Market Risk and Financial Markets Modeling
The current financial crisis has revealed serious flaws in models, measures and, potentially, theories, that failed to provide forward-looking expectations for upcoming losses originated from market risks. The Proceedings of the Perm Winter School 2011 propose insights on many key issues and advances in financial markets modeling and risk measurement aiming to bridge the gap. The key addressed topics include: hierarchical and ultrametric models of financial crashes, dynamic hedging, arbitrage free modeling the term structure of interest rates, agent based modeling of order flow, asset pricing in a fractional market, hedge funds performance and many more.
This article presents an engineering approach to estimating market resiliency based on analysis of the dynamics of a liquidity index. The method provides formal criteria for defining a “liquidity shock” on the market and can be used to obtain resiliency-related statistics for further research and estimation of this liquidity aspect. The developed algorithm uses the results of a spline approximation for observational data and allows a theoretical interpretation of the results. The method was applied to real data resulting in estimation of market resiliency for the given period.
The paper is an overview from scratch of the term structure modeling field. We present a brief review of the problem of modeling the term structure of interest rates. We start with informal problem formulation, and then, via different formalizations, we arrive at several different term structure models. We propose a new classification of term structure models based on the nature of a priori assumptions employed. We also illustrate the difference between snapshot and dynamic models, present arguments in favor of and against the approaches discussed and also point out some difficulties arising while using and combining such methods.
Liquidity is an important characteristic for any bond, but now in the literature there are no models for estimating the liquidity premium. Moreover, there is not even an exact definition of this notion. There are many facts proving the existence of the liquidity premium in the bond market. One of such fact, for example, is the difference between values of the bond spread and the credit default swap (CDS) premium. Following the Longstaff (2005) study, often, in practice, CDS data are used for the estimation of the pure credit risk of the underlying bond and henc for the separation of the bond risk premium from the liquidity premium and credit risk premium. However, the fact that CDS premium can be used for the pure credit risk measurement is a disputable proposition. The purpose of this paper is to make recommendations on the applicability of such approach for assessing liquidity premium. In this paper the risks associated with CDS transactions will be considered.Also, different approaches for assessing the liquidity bond premium and liquidity CDS premium will be reviewed as well as the correlation of these quantitites. We will see that the CDS premium does not measure the pure credit component of the bond spread.