Forecasting interest rates through Vasicek and CIR models: A partitioning approach

Orlando, Giuseppe; Mininni, Rosa Maria; Bufalo, Michele

doi:10.1002/for.2642

Cited by 30 publications

(24 citation statements)

References 20 publications

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“…As empirically investigated in Orlando and Bufalo, 3 returns, either standardized or not, do not seem to be unconditionally normally distributed. They often show a significant amount of skewness and extra‐kurtosis 4‐7 . Skew normal distributions were first introduced by Azzalini 8 and Henze 9 and have gained some momentum because of their suitability in modeling real data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In terms of applications, parametric models and model‐free are the basis of the so‐called dynamic conditional score (DCS) 13 where the updating of the score function is determining a sort of partitioning of the dataset 14,15 . In fact, DCS models often employ

t

‐skew distributions and they have been used for a variety of applications: value at risk (VaR) and expected shortfall (ES), 16 FX, 17 mortgage defaults, 18 and so forth.…”

Section: Literature Reviewmentioning

confidence: 99%

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Forecasting portfolio returns with skew‐geometric Brownian motions

Bufalo

Liseo

Orlando

2022

Appl Stoch Models Bus & Ind

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The gist of this work is to propose a minimum tracking error portfolio that could be adopted not only as an automated alternative to ETFs but, it could also be potentially used to anticipate market changes in the target index. This goal has been achieved by adopting skew Brownian motion as a general framework.The proposed solution has been declined in two versions: the case in which the constituents (i.e., in our case the subindices) of the objective portfolio are uncorrelated among each other, and the case in which correlation should be taken into account. Our tests, carried out on the S&P 500, as an example of a developed market, and Bovespa, as an example of an emerging market, shows that the proposed solutions replicate the index with a much smaller tracking error than that of the ETFs considered.

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Section: Literature Reviewmentioning

confidence: 99%

t

‐skew distributions and they have been used for a variety of applications: value at risk (VaR) and expected shortfall (ES), 16 FX, 17 mortgage defaults, 18 and so forth.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting portfolio returns with skew‐geometric Brownian motions

Bufalo

Liseo

Orlando

2022

Appl Stoch Models Bus & Ind

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“…Thus, the market needs a model that is able to cope with changing regimes, shocks, and negative rates. To this end, we have proposed within the CIR framework Orlando et al (2018Orlando et al ( , 2019aOrlando et al ( , 2019bOrlando et al ( , 2020 a new methodology that fits well the structure of interest rates and preserves the analytical tractability of the original CIR model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Apart from turning a short-rate model used for pricing into a forecasting tool, the novelty of the CIR# consists in an appropriate partitioning of the dataset into subgroups. To this end, in Orlando et al (2020), it was shown how the said partitioning enables us to capture statistically significant time changes in volatility of interest rates. This, in turn, implies modeling sudden changes/jumps in data.…”

Section: Introductionmentioning

confidence: 99%

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

Orlando

Bufalo

2021

Journal of Forecasting

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In this work, we present our findings of the so‐called CIR#, which is a modified version of the Cox, Ingersoll, and Ross (CIR) model, turned into a forecasting tool for any term structure. The main feature of the CIR# model is its ability to cope with negative interest rates, cluster volatility, and jumps. By considering a dataset composed of money market interest rates during turmoil and calmer periods, we show how the CIR# performs in terms of directionality of rates and forecasting error. Comparison is carried out with a revamped version of the CIR model (denoted CIRadj), the Hull and White model, and the exponentially weighted moving average (EWMA) which is often adopted whenever no structure in data is assumed. To confirm the analysis, testing and validation is performed on both historical and ad hoc data with different metrics and clustering criteria.

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“…Orlando et al suggest in several papers (cf. [22][23][24]) a new framework, which they call CIR# model, that fits the term structure of interest rates. Additionally, it preserves the market volatility, as well as the analytical tractability of the original CIR model.…”

Section: Review Of the Literature And Comparisonmentioning

confidence: 99%

How to handle negative interest rates in a CIR framework

Francesco¹,

Kamm

2021

SeMA

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In this paper, we propose a new model to address the problem of negative interest rates that preserves the analytical tractability of the original Cox–Ingersoll–Ross (CIR) model without introducing a shift to the market interest rates, because it is defined as the difference of two independent CIR processes. The strength of our model lies within the fact that it is very simple and can be calibrated to the market zero yield curve using an analytical formula. We run several numerical experiments at two different dates, once with a partially sub-zero interest rate and once with a fully negative interest rate. In both cases, we obtain good results in the sense that the model reproduces the market term structures very well. We then simulate the model using the Euler–Maruyama scheme and examine the mean, variance and distribution of the model. The latter agrees with the skewness and fat tail seen in the original CIR model. In addition, we compare the model’s zero coupon prices with market prices at different future points in time. Finally, we test the market consistency of the model by evaluating swaptions with different tenors and maturities.

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Forecasting interest rates through Vasicek and CIR models: A partitioning approach

Cited by 30 publications

References 20 publications

Forecasting portfolio returns with skew‐geometric Brownian motions

Forecasting portfolio returns with skew‐geometric Brownian motions

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

How to handle negative interest rates in a CIR framework

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