| Peer-Reviewed

Use of Exponential Smoothing Technique in Estimation of Returns in a Financial Portfolio (A Case of the Matatu Public Transport Business in Kenya)

Received: 17 September 2015    Accepted: 7 October 2015    Published: 22 October 2015
Views:       Downloads:
Abstract

This study sought to develop consistent estimators for the conditional mean and conditional volatility using exponential smoothing technique and to use the estimators for the conditional mean and conditional volatility to estimate VaR and ES of a financial asset in a given financial portfolio. In particular, we take the Kenyan Matatu business as our financial portfolio and we estimate the ES of the daily returns obtained from Matatus travelling the Nairobi –Eldoret highway as provided by CLASSIC SACCO. In estimating the conditional mean and conditional volatility of the returns of our portfolio, the study explored the exponential smoothing technique, whereby exponentially decreasing weights were assigned to the returns. The study proved that the estimators for the conditional mean and conditional volatility are consistent and also that the estimators for the conditional mean and conditional volatility when conditional mean is known, are asymptotically normal. Further the study gives the estimators for the VaR and ES and proves that the VaR estimator is consistent.

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 6)
DOI 10.11648/j.ajtas.20150406.19
Page(s) 484-495
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Expected Shortfall, Exponential Smoothing, Value At Risk, Conditional Mean, Conditional Volatility

