Starting from the Merton framework for firm defaults, we provide the analytics and robustness of the relationship between default correlations. We show that loans with higher default probabilities will not only have higher variances but also higher correlations between loans. As a consequence,...

In this paper we solve an intertemporal portfolio problem with correlation risk, using a new approach for the simultaneous modeling of stochastic correlation and volatility. The solutions of the model are in closed form and include an optimal portfolio demand for hedging correlation risk. We...

We develop a new completely affine model of the term structure of interest rates, in which the statevariables evolve as a matrix-valued process of stochastically correlated factors. This setting grants a newelement of flexibility in the simultaneous modeling of stochastic volatilities and...

Starting from the Merton framework for firm defaults, we provide the analytics and robustness of the relationship between default correlations. We show that loans with higher default probabilities will not only have higher variances but also higher correlations between loans. As a consequence,...

This study intends to find out how the bank or industry-specific variables like banking regulation, banking efficiency, and banking operations affect non-performing loans in South Asia. To achieve this objective this study has employed robust 1st and 2nd generation Unit root tests, CIPS test,...

Factor and sparse models are two widely used methods to impose a low-dimensional structure in high dimension. They are seemingly mutually exclusive. In this paper, we propose a simple lifting method that combines the merits of these two models in a supervised learning methodology that allows to...

We extend the three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian, and Prucha (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory...

Wavelet analysis is a new mathematical tool developed as a unified field of science over the last decade. As spatially adaptive analytic tools, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent/strong dependence, seasonality...

The literature on panel models has made considerable progress in the last few decades, integrating non-stationary data both in the time and spatial domain. However, there remains a gap in the literature that simultaneously models non-stationarity and cointegration in both the time and spatial...

In this article, we shed more light on the covariances versus characteristics debate by investigating the explanatory power of the instrumented principal component analysis (IPCA), recently proposed by Kelly et al. (2019). They conclude that characteristics are covariances because there is no...