Journal of Financial Econometrics

Karanasos, Menelaos; Xu, Yongdeng; Yfanti, Stavroula; Zopounidis, Constantin

doi: 10.1093/jjfinec/nbag008pmid: N/A

We derive an admissible parameter space for vector multiplicative error models (vMEMs), explicitly formulating it in terms of the model’s matrix parameters through a set of matrix inequalities. Another key contribution is the adoption of constrained maximum likelihood estimation for the multivariate process, which ensures compliance with these matrix inequalities and addresses the limitations of unconstrained approaches used in previous studies. To demonstrate the effectiveness of the proposed method, we apply it to four empirical cases in financial volatility modeling, emphasizing its practical relevance.

Cotturo, Paul; Liu, Fred; Proner, Robert

doi: 10.1093/jjfinec/nbag006pmid: N/A

We develop deep neural networks with economically motivated restrictions that are designed to overcome the main challenges of factor timing. Our critical innovations include integrating multitask (MT) learning to capture the common structure across factors, with long short-term memory neural networks to extract financial and macroeconomic states. This dynamic MT neural network outperforms all benchmarks in terms of predictive accuracy and economic gains. We pinpoint unemployment, along with variations on leverage, profitability, and money as key predictors, and highlight the importance of capturing their nonlinear interactions. Improved factor timing through neural networks with economic restrictions facilitates more reliable investigation into the economic mechanisms driving factor risk premia, and underscores the value of deep learning for factor investing.

Brignone, Riccardo; Gonzato, Luca; Knaust, Sven; Lütkebohmert, Eva

doi: 10.1093/jjfinec/nbag011pmid: N/A

Model calibration is a challenging yet fundamental task in financial engineering. Using sequential Monte Carlo methods, we reformulate the nonconvex optimization problem as a Bayesian estimation task. This allows to compute any statistic of the estimated parameters, mitigating the strong dependence on starting points and avoiding the troublesome local minima, that plague standard calibration methods. To accelerate computation, we incorporate Markov chain Monte Carlo methods with delayed acceptance and a neural network-based option pricing approach. When applied to S&P 500 index options, our Bayesian algorithms significantly outperform the standard approach in terms of runtime, accuracy, and statistical fit.

Vanden, Joel M

doi: 10.1093/jjfinec/nbag013pmid: N/A

When factors for asset pricing are constructed from a set of sorted portfolios, statistical tests of factor model alphas can suffer from low power. The low power arises because alpha can be decomposed into a sum of two parts, where each part is equal to zero under the null of a correct factor model. If the values of the two parts under the alternative hypothesis are nonzero, differ in sign, and have approximately equal magnitudes, they offset which results in a high probability of not rejecting a false model. Thus, insignificant alpha tests can be misleading when factors are constructed from sorted portfolios. This paper uses the parts of alpha to derive new test statistics that have high power when the traditional alpha test lacks power.

Heinen, Andréas; Valdesogo, Alfonso

doi: 10.1093/jjfinec/nbag009pmid: N/A

We investigate the benefits of including hedge funds into a portfolio of stocks, bonds, and commodities. We use a multivariate canonical vine copula regime-switching model which allows for non-linearity, asymmetry, and time variation in hedge fund returns. We find that the willingness to pay to access hedge funds is about 4 cents per dollar, and it increases with risk aversion; the weights in hedge funds show an inverse U-shape with risk aversion; hedge funds tend to replace stocks (bonds) for risk-averse (risk-tolerant) investors; investing in hedge funds increases historical returns only until 2008, but reduces volatility even after.

Foschi, Rachele; Lilla, Francesca; Mancini, Cecilia

doi: 10.1093/jjfinec/nbag007pmid: N/A

We analyze jump risks in financial asset prices modeled by Ito semimartingales with an exponential Hawkes process as the jump counter. First, using little information, we estimate the probability that an observed jump cluster is not yet exhausted. Second, we make explicit the conditional density of consecutive jump durations and prove that durations stochastically increase. Third, we provide bounds for jump probabilities in consecutive time intervals. Application to 5-minute U.S. returns shows that cluster depletion probabilities strongly correlate with the expected yearly jump count, and improve jump forecasts.