Asset portfolio securitizations and cyclicality of regulatory capital

This paper analyzes the level and cyclicality of regulatory bank capital for asset portfolio securitizations in relation to the cyclicality of capital requirements for the underlying loan portfolio as under Basel II/III. We find that the cyclicality of capital requirements is higher for (i) asset portfolio securitizations relative to primary loan portfolios, (ii) Ratings Based Approach (RBA) relative to the Supervisory Formula Approach, (iii) given the RBA for a point-in-time rating methodology relative to a rate-and-forget rating methodology, and (iv) under the passive reinvestment rule relative to alternative rules. Capital requirements of the individual tranches reveal that the volatility of aggregated capital charges for the securitized portfolio is triggered by the most senior tranches. This is due to the fact that senior tranches are more sensitive to the macroeconomy. An empirical analysis provides evidence that current credit ratings are time-constant and that economic losses for securitizations have exceeded the required capital in the recent financial crisis.     

An optimal investment strategy in bank management

This paper considers the application of stochastic optimization theory to asset and capital adequacy management in banking. The Basel II Capital Accord lays down regulations to control bank behaviour, and relies on regulatory ratios such as the capital adequacy ratio (CAR). In an attempt to address the problem of compliance to minimum CAR and under assumptions about retained earnings, loan‐loss reserves, the market and shareholder‐bank owner relationships, we construct a continuous‐time model of the Basel II CAR which is computed from the total risk‐weighted assets (TRWAs) and bank capital in a stochastic setting. In particular, we derive an optimal equity allocation strategy for the bank and monitor the performance of the Basel II CAR under the allocation strategy. Copyright © 2011 John Wiley & Sons, Ltd.     

The impact of Basel Accords on the lender’s profitability under different pricing decisions

In response to the deficiencies in financial regulation revealed by the global financial crisis a new capital regulatory standard, Basel III, has been introduced. This builds on the previous regulations known as Basel I and Basel II. We look at how the interest rate charged to maximise a lender’s profitability is affected by these three versions of the Basel Accord under three types of pricing: a fixed-price model, a two-price model and a variable risk-based pricing model. We investigate the result under two different scenarios. First, a fixed price of capital, and second, a fixed amount of equity capital available. We develop an iterative algorithm for solving the latter based on solution approaches to the former. The riskiness of the portfolio has more significance than the Basel Accord requirements but the move from Basel I to Basel II has more impact than that from Basel II to Basel III.     

Scoring decisions in the context of economic uncertainty

We consider methods for incorporating forecasts of future economic conditions into acquisition decisions for scored retail credit and loan portfolios. We suppose that a portfolio manager is faced with two possible future economic scenarios, each characterised by a known probability of occurrence and by known performance functions that give expected profit and volume. We suppose further that he must choose in advance the scoring strategy and score cutoffs to optimise performance. We show that, despite the uncertainty of performance induced by economic conditions, every efficient policy consists of a single cutoff, provided the expected profit and volume performance curves in each scenario are concave. If these curves are not concave, efficient operating points can be characterised as cutoffs on a redefined score. In cases in which two scorecards are available, we show that it may be advantageous to randomly choose the scorecard to be employed, and we provide methods for selecting efficient operating points. Discussion is limited to cases with two scorecards and two economic scenarios, but our approach and results generalise to more scorecards and more economic scenarios.     

Impact of insurance for operational risk: Is it worthwhile to insure or be insured for severe losses?

