54 research outputs found
An interest rate model with a Markovian mean reverting level
No full text© 2002 IOP Publishing LtdA two-factor Vasicek model, where the mean reversion level changes according to a continuous time finite state Markov chain, is considered. This model could capture the behaviour of monetary authorities who normally set a reference rate which changes from time to time. We derive the term structure via the analytic expression of the bond price that involves a fundamental matrix. The validity of the bond price closed form solution is verified via the forward rate dynamics.Robert J Elliott and Rogemar S Mamo
Applications of hidden Markov models in financial modelling
No full textThis thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.Various models driven by a hidden Markov chain in discrete or continuous time
are developed to capture the stylised features of market variables whose levels or
values constitute as the underliers of financial derivative contracts or investment
portfolios. Since the parameters are switching regimes, the changes and developments
in the economy as soon as they arise are readily reflected in these models.
The change of probability measure technique and the EM algorithm are fundamental
techniques utilised in the optimal parameter estimation. Recursive adaptive
filters for the state of the Markov chain and other auxiliary processes related to
the Markov chain are derived which in turn yield self-tuning dynamic financial
models. A hidden Markov model (HMM)-based modelling set-up for commodity
prices is developed and the predictability of the gold market under this setting is
examined. An Ornstein-Uhlenbeck (OU) model with HMM parameters is proposed
and under this set-up, we address two statistical inference issues: the sensitivity
of the model to small changes in parameter estimates and the selection of the optimal
number of states. The extended OU model is implemented on a data set of
30-day Canadian T-bill yields. An exponential of a Markov-switching OU process
plus a compound Poisson process is put forward as a model for the evolution of
electricity spot prices. Using a data set compiled by Nord Pool, we illustrate the
vast improvements gained in incorporating regimes in the model. A multivariate
HMM is employed as a framework in providing the solutions of two asset allocation
problems; one involves the mean-variance utility function and the other entails the
CVaR constraint. Finally, the valuation of credit default swaps highlights the important
considerations necessitated by pricing in a regime-switching environment.
Certain numerical schemes are applied to obtain approximations for the default
probabilities and swap rates.Brunel Research Initiative and Enterprise Fund (BRIEF) and European Union (Marie Curie Fellowship
A partially linearized sigma point filter for latent state estimation in nonlinear time series models
No full textA new technique for the latent state estimation of a wide class of nonlinear time
series models is proposed. In particular, we develop a partially linearized sigma point filter in which random samples of possible state values are generated at the prediction step using an exact moment matching algorithm and then a linear programming-based procedure is used in the update step of the state estimation. The effectiveness of the new ¯ltering procedure is assessed via a simulation example that deals with a highly nonlinear, multivariate time series representing an interest rate process
Filtering of an HMM-based multivariate Ornstein-Uhlenbeck model with application to forecasting market liquidity
No full textNon UBCUnreviewedAuthor affiliation: University of Western Ontario (Canada)Facult
Stock market returns and climate risk in the U.S.
No full textJEL classification: D24; O13; O47; Q40.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S1042444X24000525?via%3Dihub#appSB .Using a data set for all companies forming the S&P 500 index, we investigate the stock price responses to acute physical risks, chronic physical risks, and transition risks. Our findings reveal that certain sectors are more vulnerable to climate risks, whereas others appear to be relatively unaffected. In addition, our results show that listed firms with poor environmental performance scores are more exposed to climate risk, as indicated by their stock returns being negatively affected, compared to firms with higher environmental performance scores. This suggests that improving environmental performance may help companies to better cope with climate risks and improve their financial performances. Our analysis provides evidence that the short-term systematic risk is more vulnerable to the climate risk events, whereas effects on long-term systematic risk do not appear to be statistically significant. These findings indicate that investors and firms should pay a particular attention to short-term systematic risk when considering the potential impact of climate risk on stock market performances.Fabio Spagnolo acknowledges the financial support provided for the project “ESCAPE - Economic and Social Consequences of Altered Planet Environment,” part of the GRINS project (PE00000018). Yiyang Chen and Rogemar Mamon acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) through a Discovery Grant (RGPIN-2017-04235)
A complete yield curve description of a Markov interest rate model
No full text© World Scientific Publishing CompanyThis paper aims to present a complete term structure characterisation of a Markov interest rate model. To attain this objective, we first give a proof that establishes the Unbiased Expectation Hypothesis (UEH) via the forward measure. The UEH result is then employed, which considerably facilitates the calculation of an explicit analytic expression for the forward rate f(t, T). The specification of the bond price P(t, T), yield rate Y(t, T) and f(t, T) gives a complete set of yield curve descriptions for an interest rate market where the short rate r is a function of a continuous time Markov chain.Robert J. Elliott; Rogemar S. Mamo
Assessment of a pandemic emergency financing facility
No full textThe pandemic bond issued by the World Bank (WB) in 2017 is a financial innovation enabling the transfer of the pandemic risk from the underdeveloped/developing countries to the financial market. It covers perils of various diseases that could overwhelm the global health systems and adversely impact the world economy. If all the triggers are activated, the bond’s principal and coupons are used to finance coordinated, swift and resilient medical response to safeguard the well-being of the populace. This product, however, is criticised for its onerous trigger requirements. We examine the WB’s pandemic-bond pricing framework, which requires inputs that are only partially available. From a rather unstructured COVID-19 data set, an information database is created and customised for pandemic-bond valuation. A vector auto-regressive moving average model is utilised to jointly describe the triggers dynamics. Our modelling simulations of risk triggers reveal that the bond payout could be made in less than half of the WB’s earliest opportunity of 85 days
An interest rate model with a Markovian mean reverting level
No full textA two-factor Vasicek model, where the mean reversion level changes according to a continuous time finite state Markov chain, is considered. This model could capture the behaviour of monetary authorities who normally set a reference rate which changes from time to time. We derive the term structure via the analytic expression of the bond price that involves a fundamental matrix. The validity of the bond price closed form solution is verified via the forward rate dynamics.
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