Asset Correlation Research Articles

Book-to-market equity, operating risk, and asset correlations: Implications for Basel capital requirement

We examine the effect of book-to-market equity (BE/ME) on asset correlations in the asymptotic single risk factor (ASRF) framework under the Basel II Accord on regulatory capital requirement. Over a sample period from 1988 to 2007, we find that BE/ME is negatively related to asset correlations after controlling for firm size, default probability, and industry effects. Decomposing BE/ME into risks of operating leverage and financial leverage according to Penman et al. (2007) reveals that higher (lower) operating risk are related to lower (higher) asset correlations. Our findings suggest that incorporating risk of operating leverage into the estimation of asset correlations may improve the measurement of a bank's regulatory capital requirement and potentially reduce regulatory capital arbitrage.

The Effect of Lock-Ups on the Suggested Real Estate Portfolio Weight

We test relative illiquidity, exemplified through a temporary lock-up, as a partial explanation for the gap between theoretical and empirical weights for real estate in a multi-asset portfolio. Since asset correlations are known to increase in bear markets, reducing their diversification benefits, the ex ante knowledge of a lock-up in an asset class offering diversification benefits in bull markets (Hung et al., 2008) may reduce the optimal weight an investor wishes to put in it ex ante. Using the dynamic multiperiod portfolio policies by Brandt and Santa-Clara (2006), and introducing a lock-up in line with de Roon et al. (2009), we study the effects of a partial lock-up on the weight for REITs in a U.S. stock and bond portfolio. We find support for our prediction, in the form of lower weights for the illiquid asset once a lock-up is introduced.

Emerging economies in the 2000s: Real decoupling and financial recoupling

The paper documents an intriguing development in the emerging world in the 2000s: a decoupling from the business cycle of advanced countries, combined with the strengthening of the co-movements in the main emerging market assets that predates the synchronized selloff during the crisis. In addition, the paper tests the hypothesis that financial globalization, to the extent that it creates a common, global investor base for EM, could lead to a tighter asset correlation despite the weaker economic ties. While an examination of the impact of alternative financial globalization proxies yield no conclusive result, a closer look at global emerging market equity and bond funds show that the latter indeed foster financial recoupling during downturns, reflecting the fact that they trade near their respective benchmarks and respond to withdrawals by liquidating holdings across the board.

Fast Clustering of GARCH Processes Via Gaussian Mixture Models

The financial econometrics literature includes several multivariate GARCH models where the model parameter matrices depend on a clustering of financial assets. Those classes might be defined a priori or data-driven. When the latter approach is followed, one method for deriving asset groups is given by the use of clustering methods. In this paper, we analyze in detail one of those clustering approaches, the Gaussian Mixture GARCH. This method is designed to identify groups based on the conditional variance dynamic parameters. The clustering algorithm, based on a Gaussian Mixture model, has been recently proposed and is here generalized with the introduction of a correction for the presence of correlation across assets. Finally, we introduce a benchmark estimator used to assess the performances of simpler and faster estimators. Simulation experiments show evidence of the improvements given by the correction for asset correlation.

Stress testing a retail loan portfolio: an error correction model approach

The use of stress testing for risk monitoring has increased considerably over the last decade. Stress testing – a simulation technique used to assess the strength of a portfolio or a financial institution under unusual economic conditions – emerged as a powerful tool that was originally used in market risk. Its use has subsequently been extended into credit risk. To stress test a credit risk portfolio, practitioners focus on the key parameters that allow the risk of a credit portfolio to be assessed. These parameters, also known as Basel II parameters, are probability of default, loss given default, exposure at default and asset correlation. In this paper, using a time series approach (specifically, an error correction model), we focus on the probability of default parameter that is related to macroeconomic factors. Such an approach involves dealing with the nonstationarity of economic time series and cointegration issues. Hence, when the model is estimated, the probability of default can be simulated by measuring the effects of macroeconomic shocks applied to the model. In turn, these probabilities of default can be used to measure the impact on the probability of default under a given macroeconomic scenario, and then to improve the credit risk monitoring. The results of our study suggest that error correction models are well-suited to macroeconomic stress testing. Indeed, the fitting of the historical probability of default and the results under the stress scenarios considered here are satisfactory.

KNOW YOUR CLIENT! INVESTOR PROFILE AND TAILOR‐MADE ASSET ALLOCATION RECOMMENDATIONS

AbstractWe study how the investor profile influences the asset allocation recommendations of professional advisors. We find the investor's perceived risk attitude influences more the mix of risky assets, whereas the socioeconomic variables influence more the cash percentage. The recommendations are consistent with a diversification behavior driven by actual asset correlations. These findings support the utility of investor advisory that may help enhance the risk and return trade‐off. The main drawback of the recommendations may consist in the degree of customization that is limited by the small number of investor characteristics actually influencing the asset allocation.

