Interest rates forecasting: Between Hull and White and the CIR#—How to make a single‐factor model work

Orlando, Giuseppe; Bufalo, Michele

doi:10.1002/for.2783

Cited by 18 publications

(8 citation statements)

References 32 publications

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“…In addition, the number of healed and deceased could also be studied, although the latter, being much smaller, will present the aforementioned problems. In terms of considered models, future research could include a comparison with the more advanced CIR# by Orlando et al [19,[24][25][26]. In addition, although our work has only been done for one equation, it could also be generalized to systems of equations to discover the interrelation between different cities.…”

Section: Discussionmentioning

confidence: 99%

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Modeling COVID-19 pandemic with financial markets models: The case of Jaén (Spain)

Guerrero¹,

Galiano²,

Orlando³

2023

Preprint

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The main objective of this work is to test whether some stochastic models typically used in financial markets could be applied to the COVID-19 pandemic. To this end we have implemented the ARIMAX and Cox-Ingersoll-Ross (CIR) models originally designed for interest rate pricing but transformed by us into a forecasting tool. For the latter, which we denoted CIR*, both the Euler-Maruyama method and the Milstein method were used. Forecasts obtained with the maximum likelihood method have been validated with 95% confidence intervals and with statistical measures of goodness of fit, such as the root mean square error (RMSE). We demonstrate that the accuracy of the obtained results is consistent with the observations and sufficiently accurate to the point that the proposed CIR* framework could be considered a valid alternative to the classical ARIMAX for modelling pandemics.

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Section: Discussionmentioning

confidence: 99%

“…In this study, similar to what has been done by Orlando and at. [19,[24][25][26], when developing the CIR# model, we transform the original CIR model into a forecasting tool and compare its performance with that of the well known ARIMAX model.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling COVID-19 pandemic with financial markets models: The case of Jaén (Spain)

Guerrero¹,

Galiano²,

Orlando³

2023

Preprint

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“…We use the time series

V_{s}

to calibrate the parameter vector

ν_{t}^{σ}

on the rolling window

{scriptI}_{s}

by using the maximum likelihood (ML) estimation method, implemented in Matlab (see Kladıvko, 2007). We choose this method because it provides good results even with a rolling window of modest size, differently from more sophisticated methodologies existing in financial literature (see, e.g., Orlando & Bufalo, 2019, 2021, Section 4.4), whose estimates are efficient just on the rolling window with size greater than

L

.…”

Section: Materials Methods and Techniquesmentioning

confidence: 99%

A generalized two‐factor square‐root framework for modeling occurrences of natural catastrophes

Orlando

Bufalo

2022

Journal of Forecasting

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This work aims to forecast (over 1, 5, and 15 years) the extremes, the expected value, and the volatility of natural disasters occurrences. To achieve this objective, we adopt a generalized two‐factor square‐root model linking together occurrences and volatility through stochastic correlation (Brownian motion). We use a generalized Pareto distribution (GPD) to forecast the maximum number of occurrences as a measure of value at risk (VaR). The results are checked in terms of accuracy, compared versus some baseline models (i.e., the Poisson process and the extreme value model) and backtested.

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“…Comparison with benchmark models is a common approach in forecasting research, such as, a forecasting tool based on the ARIMAX and Cox-Ingersoll-Ross (CIR) models [53], the interest rates forecasting via a modified version of the CIR model [54,55]. Hence, we contrast the six suggested strategies with the ARIMA-GARCH model in order to further compare them with the benchmark model.…”

Section: Plos Onementioning

confidence: 99%

The roles of liquidity and delay in financial markets based on an optimal forecasting model

Yang,

Ma,

Bian

et al. 2023

PLoS ONE

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We investigate the roles of liquidity and delay in financial markets through our proposed optimal forecasting model. The efficiency and liquidity of the financial market are examined using stochastic models that incorporate information delay. Based on machine learning, we estimate the in-sample and out-of-sample forecasting price performances of the six proposed methods using the likelihood function and Bayesian methods, and the out-of-sample prediction performance is compared with the benchmark model ARIMA-GARCH. We discover that the forecasting price performance of the proposed simplified delay stochastic model is superior to that of the benchmark methods by the test methods of a variety of loss function, superior predictive ability test (SPA), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Using data from the Chinese stock market, the best forecasting model assesses the efficiency and liquidity of the financial market while accounting for information delay and trade probability. The rise in trade probability and delay time affects the stability of the return distribution and raises the risk, according to stochastic simulation. The empirical findings show that empirical and best forecasting approaches are compatible, that company size and liquidity (delay time) have an inverse relationship, and that delay time and liquidity have a nonlinear relationship. The most efficient have optimal liquidity.

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Interest rates forecasting: Between Hull and White and the CIR#—How to make a single‐factor model work

Cited by 18 publications

References 32 publications

Modeling COVID-19 pandemic with financial markets models: The case of Jaén (Spain)

Modeling COVID-19 pandemic with financial markets models: The case of Jaén (Spain)

A generalized two‐factor square‐root framework for modeling occurrences of natural catastrophes

The roles of liquidity and delay in financial markets based on an optimal forecasting model

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