VaR methods for the dynamic impawn rate of steel in inventory financing under autocorrelative return

He, Jiangbiao; Xiang-lin, Jiang; Wang, Jian; Dao-li, Zhu; Lei, Zhen

doi:10.1016/j.ejor.2012.06.005

Cited by 16 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In response to the problem of price risk management for inventory pledged, there are abundant academic works on price risk estimation [5][6][7][8].While single inventory has been widely investigated [9], the multivariate portfolio case has been dealt only by a small and more recent literature, regarding the forecasting of correlations between inventories too, which is due to that banks tend to use single inventory when practicing inventory financing. With the rapid development of inventory financing and the evolution of its mode, it is necessary for banks to practice inventory financing based on inventory portfolio.…”

Section: Introductionmentioning

confidence: 99%

“…Second, we put forward a Monte Carlo simulation method with time varying volatility for long-term risk measure of inventory portfolio instead of the square-rootof time rule with a constant volatility. The back testing results in Tables (9)(10)(11) show that the Monte Carlo simulation method is superior to square-root-of time rule. Last, we propose an application to a rigorous quantitative dynamic impawn rate interval for inventory portfolio financing based on long-term risk rather than qualitative framework based on crude rules of thumb.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The long‐term extreme price risk measure of portfolio in inventory financing: An application to dynamic impawn rate interval

Wang

Xiang-lin

et al. 2014

Complexity

View full text Add to dashboard Cite

Different from the short-term risk measure for traditional financial assets (stocks, bonds, etc.), the key to illiquid inventory portfolio traded in the over-the-counter markets is to estimate the long-term extreme price risk with time varying volatility. In this article, a new long-term extreme price risk (value at risk and conditional value at risk) measure method for inventory portfolio and an application to dynamic impawn rate interval are proposed. To realize this, we first establish AutoRegressive Moving Average-Exponential Generalized Autoregressive Conditional Heteroskedasticity-Extreme Value Theory model and multivariatet-Copula to depict the autocorrelation, fat tails, and volatility clustering of returns of inventories and the nonlinear dependence structure of inventories. Furthermore, we obtain the long-term extreme price risk with time varying volatility via Monte Carlo simulation instead of square-root-of time rule. The results show that, first, benefits from risk diversification is significant; second, longterm extreme price risk measure of inventory portfolio via Monte Carlo method outperforms the square-root-of time rule; the last is that the dynamic rate interval based on the long-term price risk is superior to the crude rules of thumb in terms of reducing efficiency loss and improving risk coverage. In summary, this article provides a new quantitative framework for managing the risk of portfolio in inventory financing practice for banks constrained by risk limitation.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The long‐term extreme price risk measure of portfolio in inventory financing: An application to dynamic impawn rate interval

Wang

Xiang-lin

et al. 2014

Complexity

View full text Add to dashboard Cite

show abstract

“…The previous research shows that due to the small sample problem of extreme volatility, tail risk might be underestimated even though the fat-tails distributions are adopted (e.g. Gençay et al [38], He et al [18]). Thus, we introduce extreme value theory to depict the tail distribution of standardized residual series.…”

Section: Tail Distribution Estimation Based On Extreme Value Theorymentioning

confidence: 99%

“…It is also worth noting that, since single inventory financing is commonly used in SCF practice, literature dealing with multivariate portfolio cases is scarce while single inventory are widely investigated (He et al, [18]). In recent years, there have been several default events caused by price collapse of inventories in which both commercial banks and logistics enterprises suffered heavy losses.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Long Memory in Price Volatility of Inventories Pledged on Portfolio Optimization of Supply Chain Finance

He¹,

Wang²,

Xiang-lin³

2016

JMF

View full text Add to dashboard Cite

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

show abstract

“…He Juan (2012) predicts the VaR of steel during various loan periods and gets the impawn rate, which may both control risk and decrease efficiency loss comparing with the experience method that the impawn rate is generally lower than 70%, by setting a model with the formula AR(1)-GARCH(1,1)-GED. A parameter K is introduced, which can improve its risk coverage [16]. In their views, the pledged inventories having autocorrelation are different from financial assets because of fat-tails and so on, meaning the market risk of the collateral may derive from some extreme situations such as dramatic price-decline.…”

mentioning

confidence: 99%

Criticality Analysis on Value-at-Risk Model of Loan-to-Value Ratios Decision in Inventory Financing of Supply Chain Finance

Liao¹,

Yu²,

Xu³

2015

OALib

View full text Add to dashboard Cite

Most literatures prefer loan-to-value ratios (LTV) decisions in supply chain finance (SCF) on the way of profit maximization. This paper attempts to discuss the relationship between LTV and market risk of the loan in inventory financing of SCF from the perspective of value at risk (VaR) for the critical value of LTV corresponding to extreme value of loan VaR to prevent the bank from the risks caused by LTV decisions under the extreme position of price-decline in commodity market. Different from the traditional method of VaR only considering the asset value, we incorporate the borrower's financial and procurement positions into VaR model. We demonstrate the critical value of LTV corresponding to extrema of the value-at-risk of loan in nonlinear analysis, as well as the critical order quantity that can monotonically affect the relationship between LTV and loan VaR in linear analysis, followed by the conclusion that higher investment may not mean higher risk from the perspective of VaR in inventory financing of SCF. Furthermore, the impact of parameters involving financial and procurement positions of the borrower is discussed to explore the affections to the bank from the borrower's procurement decisions.

show abstract

VaR methods for the dynamic impawn rate of steel in inventory financing under autocorrelative return

Cited by 16 publications

References 11 publications

The long‐term extreme price risk measure of portfolio in inventory financing: An application to dynamic impawn rate interval

The long‐term extreme price risk measure of portfolio in inventory financing: An application to dynamic impawn rate interval

The Effects of Long Memory in Price Volatility of Inventories Pledged on Portfolio Optimization of Supply Chain Finance

Criticality Analysis on Value-at-Risk Model of Loan-to-Value Ratios Decision in Inventory Financing of Supply Chain Finance

Contact Info

Product

Resources

About