Interest rates mapping

Kanevski, Mikhaïl; Maignan, M.; Pozdnoukhov, Alexei; Timonin, Vadim

doi:10.1016/j.physa.2008.02.069

Cited by 12 publications

(13 citation statements)

References 9 publications

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“…Along this line, a study was conducted (Kanevski et al, 2008;Kanevski and Timonin, 2010) using spatial statistics, to map the yield curves into a two-dimensional space (maturity and time). Then, via interpolation using geostatistical or machine learning models, the authors reconstructed full yield curves from a specified number of points considered in the data as inputs.…”

Section: Machine Learning Models In Fixed Income Applicationsmentioning

confidence: 99%

“…See, for example, the works of Agrawal et al (2013), Ballings et al (2015), Booth et al (2014a), Dunis et al (2016), Eilers et al (2014) and Vui et al (2013), just to mention a few studies. Although the literature in this field is abundant, as well as in foreign exchange markets (Choudhry et al, 2009(Choudhry et al, , 2012Fletcher, 2012;Fletcher and Shawe-Taylor, 2013;Gradojevic and Yang, 2006;Huang et al, 2007), much less scientific work has been produced covering machine learning in fixed income markets (Castellani and Santos, 2006;Dunis and Morrison, 2007;Kanevski et al, 2008;Kanevski and Timonin, 2010;Sambasivan and Das, 2017). This is the case despite the paramount importance of this asset class for any economy.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting

Nunes

Gerding

McGroarty

et al. 2019

Expert Systems with Applications

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The yield curve is the centrepiece in bond markets, a massive asset class with an overall size of USD 100 trillion that remains relatively under-investigated using machine learning. This paper is the first comprehensive study using artificial neural networks in the context of yield curve forecasting. Specifically, two models were used for forecasting the European yield curve: multivariate linear regression and multilayer perceptron (MLP), at five forecasting horizons, from next day to 20 days ahead. Five variants of the MLP were analysed with different sets of features: target to predict (univariate); the most relevant features; all generated features; and the former two incorporating synthetic data generated by the linear regression model. Additionally, two different techniques of multitask learning were employed: simultaneous modelling and transformation into multiple single task learning. The results show that considering all forecasting horizons, the MLP using the most relevant features achieved the best results and the addition of synthetic data tends to improve accuracy. Furthermore, different targets and forecasting horizons resulted in different relevant features, reinforcing the importance of custom-built models. In the two multitask learning methodologies no clear differentiation could be demonstrated, and several explaining factors are identified. Overall, the outcome is very encouraging for the development of better forecasting systems for fixed income markets.

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Section: Machine Learning Models In Fixed Income Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting

Nunes

Gerding

McGroarty

et al. 2019

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…The investigation pertaining to the future risk-free rate uncertainty undertaken in this paper fits into the strand of literature that seek to describe the fluctuations of interest rates for risk management and trading strategies optimization in the fixed income market (see Kanevski, Maignan, Pozdnoukhov and Timonin, 2008;Cont, 2005;Jamshidian and Zhu, 1997, for example). For the purpose of risk management, short term interest rate fluctuation is the subject of interest.…”

Section: Discussionmentioning

confidence: 99%

On the implied volatility layers under the future risk-free rate uncertainty

Hin

Dokuchaev

2014

IJFMD

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This paper suggests a method of estimation of the implied volatility smile uncertainty of the observed options prices due to future risk-free rate uncertainty. The purpose is to quantify the range of uncertainty under different scenarios. We consider the setting where both the implied volatility and the risk free rate are calculated jointly from the observed option prices. Due to the cumulative risk-free rate uncertainty, the corresponding system of equations is underdetermined, leading to uncertainty in the volatility surface. We estimate the size of implied volatility layers between the surfaces representing the upper and lower bounds for the implied volatilities for the future risk-free rate uncertainty, defined by current Libor rate and the size of fluctuation estimated from the historical data.

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“…Some applications also took place outside the field of geosciences, such as in finance (Kanevski et al, 2008) or in medical imaging (Pham, 2012).…”

Section: Background On Multiple-point Geostatisticsmentioning

confidence: 99%

Bridges between multiple-point geostatistics and texture synthesis: Review and guidelines for future research

Mariethoz

Lefèbvre

2014

Computers & Geosciences

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a b s t r a c tMultiple-Point Simulations (MPS) is a family of geostatistical tools that has received a lot of attention in recent years for the characterization of spatial phenomena in geosciences. It relies on the definition of training images to represent a given type of spatial variability, or texture. We show that the algorithmic tools used are similar in many ways to techniques developed in computer graphics, where there is a need to generate large amounts of realistic textures for applications such as video games and animated movies. Similarly to MPS, these texture synthesis methods use training images, or exemplars, to generate realistic-looking graphical textures.Both domains of multiple-point geostatistics and example-based texture synthesis present similarities in their historic development and share similar concepts. These disciplines have however remained separated, and as a result significant algorithmic innovations in each discipline have not been universally adopted. Texture synthesis algorithms present drastically increased computational efficiency, patterns reproduction and user control. At the same time, MPS developed ways to condition models to spatial data and to produce 3D stochastic realizations, which have not been thoroughly investigated in the field of texture synthesis.In this paper we review the possible links between these disciplines and show the potential and limitations of using concepts and approaches from texture synthesis in MPS. We also provide guidelines on how recent developments could benefit both fields of research, and what challenges remain open.

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Interest rates mapping

Cited by 12 publications

References 9 publications

A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting

A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting

On the implied volatility layers under the future risk-free rate uncertainty

Bridges between multiple-point geostatistics and texture synthesis: Review and guidelines for future research

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