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on Risk Management |
| By: | Brownlees, Christian (Universitat Pompeu Fabra); Chabot, Benjamin (Federal Reserve Bank of Chicago); Ghysels, Eric (University of North Carolina); Kurz, Christopher J. (Board of the Governors of the Federal Reserve System) |
| Abstract: | The measurement of systemic risk is at the forefront of economists and policymakers concerns in the wake of the 2008 financial crisis. What exactly are we measuring and do any of the proposed measures perform well outside the context of the recent financial crisis? One way to address these questions is to take backtesting seriously and evaluate how useful the recently proposed measures are when applied to historical crises. Ideally, one would like to look at the pre-FDIC era for a broad enough sample of financial panics to confidently assess the robustness of systemic risk measures but pre-FDIC era balance sheet and bank stock price data were heretofore unavailable. We rectify this data shortcoming by employing a recently collected financial dataset spanning the 60 years before the introduction of deposit insurance. Our data includes many of the most severe financial panics in U.S. history. Overall we find CoVaR and SRisk to be remarkably useful in alerting regulators of systemically risky financial institutions. |
| Keywords: | Financial crisis; Systemic risk; Stress testing; credit risk; High-frequency data |
| JEL: | C13 G14 G21 G28 |
| Date: | 2015–07–02 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedhwp:wp-2015-09&r=rmg |
| By: | Yue-Jun Zhang; Ting Yao; Ling-Yun He |
| Abstract: | In order to obtain a reasonable and reliable forecast method for crude oil price volatility, this paper evaluates the forecast performance of single-regime GARCH models (including the standard linear GARCH model and the nonlinear GJR-GARCH and EGARCH models) and the two-regime Markov Regime Switching GARCH (MRS-GARCH) model for crude oil price volatility at different data frequencies and time horizons. The results indicate that, first, the two-regime MRS-GARCH model beats other three single-regime GARCH type models in in-sample data estimation under most evaluation criteria, although it appears inferior under a few of other evaluation criteria. Second, the two-regime MRS-GARCH model overall provides more accurate volatility forecast for daily data but this superiority dies way for weekly and monthly data. Third, among the three single-regime GARCH type models, the volatility forecast of the nonlinear GARCH models exhibit greater accuracy than the linear GARCH model for daily data at longer time horizons. Finally, the linear single-regime GARCH model overall performs better than other three nonlinear GARCH type models in Value-at-Risk (VaR) forecast. |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1512.01676&r=rmg |
| By: | Mihaly Ormos; Dusan Timotity |
| Abstract: | We introduce an equilibrium asset pricing model, which we build on the relationship between a novel risk measure, the Expected Downside Risk (EDR) and the expected return. On the one hand, our proposed risk measure uses a nonparametric approach that allows us to get rid of any assumption on the distribution of returns. On the other hand, our asset pricing model is based on loss-averse investors of Prospect Theory, through which we implement the risk-seeking behaviour of investors in a dynamic setting. By including EDR in our proposed model unrealistic assumptions of commonly used equilibrium models - such as the exclusion of risk-seeking or price-maker investors and the assumption of unlimited leverage opportunity for a unique interest rate - can be omitted. Therefore, we argue that based on more realistic assumptions our model is able to describe equilibrium expected returns with higher accuracy, which we support by empirical evidence as well. |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1512.01806&r=rmg |
| By: | Miguel Sarmiento (Banco de la República, Colombia and Tilburg University); Jorge E. Galán (Banco de España) |
| Abstract: | This paper presents a stochastic frontier model with random inefficiency parameters which captures the influence of risk-taking on bank efficiency and distinguishes the effects among banks with different characteristics. The model is fitted to a 10-year sample of Colombian banks. Cost and profit efficiency are found to be over and underestimated, respectively, when risk measures are omitted or are not accurately modelled. Moreover, the magnitudes at which similar levels of risk affect bank efficiency vary with size and affiliation. In particular, domestic and small Colombian banks benefit more from being highly capitalised, while large and foreign banks benefit from higher exposure to credit and market risk. Holding more liquid assets is found to affect efficiency only at domestic banks. Lastly, we identify some channels that can explain these differences and provide insights for prudential regulation. |
| Keywords: | bank efficiency, Bayesian inference, heterogeneity, random parameters, risktaking, stochastic frontier models |
| JEL: | C11 C23 C51 D24 G21 G32 |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:1537&r=rmg |
| By: | Syed Abul, Basher; Perry, Sadorsky |
| Abstract: | While much research uses multivariate GARCH to model volatility dynamics and risk measures, one particular type of multivariate GARCH model, GO-GARCH, has been underutilized. This paper uses DCC, ADCC and GO-GARCH to model volatilities and conditional correlations between emerging market stock prices, oil prices, VIX, gold prices and bond prices. A rolling window analysis is used to construct out-of-sample onestep-ahead forecasts of dynamic conditional correlations and optimal hedge ratios. In most of the situations we study, oil is the best asset to hedge emerging market stock prices. Hedge ratios from the ADCC model are preferred (most effective) for hedging emerging market stock prices with oil, VIX, or bonds. Hedge ratios estimated from the GO-GARCH are most effective for hedging emerging market stock prices with gold in some instances. These results are reasonably robust to choice of model refits, forecast length and distributional assumptions. |
| Keywords: | Emerging market stock prices; DCC-GARCH, GO-GARCH; Oil prices; hedging |
| JEL: | G15 Q43 |
| Date: | 2015–12–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:68231&r=rmg |
| By: | David Puelz; P. Richard Hahn; Carlos M. Carvalho |
| Abstract: | This paper considers mean-variance optimization under uncertainty, specifically when one desires a sparsified set of optimal portfolio weights. From the standpoint of a Bayesian investor, our approach produces a small portfolio from many potential assets while acknowledging uncertainty in asset returns and parameter estimates. Our loss function used for selection is constructed by integrating over both dimensions of uncertainty, and our optimization is structured as a LASSO minimization with penalized weights. The foundations of this paper are adapted from the decoupled shrinkage and selection (DSS) procedure of Hahn and Carvalho (2015) where statistical inference and the investor's preference for simplicity are separated. |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1512.02310&r=rmg |
| By: | Martina Jasova (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic) |
| Abstract: | This paper explores the role of private credit variables as early warning indicators (EWIs) of banking crises in context of emerging economies. The performance is evaluated by using receiver operating characteristics (ROC) curve and area under the curve (AUC) on long series on credit to the private non-financial sector. The results suggest that credit-to-GDP gap as proposed by Basel III may not be the best performing indicator to signal future banking distress in case of emerging economies. Credit growth outperforms credit-to-GDP gap in all time horizon. These findings are particularly important as they challenge the literature published on EWIs in emerged economies and highlight the need to use complementary indicators and multivariate analysis especially in the environment of emerging economies. |
| Keywords: | Early warning indicators, credit-to-GDP, countercyclical capital buffer, emerging markets, ROC, area under the curve |
| JEL: | C33 G01 G28 |
| Date: | 2015–11 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2015_27&r=rmg |