References
[1] Aas, K., & Dimakos, X. K. (2004). Statistical modelling of financial time series: An introduction. Norwegian Computing Center; APPLIED RESEARCH AND DEVELOPMENT.
[2] Alexander, M. F. (1974). Third edition. In M. F. Alexander, Introduction to the Theory of Statistics (pp. 241-242, 295-296). McGraw-Hill.
[3] Amin, A. N. (2009). Reducing Emissions from Private Cars: Incentive measures for behavioural change*. United Nations Environment Programme.
[4] André Lucasa, X. Z. (2014). Score Driven Exponentially Weighted Moving Averages and Value-at-Risk Forecasting. University Amsterdam and Tinbergen Institute.
[5] Artzner, P. D. (1997). Thinking coherently. Risk.
[6] Artzner, P. F. (1997). “Thinking Coherently,” Risk, 10 (11), Mathematical Finance, 68–71.
[7] Baki Billah, M. L. (2006, April–June). Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22(2), 239-247.
[8] Boudoukh, J. R. (1997). Investigation of a class of volatility estimators. Journal of Derivatives, 4 Spring,, 63-71.
[9] Claudio H. da S. Barbedo, G. S. (2005). Evaluation of Foreign Exchange Risk Capital Requirement Models. Brazilian Review of Finance, Vol 3, No 2.
[10] Communications, M. o. (2004). Republic of Kenya. Nairobi.: Transformation of Road Transport Report.
[11] Dorothy Mccormick, W. M. (2012). Paratransit Operations And Regulation In Nairobi Matatu Business Strategies And The Regulatory Regime unpublished.
[12] Dorothy McCormick, W. M. (2013). Institutions and Business Strategies of Matatu Operators: A Case Study Report. Nairobi: African center for excellence forstudies in public and non-motorised Transport.
[13] Dr. Erik Winands, S. L. (2009). FORECASTING VOLATILITY IN THE STOCK MARKET. amsterdam: VU University.
[14] Gardner, E. J. (1985). Exponential smoothing: the state of the art. Journal of Forecasting, 1-28.
[15] Gizycki., J. A. (1999). Value at Risk: Volatility and Forecasting of the Variance Covariance Matrix.
[16] Huy-Nhiem Nguyen, Q. N. (2010). Exploring the Cost of Forecast Error in Inventory Systems. Fayetteville, AR 72701, U.S.A: Department of Industrial Engineering;University of Arkansas.
[17] Kibua1, P. O. (2004). EFFORTS TO IMPROVE ROAD SAFETY IN KENYA, ACHIEVEMENTS AND LIMITATIONS OF REFORMS IN THE MATATU INDUSTRY. Nairobi: Institute of Policy Analysis and Research (IPAR).
[18] Ladokhin, S. (2009). Volatility modeling in financial markets. Amsterdam: VU University.
[19] Legal Notices Nos. 161, 8. a. (2003 and 2004). Republic of Kenya.
[20] Madhavan, A., & Yang, J. (2003). Practical Risk Analysis for Portfolio Managers and Traders. ITG Inc.
[21] McCormick, D. W. (2011). Business Strategies of Matatu Operators in Nairobi: A Scoping Study ACET Project 14 Paratransit operations and regulation in Nairobi. University of Nairobi.
[22] McCormick, D. W. (2011). Institutions and business strategies of matatu operators in Nairobi: A scoping study ACET. African Centre of Excellence for Studies of Public and Non-Motorised Tr, Working Paper No. 14-2.
[23] MOA. (2014, november 20). matatuownersassociation. Retrieved from matatu owners association website: http://www.matatuownersassociation.com/index.php/about-us/our-profile
[24] Mwita, P. (2003). Semi-Parametric Estimation of Conditional Quantiles for Times Series with applications in finance. PhD Thesis, University of Kaiserslautern.
[25] Nyasetia, O. B. (2013). The influence of entreprenuarial personality, human capital and entry barriers on performance of entreprenuers in the informal transport business in nairobi, kenya. Unpublished.
[26] Pat. (2013, may 28). value-at-risk-with-exponential-smoothing. Retrieved from portfolio probe: http://www.portfolioprobe.com/2013/05/28/value-at-risk-with-exponential-smoothing/.
[27] Poon, S.-H. (2005). A Practical Guide to Forecasting Financial Market Volatility.pg1. John Wiley & Sons.
[28] Rau-Bredow, H. (2002). Value at Risk, Expected Shortfall, and Marginal Risk Contribution. Leo Weismantel Str. 4 D-97074 Würzburg.
[29] Roussas, G. G. (1973). A First Course in Mathematical Statistics. Addison-Wesley.
[30] S. Srinidhi, A. K. (2013). A conceptual model for demand forecasting and service positioning in the airline industry. Journal of Modelling in Management, 8(1), 123 – 139.
[31] S., E. (2015, March 3). Four Principles for Great sales forecast.. Retrieved from forbes: http: www.forbes.com
[32] Sirikhorn Klindokmai, P. N. (2014). Evaluation of forecasting models for air cargo. The International Journal of Logistics Management, 25(3), 635 - 655.
[33] Suwanvijit, W. C. (2009). Statistical model for short-term forecasting sparkling beverage sales in Southern Thailand. International Business and Economics Research Journal, 73-81.
[34] Tasche, C. A. (2001, may 9). Expected Shortfall: a natural coherent alternative to Value at Risk. Expected Shortfall: a natural coherent alternative to Value at Risk. Milano, Milano, Italy. Retrieved from http://arxiv.org/pdf/cond-mat/0105191.pdf
[35] Taylor, J. W. (2008). Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall. Journal of Financial Econometrics, Vol. 6, pp.382-406.
[36] Tes´arov´a, B. V., & Gapko, P. P. (2012). Value at Risk: GARCH vs. Stochastic Volatility Models: Empirical Study. Prague: Charles University in Prague.
[37] Tian, G. J. (2008). Forecasting Volatility Using Long Memory and Comovements: An application to option valuation under SFAS 123R. Forthcoming Journal of Financial and Quantitative Analysis.
[38] Winands, S. L. (2009). Forecasting Volatility in the Stock Market. Amsterdam: VU University Amsterdam.
[39] Yamai, Y. Y. (2001). On the validity of value-at-risk: comparative analyses with Expected Shortfall. Institute for Monetary and Economic Studies, Bank of Japan.
[40] Yoshiba, Y. Y. (2002). On the Validity of Value-at-Risk: Comparative Analyses with Expected Shortfall. Research Division I, Institute for Monetary and Economic Studies, Bank of Japan.
Cite This Article
  • APA Style

