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Distribution of Net Operating Cash-Flow-at-Risk: The Dynamic Panel Data Model

Adopting the basic principles of the comparables approach to measuring Cash-Flow-at-Risk, this paper examines the operating CFaR of Chinese non-financial listed firms with a Dynamic Panel Data estimation based on the comparable industry clusters and the economic determinants of the operating cash flow. The empirical structural functions of the net operating cash flow and the empirical distributions of CFaR are achieved, which provide a solid basis for cash flow risk management. The results show that the representative firm in the comparable industry cluster of construction and real estate industries is exposed to higher operating CFaR than that in the largely monopoly-dominated industry cluster of mining, transport and storage, and utilities industries, while the traditional manufacturing industry shows moderate operating CFaR exposure as compared with the above two industry clusters. is useful when non-financial firms have confidence in their estimates of risks and in the understanding of the effects of the market prices changing on corporate cash flows. But in reality, the corporate risk exposures are so complex and multifaceted that it is impossible to model all of them, and then the bottom- up approach will capture only a small part of the firm's overall exposures and thus lead to inaccurate estimates of the overall CFaR. By contrast, the top-down approach focuses on the total cash flow volatility, which reflects the combined effect of all the relevant risks facing a non-financial firm. Such volatility can be estimated from a firm's historical cash flows. When there are insufficient cash flow observations, Stein et al. (6) propose a comparables method to estimate a pooled cash flow distribution of a large number of comparable companies in order to gain a statistically significant estimate of volatility. The advantage of such an approach is its ability to provide systematically unbiased estimate of CFaR, and it can be easily and at relatively low cost applied to any number of non- financial firms, though it has the drawback that it does not provide an estimate of CFaR conditional on market risks. There are few studies on the subject of the risk management of non-financial firms' operating cash flows in China currently. In this paper, adopting the basic principles of the top-down, comparables-based approach to estimating Cash-Flow-at-Risk, we examine the operating CFaR of Chinese non-financial listed firms with a Dynamic Panel Data estimation based on the comparable industry clusters and the economic determinants of the operating cash flow. We believe our study contributes to the understanding of the volatility risk properties of Chinese non-financial listed firms' operating cash flows, which will provide a solid basis for the corporate risk management decisions.