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on Risk Management |
| By: | Wayne Tarrant |
| Abstract: | In this paper we look at the efficacy of different risk measures on energy markets and across several different stock market indices. We use both the Value at Risk and the Tail Conditional Expectation on each of these data sets. We also consider several different durations and levels for historical risk measures. Through our results we make some recommendations for a robust risk management strategy that involves historical risk measures. |
| Date: | 2011–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1111.4421&r=rmg |
| By: | Dominique Gu\'egan; Wayne Tarrant |
| Abstract: | The banking systems that deal with risk management depend on underlying risk measures. Following the Basel II accord, there are two separate methods by which banks may determine their capital requirement. The Value at Risk measure plays an important role in computing the capital for both approaches. In this paper we analyze the errors produced by using this measure. We discuss other measures, demonstrating their strengths and shortcomings. We give examples, showing the need for the information from multiple risk measures in order to determine a bank's loss distribution. We conclude by suggesting a regulatory requirement of multiple risk measures being reported by banks, giving specific recommendations. |
| Date: | 2011–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1111.4414&r=rmg |
| By: | Dominique Gu/'egan; Wayne Tarrant |
| Abstract: | Regulation and risk management in banks depend on underlying risk measures. In general this is the only purpose that is seen for risk measures. In this paper we suggest that the reporting of risk measures can be used to determine the loss distribution function for a financial entity. We demonstrate that a lack of sufficient information can lead to ambiguous risk situations. We give examples, showing the need for the reporting of multiple risk measures in order to determine a bank's loss distribution. We conclude by suggesting a regulatory requirement of multiple risk measures being reported by banks, giving specific recommendations. |
| Date: | 2011–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1111.4417&r=rmg |
| By: | Chia-Lin Chang (Department of Applied Economics, Department of Finance, National Chung Hsing University Taichung, Taiwan); Juan-Angel Jimenez-Martin (Departamento de Economía Cuantitativa (Department of Quantitative Economics), Facultad de Ciencias Económicas y Empresariales (Faculty of Economics and Business), Universidad Complutense de Madrid); Michael McAleer (Econometrisch Instituut (Econometric Institute), Faculteit der Economische Wetenschappen (Erasmus School of Economics), Erasmus Universiteit, Tinbergen Instituut (Tinbergen Institute).; Division of Marketing and International Business, Nanyang Technological University, Singapore.); Teodosio Pérez-Amaral (Departamento de Economía Cuantitativa (Department of Quantitative Economics), Facultad de Ciencias Económicas y Empresariales (Faculty of Economics and Business), Universidad Complutense de Madrid) |
| Abstract: | Volatility is an indispensible component of sensible portfolio risk management. The volatility of an asset of composite index can be traded by using volatility derivatives, such as volatility and variance swaps, options and futures. The most popular volatility index is VIX, which is a key measure of market expectations of volatility, and hence is a key barometer of investor sentiment and market volatility. Investors interpret the VIX cash index as a “fear” index, and of VIX options and VIX futures as derivatives of the “fear” index. VIX is based on S&P500 call and put options over a wide range of strike prices, and hence is not model based. Speculators can trade on volatility risk with VIX derivatives, with views on whether volatility will increase or decrease in the future, while hedgers can use volatility derivatives to avoid exposure to volatility risk. VIX and its options and futures derivatives has been widely analysed in recent years. An alternative volatility derivative to VIX is the S&P500 variance futures, which is an expectation of the variance of the S&P500 cash index. Variance futures are futures contracts written on realized variance, or standardized variance swaps. The S&P500 variance futures are not model based, so the assumptions underlying the index do not seem to have been clearly understood. As these two variance futures are thinly traded, their returns are not easy to model accurately using a variety of risk models. This paper analyses the S&P500 3-month variance futures before, during and after the GFC, as well as for the full data period, for each of three alternative conditional volatility models and three densities, in order to determine whether exposure to risk can be incorporated into a financial portfolio without taking positions on the S&P500 index itself. |
| Keywords: | Risk management, financial derivatives, futures, options, swaps, 3-month variance futures, 12-month variance futures, risk exposure, volatility. |
| JEL: | C22 G32 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1135&r=rmg |
| By: | Meilin Yan (School of Business and Economics, Loughborough University, UK); Maximilian J. B. Hall (School of Business and Economics, Loughborough University, UK); Paul Turner (School of Business and Economics, Loughborough University, UK) |