    Jumba Minyoso Sandra, Joel Cheruiyot Chelule, Mungatu Joseph. (2015). Use of Exponential Smoothing Technique in Estimation of Returns in a Financial Portfolio (A Case of the Matatu Public Transport Business in Kenya). American Journal of Theoretical and Applied Statistics, 4(6), 484-495. https://doi.org/10.11648/j.ajtas.20150406.19

    Copy | Download

    ACS Style

    Jumba Minyoso Sandra; Joel Cheruiyot Chelule; Mungatu Joseph. Use of Exponential Smoothing Technique in Estimation of Returns in a Financial Portfolio (A Case of the Matatu Public Transport Business in Kenya). Am. J. Theor. Appl. Stat. 2015, 4(6), 484-495. doi: 10.11648/j.ajtas.20150406.19

    Copy | Download

    AMA Style

    Jumba Minyoso Sandra, Joel Cheruiyot Chelule, Mungatu Joseph. Use of Exponential Smoothing Technique in Estimation of Returns in a Financial Portfolio (A Case of the Matatu Public Transport Business in Kenya). Am J Theor Appl Stat. 2015;4(6):484-495. doi: 10.11648/j.ajtas.20150406.19

    Copy | Download

  • @article{10.11648/j.ajtas.20150406.19,
      author = {Jumba Minyoso Sandra and Joel Cheruiyot Chelule and Mungatu Joseph},
      title = {Use of Exponential Smoothing Technique in Estimation of Returns in a Financial Portfolio (A Case of the Matatu Public Transport Business in Kenya)},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {4},
      number = {6},
      pages = {484-495},
      doi = {10.11648/j.ajtas.20150406.19},
      url = {https://doi.org/10.11648/j.ajtas.20150406.19},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150406.19},
      abstract = {This study sought to develop consistent estimators for the conditional mean and conditional volatility using exponential smoothing technique and to use the estimators for the conditional mean and conditional volatility to estimate VaR and ES of a financial asset in a given financial portfolio. In particular, we take the Kenyan Matatu business as our financial portfolio and we estimate the ES of the daily returns obtained from Matatus travelling the Nairobi –Eldoret highway as provided by CLASSIC SACCO. In estimating the conditional mean and conditional volatility of the returns of our portfolio, the study explored the exponential smoothing technique, whereby exponentially decreasing weights were assigned to the returns. The study proved that the estimators for the conditional mean and conditional volatility are consistent and also that the estimators for the conditional mean and conditional volatility when conditional mean is known, are asymptotically normal. Further the study gives the estimators for the VaR and ES and proves that the VaR estimator is consistent.},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Use of Exponential Smoothing Technique in Estimation of Returns in a Financial Portfolio (A Case of the Matatu Public Transport Business in Kenya)
    AU  - Jumba Minyoso Sandra
    AU  - Joel Cheruiyot Chelule
    AU  - Mungatu Joseph
    Y1  - 2015/10/22
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ajtas.20150406.19
    DO  - 10.11648/j.ajtas.20150406.19
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 484
    EP  - 495
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20150406.19
    AB  - This study sought to develop consistent estimators for the conditional mean and conditional volatility using exponential smoothing technique and to use the estimators for the conditional mean and conditional volatility to estimate VaR and ES of a financial asset in a given financial portfolio. In particular, we take the Kenyan Matatu business as our financial portfolio and we estimate the ES of the daily returns obtained from Matatus travelling the Nairobi –Eldoret highway as provided by CLASSIC SACCO. In estimating the conditional mean and conditional volatility of the returns of our portfolio, the study explored the exponential smoothing technique, whereby exponentially decreasing weights were assigned to the returns. The study proved that the estimators for the conditional mean and conditional volatility are consistent and also that the estimators for the conditional mean and conditional volatility when conditional mean is known, are asymptotically normal. Further the study gives the estimators for the VaR and ES and proves that the VaR estimator is consistent.
    VL  - 4
    IS  - 6
    ER  - 

    Copy | Download

Author Information
  • Department of Statistics and Actuarial Studies, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Studies, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Studies, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Sections