J.V. (Jules) Tinang Nzesseu, Dr

International Asset Pricing with Heterogeneous Agents: Estimation and Inference. (with Roméo Tédongap).

We empirically test the ability of an heterogeneous agent consumption-based asset pricing model to explain the cross-sectional variation of international equity market expected returns. We find that the first four cross-sectional moments of country consumption growth rates explain 61% of that variation, and this is much higher than the global Fama-French three-factor model. Some consumption-based factors happen to be weakly identified when the asset menu is restricted to developed countries and that might constitute a threat to statistical inference for the risk prices in multi-factor models. However, expanding the asset menu to include emerging markets resolves the issue. Our analysis highlights that relevant measures of country consumption growth heterogeneity are important for pricing international financial markets and we provide a solution to the possible identification problem of the consumption risk prices that may arise.

Portfolio Optimization and Asset Pricing Implications Under Returns Non-Normality Concerns.  (with Roméo Tédongap). Finance 2022/1 (Vol. 43), pages 47 à 94.

We investigate the implications of non-normality for asset allocation and pricing. Asset returns non-normality is captured through a multivariate normal-exponential model; we develop an estimation procedure based on a generalized method of moments. Investors’ non-normality concerns are introduced by adding a linear non-normality constraint to an otherwise standard mean-variance framework. The optimal portfolio solution is obtained in closed form and can be reformulated as a three-fund separation strategy. Suboptimal portfolios that ignore non-normality or are naive in terms of diversification may result in important welfare costs as measured by the certainty equivalent, notably for the most risk-tolerant investors who target large non-normality ratios. In equilibrium, expected returns admit a two-beta representation in which the most important beta in explaining their cross-sectional variation is the one capturing non-normality (more than 60%) while the CAPM beta explains less than 12%.

Macro Uncertainty and the Term Structure of Risk Premium.

Leading frictionless consumption-based asset pricing models predict that the expected return on assets whose cash flows appear in the distant future are on average higher than or equal to the expected returns on assets that pay off in the near future. Contrary to that prediction, some recent empirical studies have found that short-term assets earn a higher expected return than long-term assets. Here, I show that allowing the cash flows to be negatively affected by volatility shocks, as observed in the data (“leverage effect”), could make the short-term assets riskier than long-term assets; thus reconciling the theory with the empirical facts.

GMM estimation of the Long Run Risks model (with Nour Meddahi).

In this paper, we propose a GMM estimation of the structural parameters of the Long Run Risk model that allows for the separation between the consumer optimal decision frequency and the frequency by which the econometrician observes the data. Our inference procedure is also robust to weak identification. The key finding is that the Long Run Risk model adapts well to the data and the use of the estimated parameters to simulate the model enables to the improvement of some quantitative predictions of the model. We also show that the commonly used methods of statistical inference such as the bootstrap (parametric or block bootstrap) might be misleading in this case since they imply an under-coverage of the true confidence interval.

Learning from the Wisdom of Mutual Fund Managers

We design a concentrated investment strategy based on mutual funds' active shares. Our strategy consists of selecting stocks whose holdings by mutual funds deviate the most from their proportion in the market index. These stocks are traded by most mutual funds but not in the same direction. Our resulting portfolio is highly concentrated but still similar to the market portfolio in terms of total risk. It over-performs the domestic market index in terms of expected return and Sharpe ratio. This result indicates that mutual fund managers' collectively possess skills to pick outperforming stocks and time the market. We then implement deep neural network models to learn about manager skills, and to make the strategy feasible in real-time. The results give some insights about managers' strategy for stock picking by identifying the most relevant variables for stock selection.

Consumer heterogeneity, Firm characteristics, and risk exposures. (with Samuël Nelemans)

We extend the firm-level consumption risk exposure measure provided by Dittmar and Lundblad (2017) to a heterogeneous consumer framework. By doing so, we capture additional dimensions of the firm consumption risk exposures related to the cross-sectional distribution of idiosyncratic consumption growth shocks across households. Using an updated sample, our empirical analysis confirms that aggregate consumption risk exposures explain a substantial variation in average returns across anomaly portfolios. However, we find that the heterogeneous agents multi-factor model with four cross-sectional moments of CEX consumption growth as risk factors does a better job, by explaining more than two-thirds of the cross-sectional variation in average returns across anomaly portfolios. These findings are robust to several model specifications.