The ALM Optimization Model Controlling Extreme Interest Rate Risk

The asset liability management of bank refers to matching the amount of assets and liabilities reasonably to achieve the goal of bank’s liquidity,security and profitability.Interest rate risk management is one of the core of bank’s asset liability management.The variations of interest rate can change market value of bank’s assets and liabilities,leading to changes in the market value of bank owners’ equity,which imperils the safety and stability of banks.Nowadays interest rate marketization has completed in China.The uncertainty in interest rate variation is growing bigger and bigger.Interest rate risk management has great significance in maintaining bank’s safety,stability and profitability.After the interest rates liberalization,the change of interest rate term structure is becoming more and more complicated.Fluctuations of market interest rates with different maturities are significantly different.The variations of interest rate term structure have exceeded the level,slope and curvature changes assumed in the existing duration model.Any variation forms of the interest rate will give rise to the change of market value of the banks’ assets-liabilities and probably have an impact on the economic value of equity.We establish the functional relationship between the change of EVE and interest rate variations of different maturities.We build a ALM optimization model controlling extreme interest rate risk which uses the minimization of EVE variation variance as objective function and guarantees bank’s EVE loss is smaller than loss limitation under stress scenario in the constraints.This dissertation consists of five chapters:(1)First chapter is introduction.This chapter introduce this paper’s science character,significance of the subject.This chapter also analyze ALM at home and abroad.(2)In the second chapter,we introduce the calculation principle of EVE.This part reveals relationship between bank’s EVE and different term’s market interest rate variation.(3)In the third chapter,we describe the theory of controlling loss brought by interest rate risk by using variance and stress testing.There are two methods to control loss.The first method is variance which control statistical loss brought by term structure nonparallel shift.The second method is stress testing model which control loss brought by extreme risk.(4)In the fourth chapter,we build bank’s optimization model based on extreme interest rate risk.The objective function is minimization bank’s EVE variance There are three classes of constraints.The first class makes bank’s month interest income bigger than one target value.The second class controls bank’s EVE loss smaller than loss limitation in stress scenario.The final class is laws and regulations constraints which bank should obey.(5)In the fifth chapter,we give application examples and contrastive analysis.We build a ALM optimization model based on controlling extreme interest rate risk and confirm the effectiveness of this model.Main innovation and features:(1)We establish the functional relationship between the change of EVE and interest rate variations of different maturities to characterize the impact of arbitrary interest rate term structure variations on EVE.This function expression breaks the limit that model based on modified duration could only estimate the effect of interest rate parallel movement on EVE,and that principal component duration and NS duration could only estimate the impact of the change of interest rate term structure’s level,slope and curvature on EVE.(2)We use the minimization variance of EVE variation as objective function and let the mean of EVE’s fluctuation equal zero in the constraints.This ensures that the EVE loss distribution centralizes near the zero as much as possible to form approximate immunization of interest rate risk.It makes up for the deficiency that the traditional duration models could only immunize finite type of interest rate variations.At the same time,it avoids the establishment of equality constraints with duration gap equalling zero when allocating assets,which greatly reduces the cost of risk management.(3)Lastly,we establish the interest rate risk management model to control both statistical losses and extreme losses resulting from interest rate variations.