Forecasting Short Term Interest Rates Using ARMA, ARMA-GARCH and ARMA-EGARCH Models

Radha, S.; Thenmozhi, M.

doi:10.2139/ssrn.876556

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…These result report that the volatility rate of both Energy and MLPs ETFs are highly persistent and vary highly in the time series. Radha and Thenmozhi (2006) predicted short-term interest rates and found that the GARCH model was more predictive than other models because of the volatility of clusters.…”

Section: Table 4: Arma-garch Assessments For Energy and Mlps Etfsmentioning

confidence: 99%

Jump Dynamics and Leverage Effect: Evidences from Energy Exchange Traded Fund (ETFs)

Chen¹,

Hussain²

2022

Journal of Applied Finance &Amp; Banking

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This paper is concerned with the behavior of energy ETF prices. It applies three models: autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH), along with their revised forms, ARMA–Exponential-GARCH, Glosten-Jagannathan-Runkle (GJR), and GARCH diffusion process with jump models. This study looks at the volatility behavior and jumps dynamics of Energy and Master Limited Partnership's (MLP) ETFs. The results show that ARMA-GARCH is appropriate for modeling energy and MLP ETFs. Both ETFs offer positive leverage and asymmetric volatility. The results show that the jump model with a GARCH volatility specification has an actual amount of jump presence and time variation in the jump size distribution. The conclusion of the ARMA - EGARCH model gives evidence of the reverse leverage effect. The leverage term positively influences the conditional variance, while the asymmetry coefficient for the GJR model is positive and significant. These results reveal that both Energy and MLPs ETF have high volatility. JEL classification numbers: F3. Keywords: Energy ETFs, MLPs, ARMA-GARCH model, Volatility Asymmetry, Leverage and Jump Effect.

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Section: Table 4: Arma-garch Assessments For Energy and Mlps Etfsmentioning

confidence: 99%

Jump Dynamics and Leverage Effect: Evidences from Energy Exchange Traded Fund (ETFs)

Chen¹,

Hussain²

2022

Journal of Applied Finance &Amp; Banking

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“…Ren [14] applied two methodologies namely ARIMA and GARCH to detect the bond repo rate. The final outcome implied that the predictive impact of ARIMA technique was optimal when compared to GARCH approach.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

et al. 2020

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The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products.

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“…Inflation, GDP growth or unemployment rate can be taken into account as macroeconomic variables in order to forecast future interest rates. Stochastic Diffusion Processes (see, e.g., Fischer and Zechner, 1984) and more advanced time series analysis (see, e.g., Radha and Thenmozhi, 2006) offer a wider range of possibilities to forecast interest rates. As a special form of stochastic diffusion process, the LIBOR Market Model (see, e.g., Brace, Garek and Musiela, 1997) can be used.…”

Section: Typical Loan Offersmentioning

confidence: 99%

How to choose between fixed- and variable-rate loans

Fischer

Kampl

2019

oeba

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The current low interest rate landscape influences the decision whether to secure these low longterm interest rates with a fixed rate agreement or to benefit from the current negative short-term rates with a variable rate loan. The decision is based on expected future interest rates. We propose two criteria, namely the effective interest rate and the total repayments, to decide what type of loan is more advantageous. In this context, we also present the effects of varying future interest rates on the three selected repayment agreements; i.e. lump sum, constant principal and annuity repayments.

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Forecasting Short Term Interest Rates Using ARMA, ARMA-GARCH and ARMA-EGARCH Models

Cited by 10 publications

References 13 publications

Jump Dynamics and Leverage Effect: Evidences from Energy Exchange Traded Fund (ETFs)

Jump Dynamics and Leverage Effect: Evidences from Energy Exchange Traded Fund (ETFs)

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

How to choose between fixed- and variable-rate loans

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