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Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study

Year 2016, Volume: 8 Issue: 2, 53 - 71, 30.09.2016
https://doi.org/10.33818/ier.278045

Abstract

Several empirical studies in finance have examined whether or not the risk associated with any stock market responds differently in two different states of the stock market, especially in bull and bear markets. This paper studies this problem in the modelling framework, where (i) the conditional mean specification considers threshold autoregressive model for two market situations characterized as up and down markets, (ii) the conditional variance (as a measure of time-varying risk) specification is asymmetric in the sense of capturing leverage effect, and (iii) the conditional variance directly affects the conditional mean through the risk premium term in the risk-return relationship. Using daily returns on stock indices of eight countries, comprising four developed countries - the USA, the UK, Hong Kong, Japan - and four important emerging economies, called the BRIC group of countries viz., Brazil, Russia, India and China, we have found that the nature of risk-return relationship is different in up and down markets. Furthermore, the risk aversion parameter, which is significant in most of the countries, is positive in the down markets and negative in the up markets. This finding supports the hypothesis of Fabozzi and Francis (1977) and Kim and Zumwalt (1979), namely, that investors require a premium for taking downside risk and pay a premium for upside variation; moreover, the findings confirm that the nature of risk-return relationship is same for the two groups of countries.

References

  • Bai, J. and P. Perron (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66 (1), 47–78.
  • Bai, J. and P. Perron (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18 (1), 1–22.
  • Bailey, W. (1994). Risk and return on china’s new stock markets: Some preliminary evidence. Pa iŞ -Basin Finance Journal, 2 (2), 243–260.
  • Bekaert, G. and G. Wu (2000). Asymmetric volatility and risk in equity markets. Review of Financial Studies, 13 (1), 1–42
  • Bhar, R. and B. Nikolova (2009). Return, volatility spillovers and dynamic correlation in the bric equity markets: An analysis using a bivariate egarch framework. Global Finance Journal, 19 (3), 203–218.
  • Bhardwaj, R.K. and L.D. Brooks (1993). Dual betas from bull and bear markets: Reversal of the size effect. Journal of Financial Research, 16 (4), 269–83.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31 (3), 307–327.
  • Brannas, K. and J.G. DeGooijer (2004). Asymmetries in conditional mean and variance: modelling stock returns by asma-asqgarch. Journal of Forecasting, 23 (3), 155–171.
  • Campbell, J.Y. and L. Hentschel (1992). No news is good news *1: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31 (3), 281–318.
  • Chan, K., J. Petruccelli, S. Woolford and H. Tong (1985). A multiple threshold AR(1) model. Journal of Applied Probability, 22 (2), 267–279.
  • Chan, K.S. and H. Tong, (1985). On the use of the deterministic lyapunov function for the ergodicity of stochastic difference equations. Advances in Applied Probability, 17 (3), 666–678.
  • Chan, K.S. and H. Tong (1986). On estimating thresholds in autoregressive models. Journal of Time Sereries Analysis, 7, 179–190.
  • Chen, S.-N. (1982). An examination of risk-return relationship in bull and bear markets using time-varying betas. Journal of Financial and Quantitative Analysis, 17 (02), 265–286.
  • Chen, S.-S. (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators. Journal of Banking & Finance, 33 (2), 211–223.
  • Crombez, J. and R.V. Vennet (2000). Risk/return relationship conditional on market movements on the Brussels stock exchange. Tijdschrift voor Economie en Management, 45, 163–188.
  • De Santis, G. and S. Imrohoroglu (1997). Stock Returns and Volatility in Emerging Financial Markets. Journal of International Money and Finance, 16 (4), 561–579.
  • Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica, 50 (4), 987–1007.
  • Engle, R.F., D.M. Lilien and R.P. Robins (1987). Estimating time varying risk premia in the term structure: The arch-m model. Econometrica, 55 (2), 391–407.
