Robustification of an on-line EM algorithm for modelling asset prices within an HMM.- Stochastic volatility or stochastic central tendency: evidence from a hidden Markov model of the short-term interest rate.- An econometric model of the term structure of interest rates under regime-switching risk.- The LIBOR market model: a Markov-switching jump diffusion extension.- Exchange rates and net portfolio flows: a Markov-switching approach.- Hedging costs for variable annuities under regime-switching.- A stochastic approximation approach for trend-following trading.- A hidden Markov-modulated jump diffusion model for European option pricing.- An exact formula for pricing American exchange options with regime switching.- Parameter estimation in a weak hidden Markov model with independent drift and volatility.- Parameter estimation in a regime-switching model with non-normal noise.
Since the groundbreaking research of Harry Markowitz into the application of operations research to the optimization of investment portfolios, finance has been one of the most important areas of application of operations research. The use of hidden Markov models (HMMs) has become one of the hottest areas of research for such applications to finance. This handbook offers systemic applications of different methodologies that have been used for decision making solutions to the financial problems of global markets. As the follow-up to the authors' Hidden Markov Models in Finance (2007), this offers the latest research developments and applications of HMMs to finance and other related fields. Amongst the fields of quantitative finance and actuarial science that will be covered are: interest rate theory, fixed-income instruments, currency market, annuity and insurance policies with option-embedded features, investment strategies, commodity markets, energy, high-frequency trading, credit risk, numerical algorithms, financial econometrics and operational risk.
Hidden Markov Models in Finance: Further Developments and Applications, Volume II presents recent applications and case studies in finance and showcases the formulation of emerging potential applications of new research over the book's 11 chapters. This will benefit not only researchers in financial modeling, but also others in fields such as engineering, the physical sciences and social sciences. Ultimately the handbook should prove to be a valuable resource to dynamic researchers interested in taking full advantage of the power and versatility of HMMs in accurately and efficiently capturing many of the processes in the financial market.
Offers cutting-edge research developments and applications of Hidden Markov Models (HMMs) to finance and other closely allied fields
Provides an accurate picture of core financial components by filtering out the random noise in financial markets
Includes contributions from experts and active researchers in the areas of financial mathematics and actuarial science