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on Risk Management |
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Issue of 2021‒09‒06
nine papers chosen by |
| By: | Busch, Pascal; Cappelletti, Giuseppe; Marincas, Vlad; Meller, Barbara; Wildmann, Nadya |
| Abstract: | The Basel Committee on Banking Supervision (BCBS) framework used to identify global systemically important banks (G-SIBs) is based on banks’ balance sheet information, leaving information derived from market data untapped. Among the most widely used market-based systemic risk measures, Adrian and Brunnermeier’s (2016) Delta-Conditional Value at Risk (ΔCoVaR) best captures the system-wide loss-given-default (sLGD) and conditional impact concepts underlying the BCBS GSIB methodology. In this paper we investigate, using a global sample of the largest banks, whether a score based on ΔCoVaR could be useful for ranking G-SIBs or for calibrating an alternative G-SIB indicator weighting scheme. In our first analysis we find that the ΔCoVaR score is positively correlated with all five of the systemic importance categories of the BCBS framework. However, considerable information/noise with regard to the ΔCoVaR score remains unexplained. Before more is known about this residual, a score based on ΔCoVaR is difficult to interpret and is inappropriate for identifying G-SIBs in a policy context. Besides, we find that a ranking based on ΔCoVaR is subject to substantial variability over time and across empirical specifications. In our second analysis we use ΔCoVaR to place the current static weighting scheme for G-SIB indicators on an empirical footing. To do this we regress ΔCoVaR on factors derived from the G-SIB indicators. This approach allows us to focus on the part of ΔCoVaR which can be explained by balance sheet information which alleviates the identified issues of interpretability and variability. The derived weights are highest for the cross-jurisdictional activity (43%) and size (27%) categories. We conclude that ΔCoVaR is not suitable for use as an alternative G-SIB score but could be useful for policymakers to pursue an empirically grounded weighting scheme for the existing G-SIB indicators. JEL Classification: G20, G21, G28 |
| Keywords: | bank regulation, global systemically important banks, systemic risk measures |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbops:2021260&r= |
| By: | \c{C}a\u{g}{\i}n Ararat; M\"ucahit Ayg\"un |
| Abstract: | Motivated by the problem of finding dual representations for quasiconvex systemic risk measures in financial mathematics, we study quasiconvex compositions in an abstract infinite-dimensional setting. We calculate an explicit formula for the penalty function of the composition in terms of the penalty functions of the ingredient functions. The proof makes use of a nonstandard minimax inequality (rather than equality as in the standard case) that is available in the literature. In the second part of the paper, we apply our results in concrete probabilistic settings for systemic risk measures, in particular, in the context of Eisenberg-Noe clearing model. We also provide novel economic interpretations of the dual representations in these settings. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2108.12910&r= |
| By: | Nguyen, Hoang (Örebro University School of Business); Javed, Farrukh (Örebro University School of Business) |
| Abstract: | Stock and bond are the two most crucial assets for portfolio allocation and risk management. This study proposes generalized autoregressive score mixed frequency data sampling (GAS MIDAS) copula models to analyze the dynamic dependence between stock returns and bond returns. A GAS MIDAS copula decomposes their relationship into a short-term dependence and a long-term dependence. While the long-term dependence is driven by related macro-finance factors using a MIDAS regression, the short-term effect follows a GAS process. Asymmetric dependence at different quantiles is also taken into account. We find that the proposed GAS MIDAS copula models are more effective in optimal portfolio allocation and improve the accuracy in risk management compared to other alternatives. |
| Keywords: | GAS copulas; MIDAS; asymmetry |
| JEL: | C32 C52 C58 G11 G12 |
| Date: | 2021–08–30 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:oruesi:2021_015&r= |
| By: | R.L. Gudmundarson; M. Guerra; A. B. de Moura |
| Abstract: | In this work the ruin probability of the Lundberg risk process is used as a criterion for determining the optimal security loading of premia in the presence of price-sensitive demand for insurance. Both single and aggregated claim processes are considered and the independent and the dependent cases are analyzed. For the single-risk case, we show that the optimal loading does not depend on the initial reserve. In the multiple risk case we account for arbitrary dependency structures between different risks and for dependencies between the probabilities of a client acquiring policies for different risks. In this case, the optimal loadings depend on the initial reserve. In all cases the loadings minimizing the ruin probability do not coincide with the loadings maximizing the expected profit. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:ise:remwps:wp01932021&r= |
| By: | Marlon Moresco; Marcelo Brutti Righi |
| Abstract: | Recently, Castagnoli et al. (2021) introduce the class of star-shaped risk measures as a generalization of convex and coherent ones, proving that there is a representation as the pointwise minimum of some family composed by convex risk measures. Concomitantly, Jia et al. (2020) prove a similar representation result for monetary risk measures, which are more general than star-shaped ones. Then, there is a question on how both classes are connected. In this letter, we provide an answer by casting light on the importance of the acceptability of 0, which is linked to the property of normalization. We then show that under mild conditions, a monetary risk measure is only a translation away from star-shapedness. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2108.13500&r= |
| By: | Stephen Lee |