  • Fabozzi, F.J. and J.C. Francis, (1977). Stability tests for alphas and betas over bull and bear market conditions. Journal of Finance, 32 (4), 1093–99.
  • Fabozzi, F.J. and J.C. Francis (1978). Beta as a random coefficient. Journal of Financial and Quantitative Analysis, 13 (01), 101–116.
  • Faff, R. (2001). A multivariate test of a dual-beta capm: Australian evidence. Financial Review, 36, 157–174.
  • Franses, P. H. and D. Dijk (2000). Non-Linear Time Series Models in Empirical Finance. No. 9780521779654 in Cambridge Books. Cambridge University Press.
  • French,K.R.,G.W.Schwertand R.F. Stambaugh (1987). Expected stock returns and volatility. Journal of Financial Economics, 19 (1), 3–29.
  • Galagedera, D.U.A. and R. Faff (2005). Modeling the risk and return relation conditional on market volatility and market conditions. International Journal of Theoretical and Applied Finance (IJTAF), 8 (01), 75–95.
  • Glosten, L.R., R. Jagannathan and D. Runkle (1993). On the relattionship between the expected value and the volatility of the normal excess return on stocks. Journal of Finance, 3, 1779–1801.
  • Gonzalez-Rivera, G. (1998). Smooth-transition garch models. Studies in Nonlinear Dynamics & Econometrics, 3 (2), 1.
  • Granger, C.W.J. and P. Silvapulle (2002). Capital asset pricing model, bear, usual and bull market conditions and beta instability: A value-at-risk approach. NBER Working Paper 1062.
  • Hagerud, G.E. (1997). A smooth transition arch model for asset returns. Working Paper Series in Economics and Finance 162. Stockholm School of Economics.
  • Harvey, C.R. (1995). Predictable risk and returns in emerging markets. Review of Financial studies, 8 (3), 773–816.
  • Howton, S.W. and D.R. Peterson (1998). An examination of cross-sectional realized stock returns using a varying-risk beta model. The Financial Review, 33 (3), 199–212.
  • Kim, M.K. and J.K. Zumwalt (1979). An analysis of risk in bull and bear markets. Journal of Financial and Quantitative Analysis, 14, 1015–1025.
  • Kulp-Tag, S. (2007). Short-horizon asymmetric mean-reversion and overreactions: Evidence from the Nordic stock markets. Working Papers 524. Hanken School of Economics.
  • Kundu, S. and N. Sarkar (2016). Return and volatility interdependences in up and down markets across developed and emerging countries. Research in International Business and Finance, 36, 297–311.
  • Levy, R. (1974). Beta coefficient as predictors of returns. Financial Analysts Journal, 61–69.
  • Li, C.W. and W.K. Li (1996). On a double-threshold autoregressive heteroscedastic time series model. Journal of Applied Econometrics, 11 (3), 253–74.
  • Lundbergh, S. and T. Terasvirta (1998). Modelling economic high-frequency time series with star-stgarch models. Working Paper Series in Economics and Finance 291. Stockholm School of Economics.
  • Nam, K., (2003). The asymmetric reverting property of stock returns. Studies in Nonlinear Dynamics & Econometrics, 6 (4), 2.
  • Nam, K., C.S. Pyun, and S. L. Avard (2001). Asymmetric reverting behavior of short-horizon stock returns: An evidence of stock market overreaction. Journal of Banking & Finance, 25 (4), 807–824.
  • Nam, K., C.S. Pyun and S.-W. Kim (2003). Is asymmetric mean-reverting pattern in stock returns systematic? evidence from paciŞc-basin markets in the short-horizon. Journal of International Financial Markets, Institutions and Money, 13 (5), 481–502.
  • Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59 (2), 347–70.
  • Silvennoinen, A. and S. Thorp (2013). Financialization, crisis and commodity correlation dynamics. Journal of International Financial Markets, Institutions and Money, 24, 42– 65.
  • Terasvirta, T. (1994). SpeciŞcation, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89 (125), 208– 211.
  • Tong, H. (1978). On a Threshold Model. In Pattern Recognition and Signal Processing, ed. C.H. Chen. Amsterdam: Sijthoff & Noordhoff, 101–141.
  • Turner, C.M., R. Startz and C.R. Nelson (1989). A markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics, 25 (1), 3–22.
Year 2016, Volume: 8 Issue: 2, 53 - 71, 30.09.2016
https://doi.org/10.33818/ier.278045

