Predictability of Tail Risks of Canada and the U.S. Over a Century: The Role of Spillovers and Oil Tail Risks

Motivated by the long standing strong economic ties between Canada and the United States (U.S.), we examine whether such relations can be extended to their stock-market tail risks using over a century of monthly data, while also accounting for the role of tail risks of other advanced economies such as France, Germany, Japan, Italy, Switzerland, and the United Kingdom (U.K.) as well as the role of oil-market tail risk. We employ the Conditional Autoregressive Value at Risk (CAViaR) model developed by Engle and Manganelli (2004) to measure tail risks, where we estimate four variants (Adaptive, Symmetric absolute value, Asymmetric slope and Indirect GARCH) of the CAViaR model to compute the 5% Value-at-Risk (VaR). We then use model diagnostics such as the Dynamic Quantile test (DQ) test, %Hits and Regression Quantile (RQ) statistic to determine the model that best fits the data. Relying on the ``best" tail-risk model and a predictive model that additionally accounts for the salient features of the tail-risk data, we find a strong positive relation between the stock-market tail risks of Canada and the U.S., consistent with risk spillovers between the two economies. Our findings hold for various out-of-sample forecast horizons. We also find contrasting evidence for the oil-market tail risk, whose effect is positive for Canada (being a net oil exporter) and negative for the U.S. (being a net oil importer). Further results obtained after accounting for the role of tail risks of other advanced economies combined using a principal-component analysis reveal a positive relation with the U.S. and negative one for Canada, supporting the diversification potential of the latter in the presence of tail risks of advanced economies other than the U.S. Our findings have implications for investors and policymakers, and are robust to alternative VaR measures.