| Abstract: | This paper uses a relatively new quantitative model for estimating UK banks' liquidity risk. The model is called the Exposure-Based Cash-Flow-at-Risk (CFaR) model, which not only measures a bank's liquidity risk tolerance, but also helps to improve liquidity risk management through the provision of additional risk exposure information. Using data for the period 1997-2010, we provide evidence that there is variable funding pressure across the UK banking industry, which is forecasted to be slightly illiquid with a small amount of expected cash outflow (i.e. £0.06 billion) in 2011. In our sample of the six biggest UK banks, only the HSBC maintains positive CFaR with 95% confidence, which means that there is only a 5% chance that HSBC's cash flow will drop below £0.67 billion by the end of 2011. RBS is expected to face the largest liquidity risk with a 5% chance that the bank will face a cash outflow that year in excess of £40.29 billion. Our estimates also suggest Lloyds TSB's cash flow is the most volatile of the six biggest UK banks, because it has the biggest deviation between its downside cash flow (i.e. CFaR) and expected cash flow. |
| Keywords: | Liquidity risk, Exposure-based CFaR, Risk Management, Funding Pressure |
| JEL: | C15 C22 C87 G21 G32 |
| Date: | 2011–11 |
| URL: | http://d.repec.org/n?u=RePEc:lbo:lbowps:2011_06&r=rmg |
| By: | Staszkiewicz, Piotr W. |
| Abstract: | Author challenges one of the oldest accounting double bookkeeping rules, used since 1494, and proposes instead application of the quadruple accounting entry. He presents the concept of the multiply accounting entry for the risk financial statements and risk management. The development gap concept is described and introduces a simplified entry and reporting example. Model is illustrated with a number of financial-risk statements and attributes including the journal entries. The potential completion edge for users is weighted against costs and benefits. |
| Keywords: | Audit; CRD; COREP; FINREP; IFRS; BASEL; NUK; CRD; reporting; financial accounting; double-entry; risk management; fair value; conceptual framework; accord; |
| JEL: | M41 K23 G32 |
| Date: | 2011–11–21 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:34903&r=rmg |
| By: | Panayiotis P. Athanasoglou (Bank of Greece) |
| Abstract: | This paper examines the simultaneous relationship between bank capital and risk. A model is set up which assumes that banks’ decisions regarding capital and risk are made endogenously in a dynamic pattern. A simultaneous equation system was estimated using an unbalanced panel of SEE banks from 2001 to 2009. A key result for the whole sample of banks is the relationship between regulatory (equity) capital and risk which is positive (negative). However, a positive two-way relationship between regulatory capital and risk was found only in less than-adequately capitalized banks, which also increased substantially their risk in 2009. Thus, banks’ decisions differentiate between equity capital and risk and regulatory capital and risk. A positive, significant and robust effect of liquidity on capital was identified. Both regulatory and equity capital exhibit procyclical behavior, whilst the relationship between risk and rate of growth of GDP is ambitious. |
| Keywords: | Banking; capital;risk;liquidity;regulation; dynamic panel estimation |
| JEL: | C33 G21 G32 |
| Date: | 2011–08 |
| URL: | http://d.repec.org/n?u=RePEc:bog:wpaper:137&r=rmg |
| By: | Torben G. Andersen (Kellogg School of Management, Northwestern University); Tim Bollerslev (Department of Economics, Duke University); Peter F. Christoffersen (Rotman School of Management, University of Toronto); Francis X. Diebold (Department of Economics, University of Pennsylvania) |
| Abstract: | Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating both portfolio-level and asset-level analysis. Asset-level analysis is particularly challenging because the demands of real-world risk management in financial institutions - in particular, real-time risk tracking in very high-dimensional situations - impose strict limits on model complexity. Hence we stress powerful yet parsimonious models that are easily estimated. In addition, we emphasize the need for deeper understanding of the links between market risk and macroeconomic fundamentals, focusing primarily on links among equity return volatilities, real growth, and real growth volatilities. Throughout, we strive not only to deepen our scientific understanding of market risk, but also cross-fertilize the academic and practitioner communities, promoting improved market risk measurement technologies that draw on the best of both. |
| Keywords: | Market risk, volatility, GARCH |
| JEL: | C1 G1 |
| Date: | 2011–11–02 |
| URL: | http://d.repec.org/n?u=RePEc:pen:papers:11-037&r=rmg |
| By: | Subhani, Dr. Muhammad Imtiaz; Osman, Ms. Amber |
| Abstract: | Risk as expected is not that fearsome matter, although it may keep management awake at night; revenue would not be possible without it. Enterprise Risk Management at basics is broadly portrayed as structure of handling and managing risk across an organization. The key concern of this research is to investigate the ERM. The findings highlight that there are very few enterprises from developing nations which are into ERM while the developed nations’ enterprises are huskily and vigorously involved in it and this gap is due to the lack of awareness and serious concerns for value maximization of enterprise share holders in developing nations. |
