement (MCR) in three prominent versions of the Basel regulatory framework: Basel II, the 2010 version of Basel III and the 2013 version of Basel III. For this purpose, Cantelli’s inequality is used to compute theoretical MCR violation levels for different unconditional distributional specifications, accounting for skewness and kurtosis. Cantelli’s inequality allows one to perform a quantitative comparison of various Basel accords without exact knowledge of the future return process. Therefore, the results are not biased, due to the specific choice of the sample data and/or uncertainty about the underlying return process as well as the “true” risk model. The paper finds that under weak distributional specifications (i.e., normal tails), the MCR under the 2013 version of Basel III is only marginally higher than under Basel II. However, this difference increases (decreases) for risk models equipped with heavy-tailed (normal) innovations. In contrast to this, the paper documents that under the 2010 version of Basel III the MCR violation levels during a stress period are adequate, even when using a risk model with weak distributional specifications. However, the paper also shows that the MCR under the 2010 version of Basel III is too conservative in calm periods.

Quantitative Easing and the Pricing of EMU Sovereign Debt

We study the pricing of EMU sovereign debt by a novel panel regression approach. This allows us to consider a comprehensive set of observable explanatory variables jointly with additional unobservable time-varying common factors. We add to the existing literature by considering the pricing effects of conventional as well as unconventional monetary policy and by controlling for possible variance risk premium effects. During the European sovereign debt crisis, unconventional monetary policy is found to have a pronounced spread decreasing effect, where policy elasticity is about three times larger than prior to the crisis. Furthermore, a rise in the variance risk premium significantly relates to spread increases. During the overall sample period, changes in country-specific bond market liquidity as well as aggregate market liquidity both help to explain yield spread variations. Three time-varying common factors account for about two-thirds of the variation in yield spread changes. The major component is a systematic risk factor, which captures a time-varying risk premium. The remainder factors help to explain bond valuations prior to and during the debt crisis.

What Drives Tail Risk in Aggregate European Equity Markets

Purpose - – The paper aims to analyse the drivers of changes in European equity tail risk. Design/methodology/approach - – For this purpose, the paper uses a panel data model with fixed effects based on five explanatory variables including the VIX, the variance risk premium (VRP), the one-year lagged slope of the riskless term-structure, the default spread and market-specific illiquidity via the measure of Bao Findings - – There is empirical evidence that the VIX, the VRP and the default spread are key determinants of equity tail risk changes across all markets. Moreover, the results reveal that market-specific illiquidity is an important determinant in PIIGS markets and the one-year lagged term-structure slope in core markets. The analysis also documents that market-specific risk premia are a relevant determinant of equity tail risk changes. Another finding is that risk premia in PIIGS markets are basically higher as in core markets, which reflect the higher risk involved in investing in PIIGS markets. Originality/value - – The paper offers a unique perspective on equity tail risk in aggregate equity markets and helps both investors and risk managers to get a comprehensive understanding of relevant drivers.

Oil and Stock Market Returns: Direction, Volatility, or Liquidity?

We examine how oil market variables affect short-term stock market returns in the U.S. and in six other major oil-importing countries. Apart from oil price direction, we also consider oil market volatility and liquidity. Analysis of daily returns during the 2007 to 2017 period reveals that oil price direction has a significant positive effect on returns for all markets, which can be interpreted as evidence of oil as a business climate indicator. Furthermore, for the U.S. market, shocks to implied oil market volatility negatively affect stocks, this effect is significantly asymmetric, and declining oil market liquidity predicts declining stock prices.

VaR Prediction under Long Memory in Volatility

In their study on the applicability of volatility forecasting for risk management applications, [2] stress the importance of long-term volatility dependencies under longer forecast horizons. The present contribution addresses multiple-period value-at-risk (VaR) prediction for equity markets under long memory in return volatilities. We account for long memory in the τ-step ahead volatility forecast of GJR-GARCH(1,1) by using a novel estimator considering the slowly declining influence of past volatility shocks. Our empirical study of established equity markets covers daily index returns during the period 1975 to 2007. We study the out-of-sample accuracy of VaR predictions for five, ten, 20 and 60 trading days. As a benchmark model we use the parametric GARCH setting of Drost and Nijman (1993) and the Cornish-Fisher expansion as an approximation to innovation quan-tiles. The backtesting results document that our novel approach improves forecasts remarkably. This outperformance is only in part due to higher levels of risk forecasts. Even after controlling for the unconditional VaR levels of the competing approaches, the long memory GJR-GARCH(1,1) approach delivers results which are not dominated by the benchmark approach.

Can Investors Hedge Energy Risk? Evidence from Asia

The relationship between energy and stock prices is investigated in the context of Asia, including China and Japan. Oil, gas and coal prices are considered both individually and in an energy portfolio. Consistent with evidence from analysis of other asset prices in international markets, during the post Global Financial Crisis (GFC) period, Asian stock markets moved in tandem with oil prices. However, using asset pricing and portfolio theory we identify time-varying integration between individual stock markets and the energy portfolio, which in turn may limit the benefit of risk reduction through diversification. However, this relation can also be used to hedge the common factor arising from energy risk. Doing so provides benefits to investors in the form of positive risk adjusted returns, although these are episodic.

Managing COP21 Using a Stock and Oil Market Integration Index

COP21 implementation should lead to a decline in the future demand for fossil fuels. One key implication for investors is how to manage this risk. We construct a monthly stock and oil market integration index and demonstrate that oil investors can offset adverse oil price risk by holding diversified global stock portfolios. The portfolios are formed from eight different combinations of developed and emerging stock markets. We show that measuring the degree of stock-oil market integration is critical to managing the time-varying degrees of integration. Under normal market conditions markets are segmented and this yields the opportunity for oil investors to diversify energy price risk through the purchase of stocks. The optimal oil-stock diversified portfolio provides risk-adjusted positive benefits to investors, with portfolio weights changing over time as COP21 implementation proceeds.

Aktuelle Herausforderungen des modernen Finanzcontrollings

Der Begriff des Finanzcontrollings umfasst alle Maßnahmen zur Koordination innerhalb des Finanzbereichs sowie zwischen dem Finanzbereich und der Unternehmensleitung. In der Summe zielt das Finanzcontrolling auf die Aufrechterhaltung der Zahlungsfähigkeit des Unternehmens und die Maximierung des Wertes der Eigenkapitalansprüche ab. Neben dem Kernziel der Profitabilität bilden damit das Risikocontrolling und das Liquiditätscontrolling den Kern des Finanzcontrollings (vgl. z. B. auch die Definition von Mensch, 2008, S. 7). Spezielle Kompetenzfelder betreffen die Analyse von Marktund Kreditrisiken, das Feld operativer Risiken sowie die Bereiche der Anlageund Finanzoptimierung. Die Adressaten des Finanzcontrollings umfassen insbesondere Unternehmen aus allen Branchen einschließlich der Finanzdienstleistung, aber auch öffentliche und private Haushalte.