바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

Information in the Implied Volatility Curve of Option Prices and Implications for Financial Distribution Industry

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2015, v.13 no.5, pp.53-60
https://doi.org/https://doi.org/10.15722/jds.13.5.201505.53
Kim, Sang-Su
Liu, Won-Suk
Son, Sam-Ho
  • Downloaded
  • Viewed

Abstract

Purpose - The purpose of this paper is to shed light on the importance of the slope and curvature of the volatility curve implied in option prices in the KOSPI 200 options index. A number of studies examine the implied volatility curve, however, these usually focus on cross-sectional characteristics such as the volatility smile. Contrary to previous studies, we focus on time-series characteristics; we investigate correlation dynamics among slope, curvature, and level of the implied volatility curve to capture market information embodied therein. Our study may provide useful implications for investors to utilize current market expectations in managing portfolios dynamically and efficiently. Research design, data, and methodology - For our empirical purpose, we gathered daily KOSPI200 index option prices executed at 2:50 pm in the Korean Exchange distribution market during the period of January 2, 2004 and January 31, 2012. In order to measure slope and curvature of the volatility curve, we use approximated delta distance; the slope is defined as the difference of implied volatilities between 15 delta call options and 15 delta put options; the curvature is defined as the difference between out-of-the-money (OTM) options and at-the-money (ATM) options. We use generalized method of moments (GMM) and the seemingly unrelated regression (SUR) method to verify correlations among level, slope, and curvature of the implied volatility curve with statistical support. Results - We find that slope as well as curvature is positively correlated with volatility level, implying that put option prices increase in a downward market. Further, we find that curvature and slope are positively correlated; however, the relation is weakened at deep moneyness. The results lead us to examine whether slope decreases monotonically as the delta increases, and it is verified with statistical significance that the deeper the moneyness, the lower the slope. It enables us to infer that when volatility surges above a certain level due to any tail risk, investors would rather take long positions in OTM call options, expecting market recovery in the near future. Conclusions - Our results are the evidence of the investor's increasing hedging demand for put options when downside market risks are expected. Adding to this, the slope and curvature of the volatility curve may provide important information regarding the timing of market recovery from a nosedive. For financial product distributors, using the dynamic relation among the three key indicators of the implied volatility curve might be helpful in enhancing profit and gaining trust and loyalty. However, it should be noted that our implications are limited since we do not provide rigorous evidence for the predictability power of volatility curves. Meaning, we need to verify whether the slope and curvature of the volatility curve have statistical significance in predicting the market trough. As one of the verifications, for instance, the performance of trading strategy based on information of slope and curvature could be tested. We reserve this for the future research.

keywords
KOSPI 200 Index Options, Implied Volatility, Slope, Curvature, Seemingly Unrelated Regression(SUR), Financial Distribution Industry

Reference

1.

Bakshi, Gurdip., Kapadia, Narendra, & Madan, Dilip (2003). Stock return characteristics, skew laws, and the differential pricing of individual equity options. Review of Financial Studies, 16(1), 101-143.

2.

Banerjee, Shantanu, & Gavrishchaka, Valeriy (2006). Support vector machine as an efficient framework for stock market volatility forecasting. Computational Management Science, 3(2), 147-160.

3.

Bates, David S. (1991). The crash of '87: Was it expected? The evidence from options markets. The journal of finance, 46(3), 1009-1044.

4.

Bedendo, Mascia, & Hodges, Stewart D. (2009). The dynamics of the volatility skew: A Kalman filter approach. Journal of Banking & Finance, 33(6), 1156-1165.

5.

Black, Fischer, & Scholes, Myron (1973). The pricing of options and corporate liabilities. The Journal of Political Economy, 81, 637-654.

6.

Bollerslev, Tim (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327.

7.

Carr, Peter, & Wu, Liuren (2007). Stochastic skew in currency options. Journal of Financial Economics, 86(1), 213-247.

8.

Doran, James S., & Krieger, Kevin (2010). Implications for asset returns in the implied volatility skew. Financial Analysts Journal, 66(1), 65-76.

9.

Doran, James S., Peterson, David R., & Tarrant, Brian C. (2007). Is there information in the volatility skew?, Journal of Futures Markets. 27(10), 921-959.

10.

Engle, Robert F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica, 50, 987-1007.

11.

Giot, Pierre (2005). Relationships between implied volatility indexes and stock index returns. The Journal of Portfolio Management, 31(3), 92-100.

12.

Hang, Gao (2012). Study on changes and development trends of the trade structure between korea and china. The East Asian Journal of Business Management, 2(1), 19-23.

13.

Heston, Steve L. (1993). A closed-form solution for options with stochastic volatility with applications to bond and currency options. The Review of Financial Studies, 6(2), 327-343.

14.

Heynen, Ronald (1994). An empirical investigation of observed smile patterns. Review of Futures Markets, 13, 317-317.

15.

Hull, John, Nelken, Izzy, & White, Alan (2004). Merton’s model, credit risk, and volatility skews. Journal of Credit Risk Volume, 1(1), 05.

16.

Hull, John, & White, Alan (1987). The pricing of options on assets with stochastic volatilities. The Journal of Finance, 42, 281–300.

17.

Kwon, Young-Man, Park, Jin-Soo, & Kim, Myung-Gwan (2014). Beacon-based O2O marketing for financial institutions. The International Journal of Industrial Distribution &Business, 5(4), 23-29.

18.

Lee, Jung-Wan, & Zhao, Tianyuan F. (2014). Dynamic relationship between stock prices and exchange rates :Evidence from chinesestock markets. The Journal of Asian Finance, Economics and Business, 1(1), 65-14.

19.

Manaster, Steven, & Rendleman, Richard J. (1982). Option prices as predictors of equilibrium stock prices. The Journal of Finance, 37(4), 1043-1057.

20.

Merton, Robert C. (1976). Option pricing when underlying stock returns are discontinuous. Journal of Financial Economics, 3, 125–144.

21.

Mixon, Scott (2009). Option markets and implied volatility: Past versus present. Journal of Financial Economics, 94(2), 171-191.

22.

Toft, Klaus B., & Prucyk, Brian (1997). Options on leveraged equity: Theory and empirical tests. The Journal of Finance, 52(3), 1151-1180.

23.

Wolak, Frank A. (1989). Testing inequality constraints in linear econometric models. Journal of econometrics, 41(2), 205-235.

24.

Xu, Xinzhong, & Taylor, Stephen J. (1994). The term structure of volatility implied by foreign exchange options. Journal of Financial and Quantitative Analysis, 29(01), 57-74.

The Journal of Distribution Science