Due to the illiquidity of inventories pledged, the essential of price risk management of supply\nchain finance is to long-term price risk measure. Long memory in volatility, which attests a slower\nthan exponential decay in the autocorrelation function of standard proxies of volatility, yields an\nadditional improvement in specification of multi-period volatility models and further impact on\nthe term structure of risk. Thus, long memory is indispensable to model and measure long-term\nrisk. This paper sheds new light on the impact of the existence and persistence of long memory in\nvolatility on inventory portfolio optimization. Firstly, we investigate the existence of long memory\nin volatility of the inventory returns, and examine the impact of long memory on the modeling and\nforecasting of multi-period volatility, the dependence structure between inventory returns and\nportfolio optimization. Secondly, we further explore the impact of the persistence of long memory\nin volatility on the efficient frontier of inventory portfolio via a data generation process with different\nlong memory parameter in the FIGARCH model. The extensive Monte Carlo evidence reveals\nthat both GARCH and IGARCH models without accounting for long memory will misestimate the\nactual long-term risk of the inventory portfolio and further bias the efficient frontier; besides,\nthrough A sensitive analysis of long memory parameter d, it is proved that the portfolio with\nhigher long memory parameter possesses higher expected return and lower risk level. In conclusion,\nbanks and other participants will benefit from the long memory taken into the long-term\nprice risk measure and portfolio optimization in supply chain finance.
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