From insurance risk to credit portfolio management: a new approach to pricing CDOs

We present a new approach for pricing collateralized debt obligations (CDOs) which takes into account the issue of the market incompleteness. In particular, we develop a suitable extension of the actuarial framework proposed by Bayraktar et al. [Valuation of mortality risk via the instantaneous Sharpe ratio: Applications to life annuities. J. Econ. Dyn. Control, 2009, 33, 676–691], Milevsky et al. [Financial valuation of mortality risk via the instantaneous Sharpe-ratio: Applications to pricing pure endowments. Working Paper, 2007. Available at: http://arxiv.org/abs/0705.1302], Young [Pricing life insurance under stochastic mortality via the instantaneous Sharpe ratio: Theorems and proofs. Technical Report, 2007. Available at: http://arxiv.org/abs/0705.1297] and Young [Pricing life insurance under stochastic mortality via the instantaneous Sharpe ratio. Insurance: Math. Econ., 2008, 42, 691–703], which is based on the so-called instantaneous Sharpe ratio. Such a procedure allows us to incorporate the attitude of investors towards risk in a direct and rational way and, in addition, is also suitable for dealing with the often illiquid CDO market. Numerical experiments are presented which reveal that the market incompleteness can have a strong effect on the pricing of CDOs, and allows us to explain the high bid-ask spreads that are frequently observed in the markets.     

A new measure of cross-sectional risk and its empirical implications for portfolio risk management

Litterman et al. [Litterman, R., Scheinkman, J., Weiss, L., 1991. Volatility and the yield curve. Journal of Fixed Income 1 (June), 49–53] and Engle and Ng [Engle, R.F., Ng, V.K., 1993. Time-varying volatility and the dynamic behavior of the term structure. Journal of Money, Credit and Banking 25(3), 336–349] provide empirical evidence of a relation between yield curve shape and volatility. This study offers theoretical support for that finding in the general context of cross-sectional time series. We introduce a new risk measure quantifying the link between cross-sectional shape and market risk. A simple econometric procedure allows us to represent the risk experienced by cross-sections over a time period in terms of independent factors reproducing possible cross-sectional deformations. We compare our risk measure to the traditional cross-yield covariance according to their relative performance. Empirical investigation in the US interest rate market shows that (1) cross-shape risk factors outperform cross-yield risk factors (i.e., yield curve level, slope, and convexity) in explaining the market risk of yield curve dynamics; (2) hedging multiple liabilities against cross-shape risk delivers superior trading strategies compared to those stemming from cross-yield risk management.     

Basel-2 capital adequacy: Computing the ‘fair’ capital charge for loan commitment ‘true’ credit risk

This research makes two contributions: (i) to price analytically put option and extension premium embedded in a borrower-extendible commitment, and (ii) to compute the ‘fair’ capital charge that corresponds to the commitment ‘true’ credit risk. In doing so, the procedure replaces the BIS accounting-based concepts of credit-conversion factor, principal-risk factor, and initial term to maturity of irrevocable commitments with the market-based concepts of exercise-cum-takedown proportion and put value implicit in the borrower-extendible commitment, respectively. Finally, the approach is developed one step further to account for the borrowers' risk ratings by public credit agencies; this results in a two-dimensional (time-state of nature) risk-weighting system that applies to all commitment types.     

Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio

We study the impact of machine learning (ML) models for credit default prediction in the calculation of regulatory capital by financial institutions. We do so by using a unique and anonymized database from a major Spanish bank. We first compare the statistical performance of five models based on supervised learning like Logistic Lasso, Trees (CART), Random Forest, XGBoost and Deep Learning, with a well-known model like Logit. We measure the statistical performance through different metrics, and for different sample sizes and features available. We find that ML models outperform, even when relatively low amount of data is used. We then translate this statistical performance into economic impact by estimating the savings in capital when using an advanced ML model instead of a simpler one to compute the risk-weighted assets following the Internal Ratings Based (IRB) approach. Our benchmark results show that implementing XGBoost instead of Logistic Lasso could yield savings from 12.4% to 17% in terms of regulatory capital requirements.     

