本研究旨在建立一套完整的銀行市場風險衡量模式，首先定義曝險之損失公式，辨識其中產生損失之風險因子，並設定其相對應之模型，由DNS期間結構模型對利率風險因子建模，以EGARCH模型對股權風險因子建模，並藉由Random Walk模型對外匯風險因子建模，接著使用蒙地卡羅模擬法推導損失分配，進而推導出相對應之風險值或資本計提率。
利用DNS利率風險因子模型，可推導出銀行利率風險中一般市場風險之總損失分配與總風險值，並與歷史模擬法及其他方法作比較，進而求算出資本計提率及經濟資本，其資本計提率在0.28%至0.31%左右。銀行業可藉由經濟資本與法定資本之比較，看出所擁有之曝險的風險程度，並可對於銀行承做之金融商品業務或投資作調整。藉由設定不同風險因子模型，由推導出之現貨商品、期貨、遠期契約、交換等金融商品損失公式，可求出相對應之損失分配與風險值。更進一步地，可以比較同標的資產之不同商品或同商品不同到期期限之風險值變化，並可找出影響未來可能損失之原因。

In this paper, our goal is to construct a complete bank’s market risk measurement. First we define the loss formulas of the exposure. Then we identify the risk factors which cause loss, and set the corresponding models. We use DNS term structure model to build the interest rate risk factor model, EGRACH model to build the equity risk factor model, Random Walk model to build the exchange rate risk factor model. Finally, we use Monte Carlo simulation to derive the loss distribution in order to obtain the corresponding VaR and capital charge rate.
By using DNS interest rate risk factor model, we can derive the total loss distribution and VaR of the interest rate systemic risk, comparing with historical simulation or other method, to calculate the capital charge rate and economic capital. The results show that the economic capital is between 0.28% and 0.31%. We propose that banks can use our method to measure the exposure’s sensitivity by comparing the economic and regulatory capital. They can also adjust their financial business or investment. By using different risk factor models and the loss formulas of spot commodities, futures, forward contracts, and swaps, we can obtain the corresponding loss distribution and VaR. Furthermore, we can observe the change of VaR between the different financial instruments of the same underlying asset or the same financial instruments with different maturities. Finally, we can identify the reasons which impact for possible future loss.

Diebold, F.X. and Li, C. (2006), “Forecasting the Term Structure of Government Bond Yields,” Journal of Econometrics, 130, 337-364.

Li, D.X. (2000), “On Default Correlation: A Copula Function Approach,” Journal of Fixed Income, 9, 43-54.

Meese, R.A. and Rogoff, K. (1983), “Empirical exchange rate models of the seventies: Do they fit out of sample?,” Journal of International Economics, 14, 3-24.

Nelson, D.B. (1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach,” Econometrica, 59, 347-70.

Vasicek, O. (1977), “An Equilibrium Characterisation of the Term Structure,” Journal of Financial Economics, 5, 177-188.

吳宗錠, (2012), “本國銀行市場風險值模型之檢討與修正,” 中央銀行.

廖偉真、雷立芬, (2010), “不同樣本頻率之股市波動性估計─GARCH、TGARCH與EGARCH之比較, ” 臺灣銀行季刊第六十一卷第四期.

鍾經樊, (2013), “商業銀行風險管理、審慎監理與巴塞爾資本協議,” 財務風險管理課程講義.