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Financial Hedging (Hardcover)
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Financial Hedging (Hardcover)
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Financial hedging refers to taking out investments in order to
reduce or cancel the risk in another investment. Its purpose is to
minimise unwanted business risk while still allowing the business
to profit from investment activity. The problem of credit risk is
one of the most important problems in finance. It consists of
computing the probability of a firm defaulting on a debt. The time
evolution of rating for credit risk models can be studied by means
of Markov transition models. This book looks at the homogeneous and
non-homogeneous semi-Markov backward credit risk migration models.
A joint optimisation model for a firm's hedging and leverage
decisions is also examined to help establish an integrated
framework for value creation. Rather than artificially separating
the two interrelated parts of the firm's financial policy, both
corporate decision variables are treated as endogenous.
Furthermore, the cross-sectional variation in indirect bankruptcy
costs is discussed, possibly resulting from a deterioration of
relationships with customers, suppliers or other stakeholders prior
to the legal act of bankruptcy. The effect of probability weighting
on hedging decisions is explored in this book. Observed hedge
ratios in a storage context are close to zero in many situations
and often smaller than the standard minimum-variance hedge zero.
Thus, the importance of probability weighting in decision making
and how it can cause dramatic changes in behavior is looked at.
This book also re-examines hedging performance of the minimum
variance hedge ratios (MVHR) estimated using both the OLS and the
GARCH-type models with S&P 500 index futures contracts. In
particular, the out-of-sample comparison of hedging performance of
the MVHRs under different market volatility regimes are looked at.
In addition, the analysis for parametric and non-parametric Markov
processes are discussed and the construction of the transition
matrix in these two different cases. Several possible strategies
where the investors recalibrate their portfolios at a fixed
temporal horizon are proposed. The authors also show how the Markov
assumption can be used to forecast the portfolio returns and some
simple empirical comparisons between Markovian strategies and
classic reward-risk ones. Finally, articles in this book contribute
to the literature on futures hedging in commodity futures markets
by using wavelet transform analysis to define an explicit and
tractable concept of time horizon. Differences in hedge ratios are
discussed both across commodities and, for each commodity, over all
time horizons of decision-making.
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