Modelling portfolio capital flows in a global framework: Multilateral implications of capital controls

In the aftermath of the global financial crisis, many emerging market countries resorted to capital controls to tackle the excessive surge of capital inflows. A number of recent research papers have suggested that the imposition of controls may have imposed negative externalities on other countries by deflecting flows. Our aim in the research reported in this paper is to assess the efficacy of capital controls and potential deflection effects on other countries by constructing a comprehensive global econometric model which captures the dynamic interactions of capital flows with domestic and global fundamentals. The results suggest that capital controls are effective for some countries in the short run, but have no lasting effects. Moreover, there is only limited evidence of deflection effects for a small number of emerging market countries.     

Ratings based capital adequacy for securitizations

This paper develops a framework to measure the exposure to systematic risk for pools of asset securitizations and measures empirically whether current ratings-based rules for regulatory capital of securitizations under Basel II and Basel III reflect this exposure. The analysis is based on a comprehensive US dataset on asset securitizations for the time period between 2000 and 2008. We find that the shortfall of regulatory capital during the Global Financial Crisis is strongly related to ratings. In particular, we empirically show that insufficient capital is allocated to tranches with the highest rating. These tranches account for the greatest part of the total issuance volumes. Furthermore, this paper is the first to calibrate risk weights which account for systematic risk and provide sufficient capital buffers to cover the exposure during similar economic downturns. These policy-relevant findings suggest a re-calibration of RBA risk weights and may contribute to the current efforts by the Basel Committee on Banking Supervision and others to re-establish sustainable securitization markets and to improve the stability of the financial system.     

Efficient hybrid methods for portfolio credit derivatives

In this paper we discuss the computation of basket credit default swaps and collateralized debt obligation squared transactions. We suggest two hybrid algorithms for these two portfolio credit derivatives. The method combines the analytic approximation to the loss distribution of conditionally independent heterogeneous portfolios with the Monte Carlo simulation. The efficiency and accuracy of the algorithms are illustrated with examples.     

谈谈以实物资产、无形资产出资的验证

一、投入的实物资产和无形资产应与生产经营范围相符 验证时要考虑企业章程或营业执照中规定的生产经营范围,核查其投入的实物资产和无形资产等是否与生产经营范围所需相符,如果与企业生产经营范围严重不符的实物资产或无形资产,即使股东之间认可且订有出资协议,一般也不能作为出资.     

A leverage ratio rule for capital adequacy

This paper studies the economic foundations for maximum leverage ratio capital adequacy rules. The paper makes three contributions to the literature. First, we show how to determine the maximum leverage ratio such that the probability of insolvency is less than some predetermined quantity. Two, we show that a leverage ratio rule controls for the same risks as does a Value-at-Risk (VaR) capital adequacy rule. Third, we argue that leverage ratio rules are better than VaR rules because they are more intuitive and easier to compare across firms.     

创建信用卡品牌资产

据CTR市场研究发布的<2008年中国城市居民信用卡使用状况及品牌表现研究报告>显示,中国内地大多数信用卡持有者对信用卡品牌持冷漠态度,仅有1/5用户忠诚于信用卡品牌.高比例的品牌冷漠者和低水平的忠诚度说明,中国信用卡市场存在较强的同质化现象,各品牌间尚未形成显著差异,市场竞争还停留在产品和服务竞争的初级阶段.     

Computerized stock screening rules for portfolio selection

Recent studies have uncovered several systematic patterns that increase the probability that individual investors can select stock portfolios with excess returns. This study tests the feasibility of using a commercially available computerized stock screening program for investors to take advantage of these patterns. The screening program searches the three major exchanges and selects stocks on both fundamental and technical indicators: low price-to-sales ratio, small firm size, accelerating stock prices above their 50 day moving average, high trading volume, and high earnings growth. Of the 18 models tested between 1994 and 1998, those that allow for selection between exchanges yield portfolio returns that significantly exceed the average market indices.     

Haar wavelets-based approach for quantifying credit portfolio losses

This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in credit portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelet basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. The Wavelet Approximation (WA) method is particularly suitable for non-smooth distributions, often arising in small or concentrated portfolios, when the hypothesis of the Basel II formulas are violated. To test the methodology we consider the Vasicek one-factor portfolio credit loss model as our model framework. WA is an accurate, robust and fast method, allowing the estimation of the VaR much more quickly than with a Monte Carlo (MC) method at the same level of accuracy and reliability.     

