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Forecasting financial stress indices in Korea: a factor model approach

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Abstract

We propose factor-based out-of-sample forecast models for Korea’s financial stress index and its 4 subindices that are developed by the Bank of Korea. We extract latent common factors by employing the method of the principal components for a panel of 198 monthly frequency macroeconomic data after differencing them. We augment an autoregressive-type model of the financial stress index with estimated common factors to formulate out-of-sample forecasts of the index. Our models overall outperform both the stationary and the nonstationary benchmark models in forecasting the financial stress indices for up to 12-month forecast horizons. The first common factor that represents not only financial market but also real activity variables seems to play a dominantly important role in predicting the vulnerability in the financial markets in Korea.

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Notes

  1. Source: Organization for Economic Co-operation and Development, Total Share Prices for All Shares for the Republic of Korea [SPASTT01KRQ657N].

  2. For some of FSIs in the Euro, see Grimaldi (2010), Grimaldi (2011), Hollo et al. (2012), and Islami et al. (2013). There are FSIs for individual countries: Greece (Louzis and Vouldis 2011), Sweden (Sandahl et al. 2011), Canada (Illing and Liu 2006), Denmark (Hansen 2006), Switzerland (Hanschel and Monnin 2005), Germany (van Roye 2011), Turkey (Cevik et al. 2013), Colombia (Morales and Estrada 2010), and Hong Kong (Yiu et al. 2010).

  3. Misina and Tkacz (2009) investigated the predictability of credit and asset price movements for financial market stress in Canada. Kim and Shi (2015) implemented forecasting exercises for the FSI in the USA using a similar methodologies used in this paper.

  4. The 4 subindices are for the foreign exchange market, the stock market, the bond market, and the financial industry in Korea.

  5. The data are not publicly available and are for internal use only. We express our gratitude to give permission to use the data.

  6. We categorized these 198 variables into 13 groups that include an array of nominal and real activity variables.

  7. Principal component may not be ideal under certain circumstances. For example, Brave and Butters (2011) point out that this approach can have limitations when data are available at different data frequencies. On the other hand, the maximum likelihood and Kalman filter deal with the missing values and the varying frequency data observation. That is, weekly, monthly, and quarterly data with histories that potentially begin and end at different points of time could be used. We collected macroeconomic data up to 198 variables on the monthly basis without missing observations to match the frequency of the Bank of Korea’s FSI. Therefore, we believe PCA is a legitimate and appropriate approach for our purpose.

  8. Alternatively, one may use a recursive forecasting regression model that replaces \(\alpha _{j}\) with \(\alpha ^{j}\), where \(\alpha \) is the coefficient from an AR(1) model.

  9. ADF test results are available upon request.

  10. Following Andrews and Monahan (1992), we use the quadratic spectral kernel with automatic bandwidth selection for our analysis.

  11. We obtained permission from the Bank of Korea to use the data for this research.

  12. Regressions for the 1-, 3-, and 6-month ahead FSI indices yield similar patterns, although \(R^{2}\) values overall decline as the time horizon becomes larger.

  13. We used 70% initial observations.

  14. Note that some variables such as Korean stock indices and bilateral exchange rates were included in our baseline study.

  15. Alternative interpretation would be that our finding is consistent with a view that Korean policy makers were able to isolate domestic financial market conditions from external shocks. See Arregui et al. (2018) for detailed discussions on this issue.

  16. Duprey (2018) provides similar Canadian evidence.

  17. In a closely related paper, De Nicolò and Lucchetta (2017) demonstrate that quantile projection models have provided more accurate and reliable early warning signals than AR and factor augmented VAR models for tail real and financial risks in the USA up to a 1-year forecast horizon.

  18. For example in the USA, the Federal Reserve Bank of Cleveland interprets the Z-score Cleveland Financial Stress Index (CFSI) by dividing it into four levels or grades: Grade 1 Low stress period \(CFSI<-\,0.733\); Grade 2 Normal stress period \(-\,0.733\ge \hbox {CFSI}<0.544\); Grade 3 Moderate stress period \(0.544\ge \hbox {CFSI}<1.82\); and Grade 4 Significant stress period \(\hbox {CFSI}\ge 1.82\).

  19. We also implemented quantile analyses with two common factor estimates that performed similarly well.

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Authors and Affiliations

  1. Patrick E. Molony Professor of Economics, Auburn University, 138 Miller Hall, Auburn, AL, 36849, USA

    Hyeongwoo Kim

  2. Department of Accounting and Finance, Turner College of Business, Columbus State University, Columbus, GA, 31907, USA

    Wen Shi

  3. Department of Economics, Kookmin University, Seongbuk-gu, Seoul, Korea

    Hyun Hak Kim

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  1. Hyeongwoo Kim
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  2. Wen Shi
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  3. Hyun Hak Kim
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Correspondence to Wen Shi.

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Appendix

Appendix

See Figs. 9, 10, 11, and 12.

Fig. 9
figure 9

Factor estimates: differenced factors

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Fig. 10
figure 10

Factor estimates: level factors

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Fig. 11
figure 11

Factor loading coefficient estimates

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Fig. 12
figure 12

Marginal \(R^{2}\) analysis

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Kim, H., Shi, W. & Kim, H.H. Forecasting financial stress indices in Korea: a factor model approach. Empir Econ 59, 2859–2898 (2020). https://doi.org/10.1007/s00181-019-01744-y

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