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The
Degree of Stability of Price Diffusion --
Cornelis A Los
The
distributional form of financial asset returns has important
implications for the theoretical and empirical analyses
in economics and finance. It is now a well-established fact
that financial return distributions are empirically nonstationary,
both in the weak and the strong sense. One first step to
model such nonstationarity is to assume that these return
distributions retain their shape, but not their localization
(mean m) or size (volatility s) as the classical Gaussian
distributions do. In that case, one needs also to pay attention
to skewedness and kurtosis, in addition to localization
and size. This modeling requires special Zolotarev parametrizations
of financial distributions, with four parameters, one for
each relevant distributional moment. Recently popular stable
financial distributions are the Paretian scaling distributions,
which scale both in time T and frequency w. For
example, the volatility of the lognormal financial price
distribution, derived from the geometric Brownian asset
return motion and used to model Black-Scholes (1973) option
pricing, scales according to T0.5. More generally,
the volatility of the price return distributions of Calvet
and Fisher's (2002) Multifractal Model for Asset Returns
(MMAR) scales according to , where the Zolotarev stability
exponent az measures the degree of the scaling, and thus
of the nonstationarity of the financial returns.
©
2005 IUP. All Rights Reserved.
Counterparty
Risk: A Credit Contagion Model for a Bank Loan Portfolio
-- Diana Barro and Antonella
Basso In
this contribution the authors propose a contagion model
for bank loan portfolios that takes into account both a
macroeconomic component and a firm-specific microeconomic
component due to the counterparty risk. The macroeconomic
effect is assumed dependent on a few economic factors while
the microeconomic mechanism of propagation is due to the
business relations, explicitly modeled through the client
network. A wide Monte Carlo simulation analysis is carried
out in order to study the main features of the model.
©2005 IUP. All Rights Reserved.
Measuring
Financial Extremes -- Kay
Giesecke and Lisa R Goldberg Extreme
value statistics provides a practical, flexible, mathematically
elegant framework in which to develop financial risk management
tools that are consistent with empirical data. In this introductory
survey, we discuss some of the basic tools including power
law distributions, the peaks over thresholds estimation
procedure and point processes.
©
2005 Elsevier B V. This article is to be published in the
book Risk Management: A Modern Perspective, M. Ong(Ed),
Wiley 2005. Reprinted with permission.
Research
Summary
The
Financial Risk of Corporations in the Global Economy
-- D Satish
The
research paper studies a wide cross-section of non finance
companies in over 40 countries to examine the determinants
of financial risk. Firm specific issues like the type of
assets and profitability characteristics are significant
drivers of total risk. At the same time the researchers
observe that financial characteristics are less important
in the total risk. However, dividend policy seems to have
a strong affect on the total risk. The paper concludes that
interest rate derivatives are important in countries where
the financial risk is more.
©
2005 Söhnke M Bartram and Gregory W Brown. All Rights
Reserved. IUP holds the copyright
for the summary. |
