The Analysis of the 2008 US Financial Crisis: An Intervention Approach

Makatjane, Katleho; Molefe, Edward Kagiso; Wyk, Roscoe Bertrum Van

doi:10.22610/jebs.v10i1(j).2089

Cited by 3 publications

(5 citation statements)

References 5 publications

(6 reference statements)

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“…Additionally, SARIMA model is proposed in this study to serve as a predecessor that is used to filter a time series in order to obtain i.i.d residuals. According to Makatjane et al (2018a), a multiplicative SARIMA model denoted by SARI MA(p, d, q) × (P, D, Q) S follows this mathematical form…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, the estimated Q-statistics also gave vast significant evidence that the estimated model is a white noise process with Pr(Qstatistic = 0.1732)and ε t ∼ i.i.d(0, 0.02978). Makatjane et al (2018a) have suggested that model parameter estimates must be less than one as to deem them to be sufficient and significant.…”

Section: Sarima-ms-egarch-gevd Modelmentioning

confidence: 99%

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Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index

Makatjane

Moroke

2021

IJFS

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During the past decades, seasonal autoregressive integrated moving average (SARIMA) had become one of a prevalent linear models in time series and forecasting. Empirical research advocated that forecasting with non-linear models can be an encouraging alternative to traditional linear models. Linear models are often compared to non-linear models with mixed conclusions in terms of superiority in forecasting performance. Therefore, the aim of this study is to build an early warning system (EWS) model for extreme daily losses for financial stock markets. A logistic model tree (LMT) is used in collaboration with a seasonal autoregressive integrated moving average-Markov-Switching exponential generalised autoregressive conditional heteroscedasticity-generalised extreme value distribution (SARIMA-MS-EGARCH-GEVD) estimates. A time series of the study is a five-day financial time series exchange/Johannesburg stock exchange-all share index (FTSE/JSE-ALSI) for the period of 4 January 2010 to 31 July 2020. The study is set into a two-stage framework. Firstly, SARIMA model is fitted to stock returns in order to obtain independently and identically distributed (i.i.d) residuals and fit the MS(k)-EGARCH(p,q)-GEVD to i.i.d residuals; while, in the second stage, we set-up an EWS model. The results of the estimated MS(2)-EGARCH(1,1) -GEVD revealed that the conditional distribution of returns is highly volatile giving the expected duration to approximately 36 months and 4 days in regime one and 58 months and 2 days in regime two. We further found that any degree losses above 25% implies that there will be no further losses. Using the seven statistical loss functions, the estimated SARIMA(2,1,0)×(2,1,0)240−MS(2)−EGARCH(1,1)−GEVD proved to be the most appropriate model for predicting extreme regimes losses as it was ranked at 71%. Finally, the results of EWS model exhibit reasonably an overall performance of 98%, sensitivity of 79.89% and specificity of 98.40% respectively. The model further indicated a success classification rate of 89% and a prediction rate of 95%. This is a promising technique for EWS. The findings also confirmed 63% and 51% of extreme losses for both training sample and validation sample to be correctly classified. The findings of this study are useful for decision makers and financial sector for future use and planning. Furthermore, a base for future researchers for conducting studies on emerging markets, have been contributed. These results are also important to risk managers and and investors.

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Section: Methodsmentioning

confidence: 99%

Section: Sarima-ms-egarch-gevd Modelmentioning

confidence: 99%

Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index

Makatjane

Moroke

2021

IJFS

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“…These rates may depend on the money available in the market, existing rates or on the agreement between the institutions. A rise in these rates may cause financial crisis similar to that experienced in 2007 (Bonner & Eijffinger, 2013;Turner, 2013;Saraç & Karagöz, 2016, Makatjane et al, 2018. The short term interest rates are very important for financial stability.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Short- Term Interest Rates on Bank Funding Costs

Nomsobo

Wyk

2018

JEBS

Self Cite

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This study examines the impact of short- term interest rates on bank funding costs in South Africa. Literature suggests that rising short- term interest rates may cause similar financial crises experienced in 2007/08 (Bonner & Eijffinger, 2013; Turner, 2013; SaraÃ§ & Karagoz, 2016). It is vital to study short- term interest rates and bank funding costs in order to achieve financial stability. The study uses quarterly time series data for the period 2000 to 2014. To estimate the regression, the study uses the Vector Autoregressive model (VAR) and the data is found stationary at first difference. The 3 months Johannesburg Interbank Agreed Rate (JIBAR) is used as a proxy for bank funding costs whilst the prime overdraft rate, 10 -year government bonds and capital ratio are used as proxies for short- term, long- term interest rates and bank capital, respectively. The results show a positive and significant long- term relationship between the variables. The results for prime overdraft rate, 10 -year government bonds and capital ratio conform to the apriori expectations. For GDP growth the results show a positive relationship which does not conform to apriori expectations. Using the variance decomposition, the study illustrates fluctuations in JIBAR was due to changes in its value and fluctuations in the prime rate are also due to JIBAR. The study presents policy options whereby regulatory efforts need to strengthen the capital buffers of banks to reduce bank funding costs and therefore reduce short- term interest rates imposed on borrowers.

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“…To solve this problem, the SARIMA model can be applied to forecast the selected value if the is non-stationary. Some of the researches regarding the implementation of SARIMA; see Lidiema (2017) and Makatjane et al (2018). In order to integrate the nature of the seasonal pattern within the data, seasonal differencing is applicable to handle seasonal data.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of Lidiema (2017), the model of SARIMA (1,1,0)(0,0,1) is appropriate for forecasting the inflation rates in Kenya. In the case of Makatjane et al (2018), the SARIMA model is implemented to study the impact of the 2008 financial crisis on regarding exchange rate in South Africa.…”

Section: Introductionmentioning

confidence: 99%

Exchange Rate Forecasting and Value-at-Risk Estimation on Indonesian Currency Using Copula Method

Sirait¹,

Simatupang²

2019

Proceedings of the 3rd International Conference on Accounting, Management and Economics 2018 (ICAME 2018)

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This study aims to determine the future value and the value-at-risk estimation of four selected currencies, namely United States Dollar (USD), Australian Dollar (AUD), European Union Euro (EUR) and Japanese Yen (JPY) against Indonesian Rupiah (IDR). The Monte-Carlo simulation is implemented to estimate the future value of each currency relationship and integrating it with the concept from the copula method; the risk value estimation is conducted using Value-at-Risk (VaR), and the VaR estimation is within the range of 90%, 95% and 99% confidence interval. The copula method we use in this study is Clayton copula because it has the highest log-likelihood value compared to Frank and Gumbel copula; with an addition, each currency uses their regressive model to see if the selected exchange rates have the characteristic of a seasonal or non-seasonal pattern. The result we obtain in this study are JPY/IDR, and EUR/IDR relationships have the highest simulated loss and estimated risk values in each confidence interval.

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The Analysis of the 2008 US Financial Crisis: An Intervention Approach

Cited by 3 publications

References 5 publications

Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index

Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index

The Impact of Short- Term Interest Rates on Bank Funding Costs

Exchange Rate Forecasting and Value-at-Risk Estimation on Indonesian Currency Using Copula Method

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