A portfolio view of consumer credit

To compute risk-adjusted returns and gauge the volatility of their portfolios, lenders need to know the covariances of their loans’ returns with aggregate returns. We use unique credit bureau data to measure individuals’ ‘covariance risk’, i.e., the covariance of their default risk with aggregate consumer default rates, and more generally to analyze the distribution of credit, including the effects of credit scores. We find significant heterogeneity in covariance risk across consumers. Also, the amount of credit they obtain significantly increases with their credit scores, and decreases with their covariance risk (especially revolving credit), though the effect of covariance risk is smaller.     

A critical review of the new capital adequacy framework paper issued by the Basle Committee on Banking Supervision and its implications for the rating agency industry

This paper will concentrate on two main areas. First, on the way in which the Committee intends to allocate capital by assessing risk on the basis of both external and internal ratings and, second, on how it intends to use recognised independent rating agencies. It also includes a comment on bank disclosure. The paper is written from the perspective of Fitch IBCA (FI), the world’s third largest international rating agency. As a result, other institutions, possibly with a different perspective, may not agree with its contents.     

Interacting particle systems for the computation of rare credit portfolio losses

In this paper, we introduce the use of interacting particle systems in the computation of probabilities of simultaneous defaults in large credit portfolios. The method can be applied to compute small historical as well as risk-neutral probabilities. It only requires that the model be based on a background Markov chain for which a simulation algorithm is available. We use the strategy developed by Del Moral and Garnier in (Ann. Appl. Probab. 15:2496–2534, 2005) for the estimation of random walk rare events probabilities. For the purpose of illustration, we consider a discrete-time version of a first passage model for default. We use a structural model with stochastic volatility, and we demonstrate the efficiency of our method in situations where importance sampling is not possible or numerically unstable.      

The pricing of capital assets in a multiperiod world

This paper proposes a general framework for the pricing of capital assets in a multiperiod world. Under quite general conditions, the analysis shows that the equilibrium expected nominal return on any asset can always be expressed as the sum of the risk-free rate and various risk premiums. The first risk premium is identical to the usual risk premium in the Sharpe-Lintner-Mossin capital asset pricing model. The mathematical forms of all the remaining risk premiums are identical even though each individual risk premium may be present for a different reason.     

The Basel Committee proposals for a new capital accord: implications for Italian banks

Following a few general considerations on the recently proposed revision of the Basel Agreement on capital adequacy, this paper focuses on the first pillar of the Basel Committee proposals, the handling of capital requirements for credit risk in the banking book. The Basel Committee envisages an approach alternatively based on external ratings or on internal rating systems for the determination of the minimum capital requirement related to bank loan portfolios. This approach supports a system of capital requirements that is more sensitive to credit risk. On the basis of specific assumptions, these requirements provide a measure of the value at risk (VaR) produced by models used by major international banks. We first address the impact of the standardised and (internal ratings-based) IRB foundation approach using general data on Italian banks loans' portfolios default rates. We then simulate the impact of the proposed new rules on the corporate loan portfolios of Italian banks, using the unique data set of mortality rates recently published by the Bank of Italy. Three main conclusions emerge from the analysis: (i) the standardised approach implicitly penalizes Italian banks in their interbank funding as their rating is generally below AA/Aa, (ii) the average default rate experienced by Italian banks is higher than the one implied in the benchmark risk weight (BRW) proposed by the Basel Committee for the IRB foundation approach, thereby potentially leading to an increase in the regulatory risk weights, and (iii) the risk-weight is based on an average asset correlation that is significantly higher than the one historically recorded within the Italian banks' corporate borrowers. These findings support the need for a significant revision of the basic inputs and assumptions of the Basel proposals. Finally, in relation to the conditions that allow the capital market to effectively discipline banks, we comment on the proposals advanced in relation to the third pillar of the new capital adequacy scheme.     

Estimating the cost of capital with basis assets

Instead of using industry groups or asset pricing models to estimate the cost of capital we propose using risk equivalent classes known as basis assets. A basis asset is constructed by grouping firms together whose returns indicate they share a common risk exposure, which in theory permits a precise and accurate expected return estimate. Thus, knowing to which basis asset a firm belongs, the firm’s cost of capital can be obtained. Empirically, we show that basis assets lead to superior cost of capital estimates when compared with widely used industry groupings. This means we are no longer reliant on asset pricing models or industry groups to estimate the cost of capital of a firm.