Raymond Théoret

Cumulant instrument estimators for hedge fund return models with errors in variables

We revisit the factors incorporated in asset pricing models following the recent developments in financial markets – i.e., the rise of shadow banking and the change in the transmission channel of monetary policy. We propose two versions of the Fung and Hsieh (2004) hedge fund return model, especially an augmented market model which accounts for the new dynamics of financial markets and the procyclicality of hedge fund returns. We run these models with an innovative Hausman procedure, tackling the measurement errors embedded in the models factor loadings. Our empirical method also allows for confronting the drawbacks of the instruments used to estimate hedge fund asset pricing models.

Optimally weighting higher-moment instruments to deal with measurement errors in financial return models

Factor loadings are often measured with errors in financial return models. However, these models find applications in many fields of economics and finance. We present a new procedure to optimally weight two well-known cumulant (higher moments) estimators originally designed to deal with errors-in-variables. We develop a new version of the Hausman test which relies on these new instruments in order to build an indicator of measurement errors providing information about the extent of the bias for an estimated coefficient. We apply our new methodology to a well-known financial return model, i.e. the Fama and French (1997) model, over a sample of Hedge Fund Research (HFR) returns, whose distribution is strongly asymmetric and leptokurtic. Our experiments suggest that the market beta is biased by measurement errors, especially at the level of hedge fund strategies. Nevertheless, the alpha puzzle remains robust to our cumulant instruments.

A Study of Dynamic Market Strategies of Hedge Funds Using the Kalman Filter

The authors consider the dynamic market strategies of hedgefunds by using the Kalman filter. There are many studies onthe behavior of conditional alphas and betas of hedge funds,but the dynamics of these coefficients is studied within thetraditional regression framework: The resulting conditionalalphas and betas are hence for a great part arbitrary becausethey do not result from a dynamic optimization process. Inthis article, the authors try to correct this problem in partby writing transition equations for the alpha and the betawhose explanatory variables are market financial variables.The alphas of hedge fund indices appear quite difficult tocontrol, a result in line with the market efficiency hypoth-esis. Besides, the betas are much more controlable, their reac-tion to market variables being significant.

Specification Errors in Financial Models of Returns: An Application to Hedge Funds

This article uses a new set of instruments based on higher statistical moments to discard the specification errors that might be present in the Fama and French model. It shows that the usual instruments perform quite poorly in comparison to higher moments. It estimates the Fama and French model on a sample of 22 HFR hedge funds indices and 111 HFR individual hedge funds over the period 1990-2005. To do so, it compares many instrumental variables methods as the two-stage least squares and the generalized method of moments. The results show that there are few problems of specification errors on the side of the indices but this level of aggregation hides the errors. Indeed, the estimations of the sample of 111 funds reveal specification errors for the loadings of the market premium and the factor SMB which seem understated. The message to retain is that the individual investor must consider specification errors when modelling the returns of hedge funds as he does not buy the indices but the stocks of individual hedge funds. This article shows an investor how to correct the traditional measures of performance, which are alphas and factor loadings, to guide him towards a better decision process.

Conditional Financial Models and the Alpha Puzzle: A Panel Study of Hedge Fund Returns

This article proposes new estimations of conditional versions of the Fama-French model in order to deal with the alpha puzzle that emerges from hedge fund studies. Its originality lies in resorting to a new form of GMM, the GMM-C, which uses the cumulants of the explanatory variables as instruments in doing the estimations of each hedge fund strategy in panel. The estimations reveal that the new estimator is robust and is preferable to the usual OLS estimation of conditional models, which does not account for specification errors as does the GMM-C. This article also shows that the success of a conditional model in solving the alpha puzzle is related to a judicious choice of the conditioning information.

Optimal Instrumental Variables Generators Based on Improved Hausman Regression, with an Application to Hedge Fund Returns

The findings presented in this article improve the existing methods for estimating financial models of returns and especially for estimating the parameters that are relevant for portfolio managers, such as the Jensen alpha, a popular measure for stock selection, and beta, a well-known systemic risk measure. The authors focus on the presence of measurement errors in these models. For instance, the risk factors in the Fama and French models or the market model are biased by measurement, or specification, errors. These measurement errors are related to the use of proxies for measuring risk factors, such as the risk premium, and other risk factors, which are approximated by mimicking portfolios. To tackle these specification problems, the authors propose new Hausman-based estimators lying on cumulants optimal instruments. Using these newly generated strong instruments in a generalized method of moments (GMM) setting, the authors obtain new GMM estimators that they call GMM-C and its homologue GMM-hm. They also extend the methodology to the standard two-stage least squares (TSLS) framework using new optimally generated instruments. These procedures improve the existing method of moments for estimating, or calibrating, the parameters of a financial model and, more generally, for treating the problem of endogeneity often encountered in financial studies. Moreover, this study leads to a new indicator that signals the presence of specification errors in financial models. The authors apply a battery of tests and estimators to a sample of 22 HFR hedge fund indices observed monthly over the period 1990–2005. Their tests reveal that specification errors bias parameter estimation of financial models of returns and that the ranking of hedge funds is very sensitive to the choice of estimators.