Under the Basel II standards, the Operational Risk (OpRisk) advanced measurement approach allows a provision for reduction of capital as a result of insurance mitigation of up to 20%. This paper studies different insurance policies in the context of capital reduction for a range of extreme loss models and insurance policy scenarios in a multi-period, multiple risk setting. A Loss Distributional Approach (LDA) for modeling of the annual loss process, involving homogeneous compound Poisson processes for the annual losses, with heavy-tailed severity models comprised of α-stable severities is considered. There has been little analysis of such models to date and it is believed insurance models will play more of a role in OpRisk mitigation and capital reduction in future. The first question of interest is when would it be equitable for a bank or financial institution to purchase insurance for heavy-tailed OpRisk losses under different insurance policy scenarios? The second question pertains to Solvency II and addresses quantification of insurer capital for such operational risk scenarios. Considering fundamental insurance policies available, in several two risk scenarios, we can provide both analytic results and extensive simulation studies of insurance mitigation for important basic policies, the intention being to address questions related to VaR reduction under Basel II, SCR under Solvency II and fair insurance premiums in OpRisk for different extreme loss scenarios. In the process we provide closed-form solutions for the distribution of loss processes and claims processes in an LDA structure as well as closed-form analytic solutions for the Expected Shortfall, SCR and MCR under Basel II and Solvency II. We also provide closed-form analytic solutions for the annual loss distribution of multiple risks including insurance mitigation.     

持股策略下随机需求低碳供应链融资决策均衡

随机市场需求且受制造商减排水平影响,考虑碳限额与交易机制,研究制造商进行单纯银行借贷和供应商投资持股的组合融资时的最优决策和利润情况,分析消费者低碳偏好、碳交易价格和供应商的投资持股比例对供应链的最优决策变量和利润的影响。研究发现:无资金约束、单纯银行借贷和组合融资下,消费者低碳偏好、碳交易价格和持股比例与制造商的减排水平和利润以及供应链系统的利润正相关,而供应商的批发价格和制造商的生产量与消费者低碳偏好正相关,与碳交易价格负相关,而持股比例与供应商的批发价格负相关,与制造商的生产量和减排水平正相关;持股策略下制造商的减排水平和生产量最大,无资金约束时次之,单纯银行借贷时最小;而无资金约束时供应商的批发价格最高,单纯银行借贷时次之,持股策略时最低;在持股比例满足一定条件下,供应商和制造商的利润优于单纯银行借贷时的利润,并且可以优于无资金约束时的利润,提高了供应链的竞争力和效率。     

Structural models in consumer credit

We propose a structural credit risk model for consumer lending using option theory and the concept of the value of the consumer’s reputation. Using Brazilian empirical data and a credit bureau score as proxy for creditworthiness we compare a number of alternative models before suggesting one that leads to a simple analytical solution for the probability of default. We apply the proposed model to portfolios of consumer loans introducing a factor to account for the mean influence of systemic economic factors on individuals. This results in a hybrid structural-reduced-form model. And comparisons are made with the Basel II approach. Our conclusions partially support that approach for modelling the credit risk of portfolios of retail credit.     

Optimal scoring cutoff policies and efficient frontiers

Multiple business objectives are increasingly important in determining account acquisition and management policies in scored retail credit and loan portfolios. These business objectives include profit and market share, as well as the more traditional management of risk. We formulate a mathematical model that addresses the problem of how acquisition decisions should be made with multiple, conflicting objectives when one, or more than one, scorecard is available to the portfolio manager. We show that iso-contours for expected profit, volume and loss are straight lines in the receiver operating characteristic (ROC) space and develop results that establish equivalence between ROC dominance, maximum expected profit, and efficient-frontier dominance in the space of multiple business measures. For two non-dominating scorecards, we derive the efficient frontiers in the profit-volume space and provide guidelines for choosing optimal policies based on the decision maker's trade-offs between objectives.     

Optimal and coherent economic-capital structures: evidence from long and short-sales trading positions under illiquid market perspectives