| Keywords: | Risk; Enterprise Risk Management; Financial Institutions; Developed Nations; Developing Nations |
| JEL: | G32 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:34760&r=rmg |
| By: | Lydia González-Serrano (Department of Business Administration Rey Juan Carlos University); Juan-Ángel Jiménez-Martín (Departamento de Economía Cuantitativa (Department of Quantitative Economics), Facultad de Ciencias Económicas y Empresariales (Faculty of Economics and Business), Universidad Complutense de Madrid) |
| Abstract: | This paper examines the effect on the effectiveness of using futures contracts as hedging instruments of: 1) the model of volatility used to estimate conditional variances and covariances, 2) the analyzed currency, and 3) the maturity of the futures contract being used. For this purpose, daily data of futures and spot exchange rates of three currencies, Euro, British pound and Japanese yen, against the American dollar are used to analyze hedge ratios and hedging effectiveness resulting from using two different maturity currency contracts, near-month and next-to-near-month contract. We estimate four multivariate volatility models (CCC, VARMA-AGARCH, DCC and BEKK) and calculate optimal portfolio weights and optimal hedge ratios to identify appropriate currency hedging strategies. Hedging effectiveness index suggests that the best results in terms of reducing the variance of the portfolio are for the USD/GBP exchange rate. The results show that futures hedging strategies are slightly more effective when the near-month future contract is used for the USD/GBP and USD/JPY currencies. Moreover, CCC and AGARCH models provide similar hedging effectiveness although some differences appear when the DCC and BEKK models are used. |
| Keywords: | Multivariate GARCH, conditional correlations, exchange rates, optimal hedge ratio, optimal portfolio weights, hedging strategies. |
| JEL: | G32 G11 C53 C22 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1133&r=rmg |
| By: | Meilin Yan (School of Business and Economics, Loughborough University, UK); Maximilian J. B. Hall (School of Business and Economics, Loughborough University, UK); Paul Turner (School of Business and Economics, Loughborough University, UK) |
| Abstract: | This paper provides a long-term cost-benefit analysis for the United Kingdom of the Basel III capital and liquidity requirements proposed by the Basel Committee on Banking Supervision (BCBS, 2010a). We provide evidence that the Basel III reforms will have a significant net positive long-term effect on the United Kingdom economy. The estimated optimal tangible common equity capital ratio is 10% of risk-weighted assets, which is larger than the Basel III target of 7%. We also estimate the maximum net benefit when banks meet the Basel III longterm liquidity requirements. Our estimated permanent net benefit is larger than the average estimates of the BCBS. This significant marginal benenfit suggests that UK banks need to increase their reliance on common equity in their capital base beyond the level required by Basel III as well as boosting customer deposits as a funding source. |
| Keywords: | Basel III, Cost-Benefit analysis, Tangible Common Equity Capital, Liquidity |
| JEL: | C32 C53 G21 G28 |
| Date: | 2011–11 |
| URL: | http://d.repec.org/n?u=RePEc:lbo:lbowps:2011_05&r=rmg |
| By: | Jiří Witzany (University of Economics, Prague, Czech Republic) |
| Abstract: | We formulate a bivariate stochastic volatility jump-diffusion model with correlated jumps and volatilities. An MCMC Metropolis-Hastings sampling algorithm is proposed to estimate the model’s parameters and latent state variables (jumps and stochastic volatilities) given observed returns. The methodology is successfully tested on several artificially generated bivariate time series and then on the two most important Czech domestic financial market time series of the FX (CZK/EUR) and stock (PX index) returns. Four bivariate models with and without jumps and/or stochastic volatility are compared using the deviance information criterion (DIC) confirming importance of incorporation of jumps and stochastic volatility into the model. |
| Keywords: | jump-diffusion, stochastic volatility, MCMC, Value at Risk, Monte Carlo |
| JEL: | C11 C15 G1 |
| Date: | 2011–11 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2011_35&r=rmg |
| By: | Peresetsky, A. A.; Karminsky, A. M. |
| Abstract: | The paper presents an econometric study of the two bank ratings assigned by Moody's Investors Service. According to Moody’s methodology, foreign-currency long-term deposit ratings are assigned on the basis of Bank Finan-cial Strength Ratings (BFSR), taking into account “external bank support factors” (joint-default analysis, JDA). Models for the (unobserved) external support are presented, and we find that models based solely on public infor-mation can approximate the ratings reasonably well. It appears that the ob-served rating degradation can be explained by the growth of the banking sys-tem as a whole. Moody’s has a special approach for banks in developing countries in general and for Russia in particular. The models help reveal the factors that are important for external bank support. |
| Keywords: | Banks; Ratings; Rating model; Risk evaluation; Early Warning System |
| JEL: | G32 G21 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:34864&r=rmg |