Chapter 18 – The Hedge Fund Alpha Puzzle with an Application to Asian Hedge Funds

Many studies demonstrate that the high level of hedge fund alpha is due to specification errors in the return models used to analyze their performance. Using a new estimator based on Generalized Method of Moments relying on higher moments of returns as instruments, we find that this method might help solve the puzzle. Modeling returns volatility also reduces the bias related to alpha. Surprisingly, Asian hedge funds display similar behavior as global hedge funds. Finally, the subprime crisis substantially impacted the risk-taking appetite of global and Asian hedge funds. In that respect, their exposures to the Fama and French risk factors greatly decreased.

The q-factor and the Fama and French asset pricing models: hedge fund evidence

Purpose - We test the new Fama and French (2015) five-factor model relying on a thorough sample of hedge fund strategies drawn from the Barclay’s Global hedge fund database. Design/methodology/approach - We use a stepwise regression to identify the factors of the q-factor model which are relevant for the hedge fund strategy analysis. Doing so, we account for the Fung and Hsieh seven factors which prove very useful in the explanation of the hedge fund strategies. We introduce interaction terms to depict any interaction of the traditional Fama and French factors with the factors associated with the q-factor model. We also examine the dynamic dimensions of the risk-taking behavior of hedge funds using a BEKK procedure and the Kalman filter algorithm. Findings - Our results show that hedge funds seem to prefer stocks of firms with a high investment-to-assets ratio (low CMA), on the one hand, and weak firms’ stocks (low RMW), on the other hand. This combination is not associated with the conventional properties of growth stocks—i.e., low HML stocks—which are related to firms which invest more (low CMA) and which are more profitable (high RMW). Finally, SMB interacts more with RMW while HML is more correlated with CMA. The conditional correlations between SMB and CMA, on the one hand, and HML and RMW, on the other hand, are less tight and may change sign over time. Originality/value - To the best of our knowledge, we are the first to cast the new Fama and French five-factor model in a hedge fund setting which account for the Fung and Hsieh option-like trading strategies. This approach allows us to better uderstand hedge fund strategies because q-factors are useful to study the dynamic behavior of hedge funds.

Product-mix and bank performance: new U.S. and Canadian evidence

Purpose - – The purpose of this paper is to analyse the link between product-mix and bank performance with a comprehensive look at the contribution of each component of banking activities. Design/methodology/approach - – The generalized method of moments estimation approach the authors apply to the US and Canadian large data sets deals with the endogeneity issues related to banks’ decision to diversify in fee-based activities, and the authors also control the non-linearities (asymmetries) in the innovation with a complementary EGARCH procedure. Findings - – The results suggests that the increasing involvement of banks in fee generating activities has a greater positive impact on US bank performance. On the one hand, US banks are more involved in fees related to traditional lending activities and securitization, which contributes to their higher mean return. On the other hand, Canadian banks focus more on investment banking activities, which makes their financial results more procyclical and volatile. Greater profitability notwithstanding, the authors also found that US bank non-interest income activities incorporate more credit risk, a type of risk obviously less diversifiable when credit shocks occur. Originality/value - – The approach shows that the endogeneity problems related to the banks’ decision to diversify in non-traditional activities may be important. The multivariate GARCH approach the authors introduced strongly suggests that diversification gains fluctuate over the business cycle, and that the decision to diversify must be understood in a dynamic setting rather than in a static one.

Off-Balance-Sheet Activities and the Shadow Banking System: An Application of the Hausman Test with Higher Moments Instruments

The noninterest income banks generate from their off-balance-sheet activities contributes greatly to the volatility of their operating revenues. Using Canadian data, we apply a modified Hausman procedure based on higher moments instruments and revisit this phenomenon to establish that the share of noninterest income (snonin) is actually endogenous to banks returns. In 1997, after the adoption of the Value at Risk (VaR) as a measure of banks risk, the snonin sign turns positive in the returns equations, indicating the emergence of diversification gains from banks non-traditional activities. ARCH-M estimations corroborate the idea that banks have gradually adapted to their new business lines, with an adjustment process begun even before 1997. However, the banks risk premium associated to OBS activities has continuously increased since that date.