This paper broadens research literature associated with the assessment of modern portfolio risk management techniques by presenting a thorough modeling of nonlinear dynamic asset allocation and management under the supposition of illiquid and adverse market settings. Specifically, the paper proposes a re-engineered and robust approach to optimal economic capital allocation, in a Liquidity-Adjusted Value at Risk (L-VaR) framework, and particularly from the perspective of trading portfolios that have both long and short-sales trading positions. This paper expands previous approaches by explicitly modeling the liquidation of trading portfolios, over the holding period, with the aid of an appropriate scaling of the multiple-assets’ L-VaR matrix along with GARCH-M technique to forecast conditional volatility and expected return. Moreover, in this paper, the authors develop a dynamic nonlinear portfolio selection model and an optimization algorithm which allocates both economic capital and trading assets subject to some selected financial and operational rational constraints. The empirical results strongly confirm the importance of enforcing financially and operationally meaningful nonlinear and dynamic constraints, when they are available, on economic capital optimization procedure. The empirical results are interesting in terms of theory as well as practical applications and can aid in developing robust portfolio management algorithms that financial entities could consider in light of the aftermath of the latest financial crisis.     

Efficient frontier cutoff policies in credit portfolios

Historically, account acquisition in scored retail credit and loan portfolios has focused on risk management in the sense of minimizing default losses. We believe that acquisition policies should focus on a broader set of business measures that explicitly recognize tradeoffs between conflicting objectives of losses, volume and profit. Typical business challenges are: ‘How do I maximize portfolio profit while keeping acceptance rate (volume, size) at acceptable levels?’ ‘How do I maximize profit without incurring default losses above a given level?’ ‘How do I minimize the risk of large loss exposures for a given market share?’ In this paper we are not concerned with which combination of objectives are appropriate, but rather focus on the cutoff policies that allow us to capture a number of different portfolio objectives. When there are conflicting objectives we show that optimal policies yield meaningful tradeoffs and efficient frontiers and that optimal shadow prices allow us to develop risk-adjusted tradeoffs between profit and market share. Some of the graphical solutions that we obtain are simple to derive and easy to understand without explicit mathematical formulations but even simple constraints may require formal use of non-linear programming techniques. We concentrate on models and insights that yield decision strategies and cutoff policies rather than the techniques for developing good predictors.     

Arbitrage and the tax code

We provide a detailed characterization of arbitrage-free asset prices in the presence of capital gains and income taxes. The distinguishing feature of our analysis is that we impose on the model two important features of the tax code: the limited use of capital losses and the inability to wash sell. We show that under remarkably mild conditions, the lack of pre-tax arbitrage implies the lack of post-tax arbitrage with the limited use of capital losses. The conditions are that the risk free interest rate be positive and that tax rates on interest income exceed capital gains tax rates. The result also holds when only a wash sale constraint is imposed and no investor holds a portfolio with a large capital loss. We allow investors to face different tax rates and have different bases for the calculation of capital gains taxes. The characterizations we provide have important implications for both asset pricing and portfolio choice. Our results imply that models that use arbitrage-free pre-tax models continue for derivative pricing and hedging are also arbitrage free in a world with taxes. Similarly, portfolio choice models with taxes typically specify pre-tax arbitrage free price processes and then analyze portfolio choice in the presence of taxes. In these models, it is unclear if portfolio recommendations are based on risk-return tradeoffs or on the arbitrage opportunities present in the model. Our results imply that if the above features of the tax code are modeled explicitly, then we can isolate the post-tax risk-return tradeoffs.     

Optimal ROE loan pricing with or without adverse selection

The authors describe the structural solution of the loan rate as a function of default and response risk that maximizes expected return on equity for a lender's portfolio of risky loans. Under the assumptions of our model, the non-linear differential equation for the optimizing price is found to be separable in transformed financial, response and risk variables. With an end-point condition where default-free borrowers are willing to borrow at loan rates higher than the lender's cost of funds, general solutions are obtained for cases where default probabilities may depend explicitly on the offered loan rate and where adverse selection may or may not be present. For the general solution, we suggest a numerical algorithm that involves the sequential solutions of two separate transcendental equations each one of which depends on parameters of the risk and response scores. For the special case where the borrower's default probability is conditionally independent of loan rate, it is shown that the optimal solution is independent of Basel regulations on equity capital.     

