Currently, there is little quantitative analysis literature about the impact of dollar exchange rate on the overnight of other markets. Overnight-AS model and Overnight-SAV model are proposed in this article to measure the overnight risk of exchange rate based on AS mode and SAV model of CAViaR. Then these models are used to measure the risk of Yen exchange rate, HK exchange rate and RMB exchange rate,which select from 2009 to 2014 and then the pros and cons of each model are compared. The results show that Overnight-AS model and Overnight-SAV model are better than AS model and SAV model. Overnight-AS model is better than Overnight-SAV model. The overnight risk of these three exchange rates are affected by lag risks and RMB exchange rate are suffered the biggest risk. Fluctuations in the dollar index will increase the overnight market rates of these three risks. The impact of the RMB exchange rate by the dollar index is less than HK exchange rate and Yen exchange rate. The impact of the weaker dollar on overnight risk is greater than the impact of the stronger dollar. New ideas and methods for the management of exchange rate overnight risk are provided in this paper.
[1] Srideep G, Janice B. Nominal exchange rate volatility, relative price volatility and the real exchange rate[J]. Journal of International Money and Finance,2010,3(29):840-856.
[2] 郭飞. 外汇风险对冲和公司价值——基于中国跨国公司的实证研究[J]. 经济研究,2012,11(9):18-31.
[3] 吴刘杰. 汇率风险与银行业超额收益[J]. 金融论坛,2012,22(9):18-25.
[4] Du Ding,Hu Ou. Exchange rate risk in the US stock market[J]. Journal of International Financial Markets, Institutions & Money,2012,22(1):137-150.
[5] Arnold I J M,MacDonald R,de Vries C G.IMF support and inter regime exchange rate volatility[J]. Open Econ Rev,2012,23(1):193-211.
[6] David P. Gauging exchange rate targeting[J]. Journal of International Money and Finance,2014,25(43):155-166.
[7] Koenker R. Regression quantiles[J]. Econometrica,1978,64(1):33-50.
[8] Engle R, Manganelli. CAViaR: Conditional autoregressive value at risk by regression quantiles[J]. Journal of Business and Economic Statistics,2004,34(4):367-381.
[9] James W. Generating volatility forecasts from value at risk estimates[J]. Management Science,2005,65(51):712-725.
[10] Keith K, Stefan M,Marc S. Value-at-risk prediction: A comparison of alternative strategies[J]. Journal of Financial Econometrics,2006,34(4):53-89.
[11] 陈功. 基于CAViaR的DCC模型及其对中国股市的实证研究[J]. 数学实践与认识,2009,11(2):75-81.
[12] Allen D, Powell L. A gourmet's delight: CAViaR and the australian stock market[J]. Applied Economics Letters,2012,22(19):1493-1498.
[13] 张颖. 分位数回归的金融风险度量理论及实证[J]. 数量经济技术经济研究,2012,87(4):95-109.
[14] Kuster L. Value-at-risk Prediction: A comparison of alternative strategies[J]. Journal of Financial Econometrics,2006,41(4):53-89.
[15] 王新宇,宋学锋. 间接 TARCH CAViaR 模型及其 MCMC 参数估计与应用[J]. 系统工程理论与实践,2008,5(9):46-51.
[16] Taylor W. Using exponentially weighted quantile regression to estimate value at risk and expected shortfall[J]. Journal of Financial Econometrics,2008,54(6): 382-406
[17] Frank J, Masao F. CAViaR-based forecast for oil price risk[J]. Energy Economics,2009,76(31):511-518
[18] 王新宇,宋学锋,吴瑞明. 基于 AAVS-CAViaR模型的股市风险测量研究[J]. 系统工程学报,2010,44(6): 326-333.
[19] Sergio F, Masao F, Zudi L. Index-exciting CAViaR: A new empirical time varying risk model[J]. Studies in Nonlinear Dynamics & Econometrics,2010,68(14):38-52.
[20] Yu P L H,Li W K,Jin Shusong. On some models for value-at-risk[J]. Econometric Reviews,2010,51(29): 622-641.
[21] 闫昌荣. 基于流动性调整CAViaR模型的风险度量方法[J].数量经济技术经济研究,2012,20(3):21-33.
[22] 陈磊,曾勇,杜化宇. 石油期货收益率的分位数建模及其影响因素分析[J]. 中国管理科学,2012,20(6):35-40.
[23] Richard H, Cathy W S. Bayesian time-varying quantile forecasting for value at risk in financial markets[J]. Journal of Business & Economic Statistics,2012,43(29):481-492.
[24] Taylor W. Using CAViaR models with implied volatility for value-at-risk estimation[J]. Journal of Forecasting, 2013,22(32):62-74.