Management Insights

Management Insights

Public News Arrival and Cross-Asset Correlation Breakdown: Implications for Algorithmic Trading

This study models the role of public news arrivals on asset correlation in a trading environment populated by computerized algorithms. The model is empirically tested with the individual stock futures and its underlying spot markets, which are characterized by the mechanical cost-of-carry relation that is typically exploited by algorithmic trading. In normal circumstances, the return correlation between the stock futures and spot quotes is nearly perfect, because futures market makers peg their quotes to those of the underlying by using computerized algorithms. Our simple model predicts that this near-perfect correlation can occasionally break down with two conditions: one, the futures market is less liquid than the corresponding spot market; and two, the uncertainty surrounding the impact of the news on the underlying stocks is sufficiently large. This breakdown occurs because the futures market makers switch from automating the quote-matching process to manually monitor and update their quotes. By employing the comprehensive RavenPack database with firm-level news releases, we test and confirm our model predictions. In particular, the spot-futures return correlation falls as the news uncertainty rises, and this correlation breakdown is more prominent for small-cap stocks. Furthermore, for actively traded stocks, the impact of the news on the breakdown is more intense. If the overall stock market experiences extreme turbulence, however, this impact is weaker. We discuss the implications for the limits of algorithmic trading.

The calculation of portfolio unexpected loss in credit and operational risk

In effective risk management, it is of importance to take account of risk aspects from both a credit and operational risk viewpoint. This paper attempts to address both of these areas. The essential tool is the calculation of joint default probabilities starting with an asset correlation and a bivariate normal joint distribution assumption, for log returns of asset values in the case of credit risk, and, an event correlation and a bivariate normal joint distribution assumption for performance scores in the case of operational risk. From this it is shown how to calculate default correlations between counterparties or operational risks and hence a method for assessing unexpected loss for portfolios. The method of moments may then be used to construct a portfolio loss distribution and an appropriate extremal loss measure such as 99.9 per cent value at risk (VaR) or tail VaR.

Stress-Testing Credit Risk Parameters: An Application to Retail Loan Portfolios

Financial institutions are faced with the challenge to forecast future credit portfolio losses. It is common practice to focus on portfolio models consisting of a limited set of parameters, such as the probability of default, asset correlation, loss given default or exposure at default. A simple portfolio model is also used in the Basel II framework for calculating regulatory capital. With regard to the stability of the financial system, these models have to be approved by regulators who have an interest in a conservative assessment of the credit portfolio risk and require the stress-testing of risk estimates. The present paper is the first in its kind to develop a framework to stress the smallest building block, the sensitivities of risk drivers and therefore any derivative such as a risk parameter or the credit portfolio loss. As a result, estimation uncertainties as well as the correlations are taken into account. In an empirical analysis, the stress scenarios for different loan categories are analyzed for US retail borrowers and the implications on economic as well as regulatory capital explored.

Systemic Risk Contributions

We adopt a systemic risk indicator measured by the price of insurance against systemic financial distress and assess individual banks’ marginal contributions to the systemic risk. The methodology is applied using publicly available data to the 19 bank holding companies covered by the U.S. Supervisory Capital Assessment Program (SCAP), with the systemic risk indicator peaking around $1.1 trillion in March 2009. Our systemic risk contribution measure shows interesting similarity to and divergence from the SCAP loss estimates under stress test scenarios. In general, we find that a bank’s contribution to the systemic risk is roughly linear in its default probability but highly nonlinear with respect to institution size and asset correlation.

Asset correlations for credit card defaults

The capital requirements formula within the Basel II Accord is based on a Merton one-factor model and in the case of credit cards an asset correlation of 4% is assumed. In this article we estimate the asset correlation for two datasets assuming the one-factor model. We find that the asset correlations assumed by Basel II are much higher than those observed in the datasets we analyse. We show the reduction in capital requirements that a typical lender would have if the values we estimated were implemented in the Basel Accord in place of the current values.

An Alternative Way of Estimating Asset Values and Asset Value Correlations

In this article, the author suggests a new way of modeling the dynamics of a firm’s asset value and discusses how it could be useful in the computation of asset value correlations in multivariate credit risk models. The method relies on credit spreads from the credit default swap market, and by combining these spreads with stock prices and leverage ratios, the author shows how one can construct a proxy for the asset value. This proxy is then used to calculate asset value correlations among a group of major European banks selected from the stress test conducted by the Committee of European Banking Supervisors in 2010. The asset correlations are presented as a function of bank size, default risk, and geographic location. <b>TOPICS:</b>Fundamental equity analysis, credit risk management, developed markets [Europe]

A Certification Model for Regulatory Arbitrage: Will Regulatory Arbitrage Persist under Basel III?

Based on anecdotal evidence, regulatory arbitrage was a major catalyst of the recent financial crisis. However, regulatory arbitrage is both theoretically and empirically not yet adequately explored. This article develops a theoretical model of regulatory arbitrage. The model is based on a set up where regulatory risk weights act as a certification of the riskiness of a bank’s opaque balance sheet. When regulatory risk weights are misspecified, regulatory arbitrage for the (informed) banks exists. The authors empirically demonstrate that regulatory arbitrage opportunities between the trading business and the loan business existed under the Basel I and Basel II framework and are likely to subsist under the Basel III regime. Their analysis is based on the so-called asset correlation parameter that plays a crucial role for capital requirements under Basel II and Basel III.