Abstract

References

  • Bai, J. and P. Perron (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66 (1), 47–78.
  • Bai, J. and P. Perron (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18 (1), 1–22.
  • Bailey, W. (1994). Risk and return on china’s new stock markets: Some preliminary evidence. Pa iŞ -Basin Finance Journal, 2 (2), 243–260.
  • Bekaert, G. and G. Wu (2000). Asymmetric volatility and risk in equity markets. Review of Financial Studies, 13 (1), 1–42
  • Bhar, R. and B. Nikolova (2009). Return, volatility spillovers and dynamic correlation in the bric equity markets: An analysis using a bivariate egarch framework. Global Finance Journal, 19 (3), 203–218.
  • Bhardwaj, R.K. and L.D. Brooks (1993). Dual betas from bull and bear markets: Reversal of the size effect. Journal of Financial Research, 16 (4), 269–83.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31 (3), 307–327.
  • Brannas, K. and J.G. DeGooijer (2004). Asymmetries in conditional mean and variance: modelling stock returns by asma-asqgarch. Journal of Forecasting, 23 (3), 155–171.
  • Campbell, J.Y. and L. Hentschel (1992). No news is good news *1: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31 (3), 281–318.
  • Chan, K., J. Petruccelli, S. Woolford and H. Tong (1985). A multiple threshold AR(1) model. Journal of Applied Probability, 22 (2), 267–279.
  • Chan, K.S. and H. Tong, (1985). On the use of the deterministic lyapunov function for the ergodicity of stochastic difference equations. Advances in Applied Probability, 17 (3), 666–678.
  • Chan, K.S. and H. Tong (1986). On estimating thresholds in autoregressive models. Journal of Time Sereries Analysis, 7, 179–190.
  • Chen, S.-N. (1982). An examination of risk-return relationship in bull and bear markets using time-varying betas. Journal of Financial and Quantitative Analysis, 17 (02), 265–286.
  • Chen, S.-S. (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators. Journal of Banking & Finance, 33 (2), 211–223.
  • Crombez, J. and R.V. Vennet (2000). Risk/return relationship conditional on market movements on the Brussels stock exchange. Tijdschrift voor Economie en Management, 45, 163–188.
  • De Santis, G. and S. Imrohoroglu (1997). Stock Returns and Volatility in Emerging Financial Markets. Journal of International Money and Finance, 16 (4), 561–579.
  • Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica, 50 (4), 987–1007.
  • Engle, R.F., D.M. Lilien and R.P. Robins (1987). Estimating time varying risk premia in the term structure: The arch-m model. Econometrica, 55 (2), 391–407.
  • Fabozzi, F.J. and J.C. Francis, (1977). Stability tests for alphas and betas over bull and bear market conditions. Journal of Finance, 32 (4), 1093–99.
  • Fabozzi, F.J. and J.C. Francis (1978). Beta as a random coefficient. Journal of Financial and Quantitative Analysis, 13 (01), 101–116.
  • Faff, R. (2001). A multivariate test of a dual-beta capm: Australian evidence. Financial Review, 36, 157–174.
  • Franses, P. H. and D. Dijk (2000). Non-Linear Time Series Models in Empirical Finance. No. 9780521779654 in Cambridge Books. Cambridge University Press.
  • French,K.R.,G.W.Schwertand R.F. Stambaugh (1987). Expected stock returns and volatility. Journal of Financial Economics, 19 (1), 3–29.
  • Galagedera, D.U.A. and R. Faff (2005). Modeling the risk and return relation conditional on market volatility and market conditions. International Journal of Theoretical and Applied Finance (IJTAF), 8 (01), 75–95.
  • Glosten, L.R., R. Jagannathan and D. Runkle (1993). On the relattionship between the expected value and the volatility of the normal excess return on stocks. Journal of Finance, 3, 1779–1801.
  • Gonzalez-Rivera, G. (1998). Smooth-transition garch models. Studies in Nonlinear Dynamics & Econometrics, 3 (2), 1.
  • Granger, C.W.J. and P. Silvapulle (2002). Capital asset pricing model, bear, usual and bull market conditions and beta instability: A value-at-risk approach. NBER Working Paper 1062.
  • Hagerud, G.E. (1997). A smooth transition arch model for asset returns. Working Paper Series in Economics and Finance 162. Stockholm School of Economics.
  • Harvey, C.R. (1995). Predictable risk and returns in emerging markets. Review of Financial studies, 8 (3), 773–816.
  • Howton, S.W. and D.R. Peterson (1998). An examination of cross-sectional realized stock returns using a varying-risk beta model. The Financial Review, 33 (3), 199–212.
  • Kim, M.K. and J.K. Zumwalt (1979). An analysis of risk in bull and bear markets. Journal of Financial and Quantitative Analysis, 14, 1015–1025.
  • Kulp-Tag, S. (2007). Short-horizon asymmetric mean-reversion and overreactions: Evidence from the Nordic stock markets. Working Papers 524. Hanken School of Economics.
  • Kundu, S. and N. Sarkar (2016). Return and volatility interdependences in up and down markets across developed and emerging countries. Research in International Business and Finance, 36, 297–311.
  • Levy, R. (1974). Beta coefficient as predictors of returns. Financial Analysts Journal, 61–69.
  • Li, C.W. and W.K. Li (1996). On a double-threshold autoregressive heteroscedastic time series model. Journal of Applied Econometrics, 11 (3), 253–74.
  • Lundbergh, S. and T. Terasvirta (1998). Modelling economic high-frequency time series with star-stgarch models. Working Paper Series in Economics and Finance 291. Stockholm School of Economics.
  • Nam, K., (2003). The asymmetric reverting property of stock returns. Studies in Nonlinear Dynamics & Econometrics, 6 (4), 2.
  • Nam, K., C.S. Pyun, and S. L. Avard (2001). Asymmetric reverting behavior of short-horizon stock returns: An evidence of stock market overreaction. Journal of Banking & Finance, 25 (4), 807–824.
  • Nam, K., C.S. Pyun and S.-W. Kim (2003). Is asymmetric mean-reverting pattern in stock returns systematic? evidence from paciŞc-basin markets in the short-horizon. Journal of International Financial Markets, Institutions and Money, 13 (5), 481–502.
  • Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59 (2), 347–70.
  • Silvennoinen, A. and S. Thorp (2013). Financialization, crisis and commodity correlation dynamics. Journal of International Financial Markets, Institutions and Money, 24, 42– 65.
  • Terasvirta, T. (1994). SpeciŞcation, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89 (125), 208– 211.
  • Tong, H. (1978). On a Threshold Model. In Pattern Recognition and Signal Processing, ed. C.H. Chen. Amsterdam: Sijthoff & Noordhoff, 101–141.
  • Turner, C.M., R. Startz and C.R. Nelson (1989). A markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics, 25 (1), 3–22.
There are 44 citations in total.