Continuous‐time stochastic modelling of capital adequacy ratios for banks

Regulation related to capital requirements is an important issue in the banking sector. In this regard, one of the indices used to measure how susceptible a bank is to failure, is the capital adequacy ratio (CAR). We consider two types of such ratios, viz. non‐risk‐based (NRBCARs) and risk‐based (RBCARs) CARs. According to the US Federal Deposit Insurance Corporation (FDIC), we can further categorize NRBCARs into leverage and equity capital ratios and RBCARs into Basel II and Tier 1 ratios. In general, these indices are calculated by dividing a measure of bank capital by an indicator of the level of bank risk. Our primary objective is to construct continuous‐time stochastic models for the dynamics of each of the aforementioned ratios with the main achievement being the modelling of the Basel II capital adequacy ratio (Basel II CAR). This ratio is obtained by dividing the bank's eligible regulatory capital (ERC) by its risk‐weighted assets (RWAs) from credit, market and operational risk. Mainly, our discussions conform to the qualitative and quantitative standards prescribed by the Basel II Capital Accord. Also, we find that our models are consistent with data from FDIC‐insured institutions. Finally, we demonstrate how our main results may be applied in the banking sector. Copyright © 2005 John Wiley & Sons, Ltd.     

多约束的多阶段积极投资组合模型及实证研究

构建投资组合时需要衡量其风险, 除了考虑组合本身的风险暴露, 还需考虑其相对基准组合的风险暴露. 再者, 确定组合权重时需要根据市场的规则加入合适的约束. 基于此, 为了较为完整地考虑现实投资组合面临的风险及交易约束, 将绝对风险(CVaR)和相对风险(跟踪误差)作为风险约束, 将交易成本、卖空限制和多元权值作为交易限制约束, 构建一个新的多阶段投资组合模型, 并利用动态规划和非线性优化方法进行求解. 最后, 利用上证50成分股中41只股票构建投资组合进行实证研究. 实证结果表明构建的多阶段投资组合模型能持续战胜基准组合且优于单阶段投资组合, 同时也表明模型考虑多元权值约束具有现实意义.     

Portfolio-optimization models for small investors

Since 2010, the client base of online-trading service providers has grown significantly. Such companies enable small investors to access the stock market at advantageous rates. Because small investors buy and sell stocks in moderate amounts, they should consider fixed transaction costs, integral transaction units, and dividends when selecting their portfolio. In this paper, we consider the small investor’s problem of investing capital in stocks in a way that maximizes the expected portfolio return and guarantees that the portfolio risk does not exceed a prescribed risk level. Portfolio-optimization models known from the literature are in general designed for institutional investors and do not consider the specific constraints of small investors. We therefore extend four well-known portfolio-optimization models to make them applicable for small investors. We consider one nonlinear model that uses variance as a risk measure and three linear models that use the mean absolute deviation from the portfolio return, the maximum loss, and the conditional value-at-risk as risk measures. We extend all models to consider piecewise-constant transaction costs, integral transaction units, and dividends. In an out-of-sample experiment based on Swiss stock-market data and the cost structure of the online-trading service provider Swissquote, we apply both the basic models and the extended models; the former represent the perspective of an institutional investor, and the latter the perspective of a small investor. The basic models compute portfolios that yield on average a slightly higher return than the portfolios computed with the extended models. However, all generated portfolios yield on average a higher return than the Swiss performance index. There are considerable differences between the four risk measures with respect to the mean realized portfolio return and the standard deviation of the realized portfolio return.     

Concentrated portfolio selection models based on historical data

A new risk measure fully based on historical data is proposed, from which we can naturally derive concentrated optimal portfolios rather than imposing cardinality constraints. The new risk measure can be expressed as a quadratics of the introduced greedy matrix, which takes investors' joint behavior into account. We construct distribution‐free portfolio selection models in simple case and realistic case, respectively. The latest techniques for describing transaction cost constraints and solving nonconvex quadratic programs are utilized to obtain the optimal portfolio efficiently. In order to show the practicality, efficiency, and robustness of our new risk measure and corresponding portfolio selection models, a series of empirical studies are carried out with trading data from advanced stock markets and emerging stock markets. Different performance indicators are adopted to comprehensively compare results obtained under our new models with those obtained under the mean‐variance, mean‐semivariance, and mean‐conditional value‐at‐risk models. Out‐of‐sample results sufficiently show that our models outperform the others and provide a simple and practical approach for choosing concentrated, efficient, and robust portfolios. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimal portfolio strategies benchmarking the stock market

