Taufiq Choudhry, Sarosh Shabi and Fotios I. Papadimitriou Southampton Management School

1. Stock Market Volatility, Business Cycle and the Financial Crisis: Evidence from Linear and Non-Linear Causality Tests Taufiq Choudhry, Sarosh Shabi and Fotios I. Papadimitriou Southampton Management School University of Southampton University of Sussex, 8 April

2. Research Theme • This paper studies the long run relationship between Stock Market Volatility and Business Cycles, by means of linear and non-linear bivariate and multivariate causality tests. • And, further investigates the impact of the financial crisis on this macro-financial relationship.

3. Contents • Introduction • Stock Market Volatility • Business Cycles • Financial Crisis Effect • Theoretical Framework • Research Questions • Data and Methodology • Results and Findings • Conclusion

4. Introduction • Business cycles are varying fluctuations found in economic activity (e.g. productivity, consumption, investment, unemployment) over a period of time. Cycles exhibit similarities across countries and over time (Burns and Mitchell, 1946). • Stock volatility is the fluctuation in stock prices/ returns due to arrival of new information that influences investor’s perceptions (Gregoriou, 2009 and Engle, 2011). Volatility varies over time and displays patterns in its movements (Schwert, 1989). It is higher in recession and lower in boom (Hamilton and Lin, 1996).

5. Financial Crisis • The current crisis led to massive decline in world industrial production and enormous drop in equity valuations. • Share prices started to fall from early 2008 in various countries, but the real equity disaster took account over 31 trading days (Sept - Oct 2008) as almost all indices collapsed by 30-40%. • Uncertainty hike reflected in the stock market volatility was four times higher than its average normal volatility level of 10-12%

6. Financial Crisis • The extremely high stock return volatility reflected spiked uncertainty that caught much attention and prompted speculation of the economic consequences of the crisis (Schwert, 2011). • However, the market did not expect the volatility to sustain for longer (Schwert, 2011) and thus volatility dwindled down after peaking in November 2008, although it did perk up twice afterwards. • The aggregate demand (by consumer) especially for imports dipped due to economic downturn around the globe and particularly in advanced countries. • Moreover, the halt in credit creation, on the supply (producer) side had a direct impact on exports potential due to excessive decline in external finance (including trade finance) (Chor and Manova, 2010).

7. Financial Crisis • The output dropped due to reduced demand for commodities and shrinkage of available financing for running the production units. The massive drop in output showed in the third quarter of 2008 onwards, in most of the countries around the world. • Given the massive adverse effect of the crisis on the stock market volatility and industrial production, it is empirical interest to investigate the effect of the crisis on the relationship between stock market volatility and industrial production (business cycles).

8. Industrial Production

9. Stock Market Volatility

10. Relationships Tested

11. Research Questions • Is there a causal (linear and non-linear) relationship between Stock Market Volatility and Business Cycles? • Is there a causal (linear and non-linear) relationship between stock market volatility of country A and business cycle of country B? • Did the current financial crisis affect the above relationships?

12. Data Countries: US, UK, Japan and Canada Time Period: • Before financial crisis: Jan 1990 – June 2007 (Monthly) • Full sample including crisis : Jan 1990 – December 2011 (Monthly) Variables: • Stock market volatility (Estimated from stock market indices) • Business Cycle (Output: Index of Production) Spill-over effect: For all countries US (being the largest national economy and most influential economic power) is considered as possible source of spill-over.

13. Methodology • Descriptive Statistics • Volatility Estimation – Asymmetric GARCH • Unit Root/Stationarity Tests • Causality Testing • Bivariate and Multivariate Linear • Bivariate Non-Linear (Hiemstra and Jones, 1994; Diks and Panchenko, 2006) • Multivariate Non-Linear (Bai, et al. 2010)

14. None-Linear Models • Why apply non-linear models? • Market frictions, such as transaction costs, information frictions, etc., can prevent the linear models converging to a long run equilibrium (Granger, 1989). • Market frictions give rise to asymmetric adjustments to equilibrium (Anderson, 1997). • Nonlinearities could be also be due to, diversity in agents’ beliefs, heterogeneity investors’ objectives, herd behaviour, etc.

15. Non-linear Models • Hiemstra and Jones (1994) developed a non-linear and nonparametric Granger causality test based on the work of Baek and Brock (1992). • Diks and Panchenko (2006) proposed a modified Hiemstra-Jones test. According to them the Hiemstra-Jones test is subjected to over-rejection bias on null hypothesis of Granger causality. • Bai et al., (2010) have extended the bivariate non-linear Granger causality model of Hiemstra-Jones test to multivariate setting.

16. Non-Linear Tests • For multivariate setting in this paper, there are total four variables, business cycle of country A, business cycle of country B, stock market volatility of country A and stock market volatility of country B. • The aim is to analyse whether non-linear causal relationship established between stock volatility and business cycle within country (say country ‘A’) spills across borders. • Thus business cycle of country B is assumed to bear a causal relationship with volatility of its own stock market ‘B’ and that of another country’s stock market ‘A’. • Country ‘A’ in all cases is US, as it is the biggest economy which has economic and political influence on co­untries around the globe.

17. Bivariate Linear Causality

18. Bivariate Non-Linear Causality Hiemstra and Jones (1994) Diks and Panchenko (2006)

19. Multivariate Linear CausalityCanada vs. US

20. Multivariate Linear CausalityJapan vs. US

21. Multivariate Linear CausalityUK vs. US

22. Multivariate Nonlinear Causality S-Vol Canada S-Vol US S-Vol UK BC Canada BC US BC UK

23. Conclusion • Evidence of linear bi-directional causality in case of Canada and UK, whereas, in case of US, feedback is only present for full sample period. Before the financial crisis US stock volatility causes its business cycles. • Nonlinear feedback effect is shown for UK, whereas, in case of Canada, feedback effect only shown after inclusion of financial crisis. • Cross country linear feedback effect between Canadian SV - American BC; and Canadian BC – American SV strengthen over the financial crisis period and strong evidence of mutual interdependence is found.

24. Conclusion • Financial crisis has made the cross country spill-over effect between stock market volatility and business cycle more evident. • Among UK, Canada and Japan, UK is found to have the strongest cross country interdependence with US.

25. Thank you • Q & A