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on Risk Management |
| By: | zhang, zhichao; Xie, Li; lu, xiangyun; zhang, zhuang |
| Abstract: | With a sample of 354 U.S. large bank holding companies, this paper investigates the determination of financial distress in financial institutions. We find that: (1) the house price index is consistently significant and positively associated with the Distance-to-Default (DD) measure in the U.S. banking market; (2) all the three major banking risk characteristics i.e. non-performing loans, short-term wholesale funding, and the credit-risk indicator are reliable factors behind DD determination; (3) for the two alternative measures of BHC activity diversification, non-interest income is positively related with BHCs’ DD whereas off-balance-sheet activity is negatively associated to the financial distress measure; and (4) Relevant capital requirements indicators including Tier I Risk-Based Capital Ratio, Total Risk-Based Capital Ratio, Tier I Leverage Ratio should be taken in regulatory assessment of BHCs’ financial distress. |
| Keywords: | Bank Holding Company; Distance-to-Default; Financial distress; Bank regulation; Capital requirements; Non-interest income; Off-balance-sheet activities. |
| JEL: | C53 G14 G21 G28 |
| Date: | 2014–01–31 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:53545&r=rmg |
| By: | Anderson, Edward; Xu, Huifu; Zhang, Dali |
| Abstract: | Conditional value at risk (CVaR) has been widely used as a risk measure in finance. When the confidence level of CVaR is set close to 1, the CVaR risk measure approximates the extreme (worst scenario) risk measure. In this paper, we present a quantitative analysis of the relationship between the two risk measures and it's impact on optimal decision making when we wish to minimize the respective risk measures. We also investigate the difference between the optimal solutions to the two optimization problems with identical objective function but under constraints on the two risk measures. We discuss the benefits of a sample average approximation scheme for the CVaR constraints and investigate the convergence of the optimal solution obtained from this scheme as the sample size increases. We use some portfolio optimization problems to investigate teh performance of the CVaR approximation approach. Our numerical results demonstrate how reducing the confidence level can lead to a better overall performanc e. |
| Keywords: | sample average approximation; distributional robust optimization; semi-infinate programming; minimax; robust optimization; CVaR approximation |
| Date: | 2014–01 |
| URL: | http://d.repec.org/n?u=RePEc:syb:wpbsba:2123/9943&r=rmg |
| By: | Zhiguo He; Arvind Krishnamurthy |
| Abstract: | Systemic risk arises when shocks lead to states where a disruption in financial intermediation adversely affects the economy and feeds back into further disrupting financial intermediation. We present a macroeconomic model with a financial intermediary sector subject to an equity capital constraint. The novel aspect of our analysis is that the model produces a stochastic steady state distribution for the economy, in which only some of the states correspond to systemic risk states. The model allows us to examine the transition from “normal” states to systemic risk states. We calibrate our model and use it to match the systemic risk apparent during the 2007/2008 financial crisis. We also use the model to compute the conditional probabilities of arriving at a systemic risk state, such as 2007/2008. Finally, we show how the model can be used to conduct a macroeconomic “stress test” linking a stress scenario to the probability of systemic risk states. |
| JEL: | E44 G01 G2 |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:19885&r=rmg |
| By: | Alice X. D. Dong; Jennifer S. K. Chan; Gareth W. Peters |
| Abstract: | We develop quantile regression models in order to derive risk margin and to evaluate capital in non-life insurance applications. By utilizing the entire range of conditional quantile functions, especially higher quantile levels, we detail how quantile regression is capable of providing an accurate estimation of risk margin and an overview of implied capital based on the historical volatility of a general insurers loss portfolio. Two modelling frameworks are considered based around parametric and nonparametric quantile regression models which we develop specifically in this insurance setting. In the parametric quantile regression framework, several models including the flexible generalized beta distribution family, asymmetric Laplace (AL) distribution and power Pareto distribution are considered under a Bayesian regression framework. The Bayesian posterior quantile regression models in each case are studied via Markov chain Monte Carlo (MCMC) sampling strategies. In the nonparametric quantile regression framework, that we contrast to the parametric Bayesian models, we adopted an AL distribution as a proxy and together with the parametric AL model, we expressed the solution as a scale mixture of uniform distributions to facilitate implementation. The models are extended to adopt dynamic mean, variance and skewness and applied to analyze two real loss reserve data sets to perform inference and discuss interesting features of quantile regression for risk margin calculations. |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1402.2492&r=rmg |
| By: | Andrew Clare; James Seaton; Peter N. Smith; Stephen Thomas |
