Capital Ratios and Systemic Risk: Regulating non-banks in the syndicated lending market, January 2021 version
Should a financial regulator impose the same level of constraint on all market participants, for the sake of maintaining a strong macro-prudential oversight? Using a structural model of syndicated lending, I argue that such blanket approach to regulation may back-fire when applied to financial institution that are less risk-averse than banks, as they prioritise a search-for-yield to the expense of systemic risk. Assuming a mean-variance preference structure on lenders, I calibrate risk-aversion parameters and funding costs to each lenders in the syndicated loan market and run two counterfactual experiments to assess policy effectiveness at tackling systemic risk. I show that regulating non-banks leads them to take on more risk, therefore increasing the overall level of risk in the system, even as the probability of default of the average borrower decreases, the tail risk increases.
Time-Varying Elasticity of Substitution in Near-Money Assets, September 2020 version
The Liquidity Coverage Ratio component of Basel III emphasizes the importance of the substitutability of money with other safe and liquid assets in governments' macroprudential policies. Misunderstandings of this coverage ratio could lead to underestimates of systemic risk. The model presented in this paper introduces dynamics into the elasticity of substitution between deposits and near-money assets in order to explicitly capture variations that could constrain liquidity coverage. Adding an autoregressive structure to the elasticity of substitution accounts for the pattern of expansion and contraction described in the credit markets literature, and yields a more realistic view of funding markets. The model is applied to U.S. and Canadian data and is shown to exhibit key economic features with important policy implications for market participants and regulators.
The Copula Multivariate GARCH (CMGARCH) model is based on a dynamic copula function with time-varying parameters. It is particularly suited for modelling dynamic dependence of non-elliptically distributed financial returns series. The model allows for capturing more flexible dependence patterns than a multivariate GARCH model and also generalizes static copula dependence models. Nonetheless, the model is subject to a number of parameter constraints that ensure positivity of variances and covariance stationarity of the modeled stochastic processes. As such, the resulting distribution of parameters of interest is highly irregular, characterized by skewness, asymmetry, and truncation, hindering the applicability and accuracy of asymptotic inference. In this paper, we propose Bayesian analysis of the CMGARCH model based on Constrained Hamiltonian Monte Carlo (CHMC), which has been shown in other contexts to yield efficient inference on complicated constrained dependence structures. In the CMGARCH context, we contrast CHMC with traditional random-walk sampling used in the previous literature and highlight the benefits of CHMC for applied researchers. We estimate the posterior mean, median and Bayesian confidence intervals for the coefficients of tail dependence. The analysis is performed in an application to a recent portfolio of S&P500 financial asset returns.
"Static and Dynamic Modelling of Credit Default Risk: Tails, Moments, and Calibration." Master's Thesis, University of Waterloo, 2014. UWSpace (http://hdl.handle.net/10012/8709 )
"Testing for the presence of leverage in financial time series." (with Chrismos Egbewole), Honour's thesis, University of Ottawa, 2012.