Pathwise estimation and inference for diffusion market models
discusses contemporary techniques for inferring, from options and
bond prices, the market participants' aggregate view on important
financial parameters such as implied volatility, discount rate,
future interest rate, and their uncertainty thereof. The focus is
on the pathwise inference methods that are applicable to a sole
path of the observed prices and do not require the observation of
an ensemble of such paths. This book is pitched at the level of
senior undergraduate students undertaking research at honors year,
and postgraduate candidates undertaking Master's or PhD degree by
research. From a research perspective, this book reaches out to
academic researchers from backgrounds as diverse as mathematics and
probability, econometrics and statistics, and computational
mathematics and optimization whose interest lie in analysis and
modelling of financial market data from a multi-disciplinary
approach. Additionally, this book is also aimed at financial market
practitioners participating in capital market facing businesses who
seek to keep abreast with and draw inspiration from novel
approaches in market data analysis. The first two chapters of the
book contains introductory material on stochastic analysis and the
classical diffusion stock market models. The remaining chapters
discuss more special stock and bond market models and special
methods of pathwise inference for market parameter for different
models. The final chapter describes applications of numerical
methods of inference of bond market parameters to forecasting of
short rate. Nikolai Dokuchaev is an associate professor in
Mathematics and Statistics at Curtin University. His research
interests include mathematical and statistical finance, stochastic
analysis, PDEs, control, and signal processing. Lin Yee Hin is a
practitioner in the capital market facing industry. His research
interests include econometrics, non-parametric regression, and
scientific computing.
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