| Abstract: | A growing body of literature suggests that over widely varying historical eras and across a wide range of asset classes momentum investing, often accompanied by a trend following overlay, provides superior risk-adjusted returns. We examine the effectiveness of applying these methodologies to pan-European equity asset allocation through periods of potentially substantial market dislocation, in particular, with the advent of the single currency and the equity market crashes of the early 2000‟s and 2008.With the introduction of the Euro there has been much discussion of the benefits of diversification via country based portfolios versus industry sector portfolios. Early studies simply looked at changing return correlations over time. The simple conclusion that increasing country correlations over time drives superior risk-adjusted portfolios towards diversification across sectors has been increasingly challenged. Our approach is different in that we apply momentum and trend following investing strategies and assess whether it is sectoral or country indices which dominate our portfolios through periods of structural changes and extreme volatility. Diversification via sectors is clearly the best strategy in times of equity market stress. In addition, the application of trend following offers a substantial improvement in risk-adjusted performance compared to traditional buy-and-hold portfolios. The terms momentum and trend following have often been used interchangeably although the former is a relative concept and the latter absolute. By combining the two we find that one can achieve the higher return levels associated with momentum portfolios but with much reduced volatility, tail risk and drawdowns due to trend following. We observe that a flexible asset allocation strategy that allocates capital to the best performing instruments irrespective of asset class enhances this further. Such methodologies offer superior risk adjusted returns, especially through periods of raised market volatility |
| Keywords: | Trend following, Momentum investing, tail risk, European equity sectors, Financial Crisis |
| Date: | 2014–01 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2014-08&r=rmg |
| By: | Krasnosselski, Nikolai; Cremers, Heinz; Sanddorf, Walter |
| Abstract: | -- The globalisation on financial markets and the development of financial derivatives has increased not only chances but also potential risk within the banking industry. Especially market risk has gained major significance since market price variation of interest rates, stocks or exchange rates can bear a substantial impact on the value of a position. Thus, a sound estimation of the volatility in the market plays a key role in quantifying market risk exposure correctly. This paper presents GARCH models which capture volatility clustering and, therefore, are appropriate to analyse financial market data. Models with Generalised AutoRegressive Conditional Heteroskedasticity are characterised by the ability to estimate and forecast time-varying volatility. In this paper, the estimation of conditional volatility is applied to Value at Risk measurement. Univariate as well as multivariate concepts are presented for the estimation of the conditional volatility. |
| Keywords: | ARCH,Backtesting,BEKK-GARCH,Bootstrapping,CCC-GARCH,Conditional Volatility,Constant Mean Model,DCC-GARCH,EWMA,GARCH,GJR-GARCH,Heteroskedasticity,IGARCH,Mandelbrot,Misspecification Test,Multivariate Volatility Model,Stylized Facts,Univariate Volatility Model,Value at Risk,Volatility Clustering |
| JEL: | C01 C02 C12 C13 C14 C15 C22 C32 C51 C52 C53 G32 G38 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:fsfmwp:208&r=rmg |
| By: | Gogas, Periklis (Democritus University of Thrace, Department of Economics); Papadimitriou , Theophilos (Democritus University of Thrace, Department of Economics); Matthaiou, Maria- Artemis (Democritus University of Thrace, Department of Economics) |
| Abstract: | A healthy and stable banking system resilient to financial crises is a prerequisite for sustainable growth. Minimization of a) the associated systemic risk and b) of the contagion effect in a banking crisis is a necessary condition to achieve this goal. The Central Bank is in charge of this significant undertaking via a close and detailed monitoring of the banking network that can significantly limit the outbreak of a crisis and a subsequent contagion. In this paper, we propose the use of an auxiliary monitoring system that is both efficient on the required resources and can promptly identify a set of banks that are in distress so that immediate and appropriate action can be taken by the supervising authority. We use the interrelations between banking institutions for efficient monitoring of the entire banking network employing tools from Complex Networks theory. In doing so, we introduce the Threshold Minimum Dominating Set (T-MDS). The T-MDS is used to identify the smallest most efficient subset of banks able to act as a) sensors of distress of a manifested banking crisis and b) provide a path of possible contagion. Moreover, at the discretion of the regulator, the methodology is versatile in providing multiple layers of supervision and monitoring by setting the appropriate threshold levels. We propose the use of this method as a supplementary monitoring tool in the arsenal of a Central Bank. Our dataset includes the 122 largest American banks in terms of their total assets. The empirical results show that when the T-MDS methodology is applied, we can have an efficient supervision of the whole banking network, by monitoring just a small subset of banks. We will show that, the proposed methodology is able to achieve an efficient overview of the 122 banks by only monitoring 47 T-MDS nodes. |
| Keywords: | Complex networks; Minimum Dominating Set; Banking supervision; Interbank loans |
| JEL: | D85 E58 G28 |
| Date: | 2014–01–31 |
| URL: | http://d.repec.org/n?u=RePEc:ris:duthrp:2014_007&r=rmg |