| Abstract: | One of the key research questions in the private commercial real estate market involves the investigation of the amount of specific risk in portfolios of various size. In other words, the number of properties a real estate portfolio needs to diversified, i.e. have no specific risk. Previous studies suggesting that the reduction in specific risk from increasing portfolio size is difficult to achieve and so investors should increase portfolio size almost indefinitely. A conclusion largely based on the linear regression model of Evans and Archer (1968), which infers the amount of specific risk indirectly. This paper therefore re-examine the extent of diversification in private commercial real estate portfolios using two approaches not previous applied in real estate portfolio analysis. First, to overcome the issues with the linear regression approach of Evans and Archer (1968) we use the multivariate curve fitting methodology of Hueng and Yau (2006) to estimate the amount of specific risk in portfolios at each portfolio size directly and find the point at which the specific risk is zero. Second, to avoid timing biases due to changes in the risk/return performance resulting from a fixed holding-periods we follow Chong and Philips (2013) and use randomise start dates with 5-year and 10-year holding periods. Using quarterly returns over the period 2000:1 to 2020:4 for 77 ‘property assets’ and simulation, without replacement, the linear model of Evans and Archer (1968) indicates that even after holding all 77 ‘property assets’ specific risk was still not zero. Confirming the results of previous studies. In contrast, using the multivariate curve fitting approach of Hueng and Yau (2006) the results show that investors can reduce about 95% of the specific risk with only five or six ‘property assets’ and that specific risk is zero at the 11 ‘property asset’ portfolio level. Thus, we reject the idea that investors in private commercial real estate should continue to increase their portfolio almost indefinitely. |
| Keywords: | Linear Regression; multivariate modelling; portfolio specific risk; town level data |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_211&r= |
| By: | Chiara Künzle; Sven Bienert; Cay Oertel; Werner Gleißner |
| Abstract: | This paper aims to show that the value of a real estate portfolio can be increased through systematic diversification. This value contribution can, on the one hand, be proven within a portfolio and, on the other hand, by including the owner's remaining assets. A quantification would be a comprehensible proof that a portfolio can generate an investor-specific value contribution through diversification beyond the sum of the individual market values. The basic research approach is proven using an alternative valuation method. In particular a DCF valuation is used, which is extended by a Monte Carlo Simulation. This method addresses all risks that can arise from real estate investments. This approach can help portfolio managers with transaction decisions. Moreover, it is an instrument that demonstrates the competence of the initiator and helps to achieve better financing conditions by showing professional investors the efficiency of the planned fund or portfolio. The paper presents an alternative approach to the prevailing Modern Portfolio Theory, which focuses only on the expected return on the one hand and the corresponding risk on the other. With the method applied in this paper, the value contribution of such a diversification strategy is demonstrated for the first time using market data. |
| Keywords: | Direct Property Investment; Discounted Cash Flow; Diversification; Risk Management |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_82&r= |
| By: | Yacine Aït-Sahalia; Felix Matthys; Emilio Osambela; Ronnie Sircar |
| Abstract: | We analyze an environment where the uncertainty in the equity market return and its volatility are both stochastic, and may be potentially disconnected. We solve a representative investor's optimal asset allocation and derive the resulting conditional equity premium and risk-free rate in equilibrium. Our empirical analysis shows that the equity premium appears to be earned for facing uncertainty, especially high uncertainty that is disconnected from lower volatility, rather than for facing volatility as traditionally assumed. Incorporating the possibility of a disconnect between volatility and uncertainty significantly improves portfolio performance, over and above the performance obtained by conditioning on volatility only. |
| JEL: | G11 G12 |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29195&r= |
| By: | Karl-Friedrich Keunecke; Hunter Kuhlwein; Cay Oertel |
| Abstract: | Autoregressive heteroscedastic effects in financial time series have been subject to a broad field of applied econometrics. Both academic research as well as the industry apply GARCH processes to real estate data with previous investigation mostly focused on securitized real estate positions. So far, the common approach in the literature has been to assume normal distribution of the innovation term for the GARCH modelling of direct real estate markets (Miles, 2008). The specified assumption of normality however falls short of the data characteristics exhibited by direct real estate markets, such as returns of real estate prices explicitly not normally distributed and better characterized by a more leptokurtic, skewed distribution (Schindler, 2009). Ghahramani and Thavaneswaran (2007) point out that typically the innovation distribution is selected without further justification. Consequently, the omission of a priori assumptions about the innovation term distributions being fit to direct real estate leading to misspecification and -parameterization of GARCH models is the research aim of this study. The employed analysis will utilize monthly transaction-based data for ten US property market subsets, whilst observing a window of time to encompass different market conditions and volatility regimes (Perlin et al., 2021). Determining how ARCH effects might differ across different US real estate submarkets as well as major and non-major markets builds on and extends previous research focused on geographical disaggregation (see Crawford and Fratantoni, 2003; Dolde and Tirtioglu, 1997; Miles, 2008; Schindler, 2009). Subsequently fitting and estimating each data subset with a conditionally normally distributed GARCH model will be juxtaposed by employing a variety of innovation distributions to the data. It follows the central hypothesis of this paper, that the goodness of fit for GARCH models can be improved by allowing for the conditional distribution to be modeled as a flexible a priori assumption. Investigating the differing goodness of fit for the models and employing the most appropriate models to re-estimate the GARCH parameters will allow an analysis of the differences in volatility clustering effects to the model employing normally distributed innovations. The aim is to show empirically, that non-normal innovation term distribution leads to a potentially better goodness of fit of the GARCH model. The utilization of a priori assumptions of GARCH model specification is of high importance not only for portfolio management of investors, but also risk management for economic institutions such as central banks and mortgage banks (Schindler, 2009). To the best of the authors’ knowledge, there is no study which scientifically examines the innovation term distribution of GARCH models of direct real estate investments. This paper aims to provide a better understanding of the influence a priori assumptions of the innovation term can take to increase the validity of volatility models for direct real estate investments. |
| Keywords: | Capital Values; GARCH; Innovation term distribution; Volatility modeling |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_75&r= |