Assessing the risk-return trade-off in loan portfolios

Mencía, Javier Juste

doi:10.1016/j.jbankfin.2012.01.007

Cited by 18 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similarly, in a technical report from KMV, Kealhofer and Bohn (2001) measure unexpected loss by the standard deviation of loss only due to default in a default‐only model, where there are two states: default and no default. Mencía () models homogeneous loan classes, each comprising conditional independent loans whose conditional default probability is a probit function of a Gaussian state variable. He shows that, in his setting, mean–variance analysis is fully consistent with constant relative risk aversion utility maximization.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The most marked difference from the aforementioned banking literature is that this paper focuses on credit risk, where there are only two conditional states: default and no default. Thus, we can adapt similar approaches from the credit‐portfolio‐optimization literature (Altman & Saunders, ; Kealhofer & Bohn, ; Mencía, ). Moreover, we study whether banks restructure their portfolios from low‐risk, low‐earning assets to high‐risk, high‐earning assets to compensate for additional costs imposed by capital requirements.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To address the above‐mentioned questions, we regard a bank as its assets’ manager and consider her portfolio allocation with a minimum regulatory capital requirement as a possible binding condition. Since our focus is on banks’ risk‐taking and asset allocation, we deliberately abstract from banks’ interest‐setting behavior, screening and monitoring –in line with, for example, Altman and Saunders (), Kealhofer and Bohn (), and Mencía (). We find that when a bank's capital is not constrained by regulation, its asset allocation decision depends on the risk measure of assets (namely, the cash‐flow volatility around the expected loss due to default risk) and on the key measure of an asset's valuation, the Sharpe ratio (Sharpe, ), modified according to our settings.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The effect of stricter capital regulation on banks’ risk‐taking: Theory and evidence

Lundtofte

Nielsen

2019

Euro Fin Management

View full text Add to dashboard Cite

A simple portfolio choice model shows that, when a bank's capital is constrained by regulation, regulatory cost (risk weightings) alters the risk and value calculations for the bank's assets. In particular, we find that banks may respond to stricter regulation by increasing the share of high‐risk assets. Our empirical results show that US banks responded to the implementation of the stricter Basel II regulations by increasing the share of high‐risk assets in the risky part of their portfolios.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The effect of stricter capital regulation on banks’ risk‐taking: Theory and evidence

Lundtofte

Nielsen

2019

Euro Fin Management

View full text Add to dashboard Cite

show abstract

“…Paris (2005) treats the selection problem for a portfolio of consumer loans using a one-period, discrete-state formulation and an expected utility objective. Mencia (2012) studies mean-variance and utility-based formulations when loans are placed into groups of loans with similar characteristics. None of these authors address the significant modeling and computational issues arising with large problems, nor the availability of detailed loan-and borrower-level information.…”

Section: Related Literaturementioning

confidence: 99%

Large-Scale Loan Portfolio Selection

Sirignano¹,

Tsoukalas

Giesecke

2016

Operations Research

View full text Add to dashboard Cite

We consider the problem of optimally selecting a large portfolio of risky loans, such as mortgages, credit cards, auto loans, student loans, or business loans. Examples include loan portfolios held by financial institutions and fixed-income investors as well as pools of loans backing mortgage-and asset-backed securities. The size of these portfolios can range from the thousands to even hundreds of thousands. Optimal portfolio selection requires the solution of a high-dimensional nonlinear integer program and is extremely computationally challenging. For larger portfolios, this optimization problem is intractable. We propose an approximate optimization approach that yields an asymptotically optimal portfolio for a broad class of data-driven models of loan delinquency and prepayment. We prove that the asymptotically optimal portfolio converges to the optimal portfolio as the portfolio size grows large. Numerical case studies using actual loan data demonstrate its computational efficiency. The asymptotically optimal portfolio's computational cost does not increase with the size of the portfolio. It is typically many orders of magnitude faster than nonlinear integer program solvers while also being highly accurate even for moderate-sized portfolios. AbstractWe consider the problem of optimally selecting a large portfolio of risky loans, such as mortgages, credit cards, auto loans, student loans, or business loans. Examples include loan portfolios held by financial institutions and fixed-income investors as well as pools of loans backing mortgage-and asset-backed securities. The size of these portfolios can range from the thousands to even hundreds of thousands. Optimal portfolio selection requires the solution of a high-dimensional nonlinear integer program and is extremely computationally challenging. For larger portfolios, this optimization problem is intractable. We propose an approximate optimization approach that yields an asymptotically optimal portfolio for a broad class of data-driven models of loan delinquency and prepayment. We prove that the asymptotically optimal portfolio converges to the optimal portfolio as the portfolio size grows large. Numerical case studies using actual loan data demonstrate its computational efficiency. The asymptotically optimal portfolio's computational cost does not increase with the size of the portfolio. It is typically many orders of magnitude faster than nonlinear integer program solvers while also being highly accurate even for moderate-sized portfolios.

show abstract

“…In the spirit of Best and Grauer (1991) and Mencí a (2012), using NPK method, we illustrate the risk and return characteristics and transitory nature of the CNSSF strategic stocks and SSE 50 Index stock during the financial crisis. The CNSSF adopts a long-term, value-oriented investment and rebalance strategy.…”

Section: Risk and Return Charactermentioning

confidence: 99%

Three essays on the financial analysis of the political actions of the Chinese National Social Security Fund

Li¹

View full text Add to dashboard Cite

In this thesis, using a finance lens, we investigate various aspects of political actions of the Chinese National Social Security Fund (CNSSF). The thesis comprises three empirical papers. In the first essay, we analyze the trading behavior of the CNSSF that operates in a highly political environment. We show that the CNSSF adopts a rebalancing strategy that maintains both portfolio liquidity and growth opportunities. Further, we find direct and indirect evidence that the CNSSF actively intervenes in the stock market by providing liquidity to mutual funds in distress. This liquidity provision can be primarily explained by public information quality. Most notably, CNSSF can profit by assisting distressed mutual funds, especially if such liquidity provision relates to private information. In addition, this bailout-like behavior is not speculative and can positively improve the performance of the distressed mutual funds.In essay 2, we propose a new measure of the policy information advantage available to the CNSSF. Specifically, we assess the impact of this policy-linked "information advantage" on stock performance. Our results show that in the short run, there is a positive and significant information advantage-stock performance linkage.Moreover, our findings support the view that CNSSF promotes the absorption of inside information into prices. In contrast, in the long run, the policy information advantage of the CNSSF negatively decreases the firm operation performance. Finally, we document that there is an information network spillover of the information advantage across the Chinese mutual fund industry, which also affects mutual fund performance.In the third essay, we propose a smooth non-parametric estimation to explore the safety-first portfolio optimization problem. As an empirical application, we simulate optimal portfolios and display return-risk characteristics using the CNSSF strategic stocks. We obtain a non-parametric estimation calculation formula for loss (truncated) probability using the kernel estimator of the portfolio returns' cumulative distribution function, and embed it into two types of safety-first portfolio selection models. We numerically and empirically test our non-parametric method to demonstrate its accuracy and efficiency. Cross-validation results show that our non-parametric kernel estimation method outperforms the empirical distribution method.

show abstract

Assessing the risk-return trade-off in loan portfolios

Cited by 18 publications

References 33 publications

The effect of stricter capital regulation on banks’ risk‐taking: Theory and evidence

The effect of stricter capital regulation on banks’ risk‐taking: Theory and evidence

Large-Scale Loan Portfolio Selection

Three essays on the financial analysis of the political actions of the Chinese National Social Security Fund

Contact Info

Product

Resources

About