The Impact of Dynamic Correlations on Diversification within Long-Only and Hedge Fund Portfolios: Time To Rethink Portfolio Management

Correlation and Diversification: Strong (positive or negative) correlations make diversification that much harder to achieve, especially as correlations tend to increase with volatility. As greater occurrences of increasing volatility and more frequent ‘surprises’ have become the norm, it seems sensible to examine correlations in more depth, as these impact how asset‐classes and funds perform within the overall portfolio. To condense and assess vast quantities of data, a rudimentary scoring system was introduced. Counts were applied to rolling correlations across various time periods and the frequency of each score was recorded. We demonstrate that intra-day correlations vary from -1 to 1 across and intra-asset classes. This throws into question the relevance of Traditional Portfolio Theory and its attempt at diversification, as it assumes constant correlations and volatility. This paper demonstrates how asset correlations change significantly intra-day according to the different input/output time periods selected. From an investor's standpoint it should be clear that relying on monthly performance reports and monthly correlations mask an awful lot of information and in effect makes portfolio management ineffective.

Investigating Implied Asset Correlation and Capital Requirements: Empirical Evidence from the Italian Banking System

The Basel Committee’s reform to strengthen the global capital framework, known as Basel III, takes into account a series of measures to address procyclicality and, consequently, make banks’ capital requirements more stable during the different phases of the economic cycle. The range of possible approaches that Supervisory Authorities could follow to address this issue includes measures such as the use of through-the-cycle probability of default (PD) estimates and/or the calibration of the other risk parameters, i.e., the confidence level and the relation between PD and asset correlation, in an anti-cyclical way. Particularly, this paper aims at detecting further the relation between PD and asset correlation, based on Italian banking system empirical loss data. The authors test the regulatory asset value correlation assumptions through a measure of implied asset correlation that they get by equalling the empirically observed unexpected loss with the regulatory capital requirements. This research sheds more light on the inverse relation between PD and asset correlation, which is one of the main hypotheses the internal ratings based approach is built on, and that has not been modified by the Basel III reform. The paper demonstrates that the sign of this relation depends on the combination of two opposite effects: the “PD effect”, which is consistent with the inverse relation hypothesis and the “PD volatility effect”, which has been neglected by prior literature. According to the provided evidence, if a certain change in the PD comes along with a change in the volatility of the default rate distribution, the inverse relation doesn’t hold.

Regime shifts in mean-variance efficient frontiers: Some international evidence

This article examines how the presence of regimes in means, variances and correlations of asset returns translates into explicit dynamics of the Markowitz mean-variance frontier (MVF). In particular, the article shows both theoretically and through an application to international equity portfolio diversification that substantial differences exist between bull and bear regime-specific frontiers, both in statistical and in economic terms. Using Morgan Stanley Capital International investable indices for five countries/macro-regions, it is possible to characterize the MVFs and optimal portfolio strategies in bull periods, in bear periods and in periods in which high uncertainty exists on the nature of the current regime. A recursive back-testing exercise shows that between 1998 and 2010, adopting a switching mean-variance strategy may have yielded considerable risk-adjusted pay-offs, which were the largest in correspondence to the 2007–2009 financial crisis.

Stochastic Volatility Model Multi Asset Correlation Calibration

The correlation after the subprime crisis 2007 became a major parameter. Most part of the time estimate historically, this parameter was free to the user. In the case of Multi Asset Multi factor stochastic model, some instantaneous correlation are not calibrated or very difficult to implied. In this paper we propose an analitical approximation for the Heston Correlation Matrice. The methodology can be extended to other Multi Factor Multi Asset Model.

Bayesian Inference for Credit Risk with Serially Dependent Factor Model

Default probability and asset correlation are key factors in determining credit default risk in loan portfolios. Therefore, many articles have been devoted to the study in quantifying default probability and asset correlation. However, the classical estimation methods (e.g. MLE) usually use only historical data and often underestimate the default probability in special situations, such as the occurrence of a financial crisis. By contrast, the Bayesian method is seen to be a more viable alternative to solving such estimation problems. In this paper, we consider the Bayesian approach by applying Markov chain Monte Carlo (MCMC) techniques in estimating default probability and asset correlation under serially dependent factor model. The empirical results and out-of-sample forecasting for S&P default data provide strong evidence to support that the serially dependent factor model is reliable in determining credit default risk.

Fat-Tails and Their (Un)Happy Endings: Correlation Bias and its Implications for Systemic Risk and Prudential Regulation

The correlation bias refers to the fact that claim subordination in the capital structure of the firm influences claim holders’ preferred degree of asset correlation in portfolios held by the firm. Using the copula capital structure model, it is shown that the correlation bias shifts shareholder preferences towards highly correlated assets. For financial institutions, the correlation bias makes them more prone to fail and raises the level of systemic risk given their interconnectedness. The implications for systemic risk and prudential regulation are assessed under the prism of Basel III, and potential solutions involving changes to the prudential framework and corporate governance are suggested.