Details

Subjects Business Administration
Other ID JA42HU82DB
Journal Section Articles
Authors

Srikanta Kundu This is me

Nityananda Sarkar This is me

Publication Date September 30, 2016
Submission Date September 30, 2016
Published in Issue Year 2016 Volume: 8 Issue: 2

Cite

APA Kundu, S., & Sarkar, N. (2016). Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study. International Econometric Review, 8(2), 53-71. https://doi.org/10.33818/ier.278045
AMA Kundu S, Sarkar N. Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study. IER. December 2016;8(2):53-71. doi:10.33818/ier.278045
Chicago Kundu, Srikanta, and Nityananda Sarkar. “Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study”. International Econometric Review 8, no. 2 (December 2016): 53-71. https://doi.org/10.33818/ier.278045.
EndNote Kundu S, Sarkar N (December 1, 2016) Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study. International Econometric Review 8 2 53–71.
IEEE S. Kundu and N. Sarkar, “Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study”, IER, vol. 8, no. 2, pp. 53–71, 2016, doi: 10.33818/ier.278045.
ISNAD Kundu, Srikanta - Sarkar, Nityananda. “Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study”. International Econometric Review 8/2 (December 2016), 53-71. https://doi.org/10.33818/ier.278045.
JAMA Kundu S, Sarkar N. Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study. IER. 2016;8:53–71.
MLA Kundu, Srikanta and Nityananda Sarkar. “Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study”. International Econometric Review, vol. 8, no. 2, 2016, pp. 53-71, doi:10.33818/ier.278045.
Vancouver Kundu S, Sarkar N. Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study. IER. 2016;8(2):53-71.