The paper investigates the impact of adding a shortfall risk constraint to the problem of a portfolio manager who wishes to maximize his utility from the portfolios terminal wealth. Since portfolio managers are often evaluated relative to benchmarks which depend on the stock market we capture risk management considerations by allowing a prespecified risk of falling short such a benchmark. This risk is measured by the expected loss in utility. Using the Black–Scholes model of a complete financial market and applying martingale methods, explicit analytic expressions for the optimal terminal wealth and the optimal portfolio strategies are given. Numerical examples illustrate the analytic results.     

New concepts and algorithms for portfolio choice

The paper studies the design of optimal (bond) portfolios taking into account various possible utility functions of an investor. The most prominent model for portfolio optimization was introduced by Markowitz. A real solution in this model can be achieved by quadratic programming routines for mean-variance analysis. Of course, there are many reasons for an investor to prefer other utility criteria than return/variance of return in the Markowitz model. In the last few years, many efficient multiple purpose optimization heuristics have been invented for the needs in optimizing telephone nets, chip layouts, job shop scheduling etc. Some of these heuristics have essential advantages: they are extremely flexible and very easy to implement on computers. One example of such an algorithm is the threshold-accepting algorithm (TA). TA is able to optimize portfolios under nearby arbitrary constraints and subject to nearly every utility function. In particular, the utility functions need neither to be convex, differentiable nor ‘smooth’ in any sense. We implemented TA for bond portfolio optimization with different utility criteria. The algorithms and computational results are presented. Under various utility functions, the ‘best’ portfolios look surprisingly different and have quite different qualities. Thus, for a portfolio manager it might be useful to provide himself with such a ‘multiple-taste’ optimizer in order to be able easily to readjust it according to his own personal utility considerations.     

The development of a simple and intuitive rating system under Solvency II

Regulatory authorities pay considerable attention to setting minimum capital levels for different kinds of financial institutions. Solvency II, the European Commission’s planned reform of the regulation of insurance companies is well underway. One of its consequences will be a shift in focus to internally based models in determining the regulatory capital needed to cover unexpected losses. This evolution emphasises the importance of credit risk assessment through internal ratings. In light of this new prudential regulation, this paper suggests a Basel II compliant approach to predicting credit ratings for non-rated corporations and evaluates its performance compared to external ratings. The paper provides an interesting modelling of non-financial European companies rated by S&P. In developing the model, broad applicability is set as an important boundary condition. Even though the model developed is fairly simple and maintains a high level of granularity, it gives high rates of accuracy and is very interpretable.     

Portfolio optimization based on downside risk: a mean-semivariance efficient frontier from Dow Jones blue chips

To create efficient funds appealing to a sector of bank clients, the objective of minimizing downside risk is relevant to managers of funds offered by the banks. In this paper, a case focusing on this objective is developed. More precisely, the scope and purpose of the paper is to apply the mean-semivariance efficient frontier model, which is a recent approach to portfolio selection of stocks when the investor is especially interested in the constrained minimization of downside risk measured by the portfolio semivariance. Concerning the opportunity set and observation period, the mean-semivariance efficient frontier model is applied to an actual case of portfolio choice from Dow Jones stocks with daily prices observed over the period 2005–2009. From these daily prices, time series of returns (capital gains weekly computed) are obtained as a piece of basic information. Diversification constraints are established so that each portfolio weight cannot exceed 5 per cent. The results show significant differences between the portfolios obtained by mean-semivariance efficient frontier model and those portfolios of equal expected returns obtained by classical Markowitz mean-variance efficient frontier model. Precise comparisons between them are made, leading to the conclusion that the results are consistent with the objective of reflecting downside risk.