Archetypes of collective yield curve movements
In this paper we apply a new approach to examine the historical behaviour of interest rate (IR) movements in seven major currencies AUD,CAD,CHF,EUR,GBP,JPY and USD. We apply Principle Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA),to illustrate,understand and model the past behaviour of the above risk factors. The top-down approach aims to form archetypical movements in a completely data- driven fashion. The extracted archetypes can be mapped on coarse categories like fall rise or twist and contain information on the co-movement of risk factors in different currencies. We show that simple correlations are not able to catch the complex behaviour observed in the data set. The objective of this publication is to encourage a more holistic view of forecasting risk factors that are intimately connected. Ignoring this connection and instead performing single risk factor forecasts may leave us with an overall scenario that has not been observed in the past. In our approach we propose archetypes of co-movements as building blocks. Thus,we start from collective movements that are coherent from a historical perspective. A set of risk factor forecasts are then performed by adapting an archetype rather than building single risk factor forecasts from scratch. This approach opens the door to integrated,coherent forecasts created from complex building blocks. The methods may be applied within scenario simulations,forecasting,filtering techniques and technical analysis.
interest rate risk management principle components analysis clustering archetypes coherent forecasts
Dominik Dersch
UniCredit Markets & Investment Banking,Bayerische Hypo-und Vereinsbank AG,D-81925 Munich,Germany
国际会议
The First International Conference on Management Innovation(ICMI 2007)(管理创新会议)
上海
英文
377-383
2007-06-04（万方平台首次上网日期，不代表论文的发表时间）