The Information Content of Indian Implied Volatility Index

3 The Information Content of Indian Implied Volatility Index 3.1 Introduction Most of the investors believe that stock prices, even when rising, climb a wall of uncertainty and worry. When volatility and investors sentiment about the future go hand in hand, the forward looking measure of volatility implied by the option prices is often quoted as bona fide investors fear gauge (Whaley, 2). Implied volatility has become so important that it is being reported on routine basis by financial news agencies and is closely followed by investors for making investment decisions. As a result, the study of information content and forecast quality of implied volatility has become an important topic in financial research. The researchers study the information content of implied volatility in two ways. The first class deals with the contemporaneous association between implied volatility index and market returns, which is rather limited in the finance literature. Second, category is focused on analyzing its ability to forecast future volatility and to empirically examine the relationship between realized and implied volatility. The present chapter aims to study the first dimension for the financial market of an emerging economy like India. These volatility indices can be used (i) to make inferences on current stock market returns, (ii) to learn about the current volatility expectations quoted in the market, and/or (iii) to anticipate future uncertainty in market returns. A negative relationship Association between Volatility Index (VIX) and S&P CNX Nifty in India 61

between changes in implied volatility index and market returns has been documented in the literature for the various financial markets. These results give foundation to the interpretation of implied volatility index as a measure of capturing market sentiments and risks. The arrival of news to the market, a sudden increase in the trading volume and the number of orders crossed may produce a negative relationship between volatility changes and index returns. In particular, an increased level of uncertainty in markets, due to the release of some economic data figures or some political announcements or policy changes that increase risk, may cause an upward surge in financial market volatility. At the same time, these changes induce pressure on the selling decisions of the investors; that may lead to generate negative returns. Whaley (2), Simon (23) and Giot (25) found a negative contemporaneous relationship between volatility changes and index returns in American markets. They found that arrival of bad news may induce a larger volatility increase than the arrival of good news of same relevance. Therefore, if this asymmetric negative relationship between volatility changes and index returns is confirmed, the information in volatility index can become an important element in portfolio management. In the light of the above uses of volatility index as a measure of expected volatility, the empirical analysis of the present study is initiated by studying the information content of implied volatility index of India (henceforth IVIX). The present chapter is devoted to investigate the following issues: (i) (ii) (iii) The major macroeconomic events and volatility index; Statistical properties and seasonal patterns in IVIX; Leverage effect and asymmetry in risk-return relationship and (iv) Short-term asymmetric return-volatility relation. The above issues are analyzed by developing the following hypothesis: Hypothesis I: A negative contemporaneous correlation exists between innovations in IVIX and S&P CNX Nifty returns. Hypothesis II: Contemporaneous negative and positive index returns are the most important determinants of changes in implied volatility. Association between Volatility Index (VIX) and S&P CNX Nifty in India 62

Hypothesis III: The lagged returns and/or changes in the past implied volatilities determine current changes in the implied volatility. Hypothesis IV: Asymmetry exists for the return-volatility relation, that is, negative returns have much higher impact on volatility than the positive returns. Hypothesis V: In short-term return-volatility relation, contemporaneous returns are the most important determinants of the largest changes in the implied volatility index. The asymmetry is implied to vary across the quantiles of IV change distribution, particularly from the median quantile to the uppermost quantile of IV change distributions. The given hypotheses are tested empirically using the analysis structured on bi-partite levels. Firstly, the univariate properties and seasonality of time-series history of IVIX are examined. Secondly, the temporal relationship between volatility and stock market returns is examined using three different methodologies. The changes in implied volatility index are regressed on: lead-lag, contemporaneous and contemporaneous absolute stock market returns; on positive and negative returns; and finally, the shortterm return-volatility relationship is determined using the quantile regression method. 3.2 Empirical analysis 3.2.1 Daily and weekly movements of IVIX: The current section examines daily and weekly movements of implied volatility Index of India and its underlying index S&P CNX Nifty during sample period since March, 29 till June, 212 (Figure 3.1 and Figure 3.2). The preliminary analysis intends to capture the movements of IVIX and S&P CNX Nifty during the sample period and their behavior surrounding some important economic events. The univariate properties of the changes in IVIX and S&P CNX Nifty are computed, along with IVIX cross-correlations with its underlying index returns of S&P CNX Nifty. The approach followed here is based on the methodology adopted by Fleming et al. (1995) for assessing the behavior of CBOE Market volatility index. It is the conventional technique of examining the risk-return relation and is widely used in many studies to analyze the intertemporal relationship between the implied volatility indices and market returns of different countries (Dowling & Muthuswamy, 25; Frijns et al. 21) Association between Volatility Index (VIX) and S&P CNX Nifty in India 63

S&P CNX Nifty IVIX S&P CNX Nifty IVIX Figure 3.1: Daily movements of S&P CNX Nifty and IVIX 7 6 6 5 4 3 2 1 5 4 3 2 1 3-2-9 7-2-9 11-2-9 3-2-1 7-2-1 11-2-1 3-2-11 7-2-11 11-2-11 3-2-12 Date S&P CNX Nifty IVIX Source: Authors Figure 3.2: Weekly movements of S&P CNX Nifty and IVIX 7 6 5 4 3 2 1 6 5 4 3 2 1 Date Nse Vix Source: Authors 3.2.2 Worldwide macroeconomic shocks and IVIX: Figure 3.3 plots the daily closing IVIX at levels against absolute magnitude of S&P CNX Nifty index returns. The absolute returns can be interpreted as a measure of risk i.e. volatility of an asset (Granger & Sin, 2; Taylor, 1986). Thus, Figure 3.3 shows the movements in the Indian volatility index in accordance with the key events that occurred in the financial markets at global and domestic levels. Figure 3.3 shows that several periods of Association between Volatility Index (VIX) and S&P CNX Nifty in India 64

relatively high volatility and large stock market moves stand out. IVIX has reached the highest value of 56.7 on May 19, 29 and the lowest value of 15.22 on September 6, 21, thus, dropping almost 3 times in this period. At the start of data period (April, 29), there was an atmosphere of political uncertainty due to which IVIX rose to 56. This peak value of IVIX was indicating a high level of nervousness in the markets. In November 29, debt crisis enveloped Dubai markets; which spread a panic alarm across the world stock markets. Indian markets also took a sharp plunge, with banking and realty sectors being the worst sufferers. The Greek debt crisis in May, 21 resulted in rise of more than 23% in IVIX, as it stayed in a narrow range for the preceding couple of months. This reflected a spurt in uncertainty and panic in the Indian stock markets and IVIX reached the level of 34.37 in May, 21. After this, the index declined to the lowest level of 15.22 in Sept, 21 and attained a plateau level 22 in the end of year 21. The Ireland crisis coupled with hike in the interest rates by the Chinese government also affected volatility of the Indian markets. For the first six months of 211, India registered an impressive growth in FDI inflows. Stock market volatility represented by VIX declined from 26 levels at the beginning of 211 to around 2 levels, indicating that investor were gaining confidence in the Indian markets. But, in Aug, 211 S&P lowered its long-term sovereign on the US from AAA to AA+. This move triggered a fresh round of shockwaves in global markets and IVIX witnessed the sharpest rise of over 3% in the preceding two years. The index shot up 23% to close at 24.9, compared to previous day close of 2.22. In October 211, there was a return of optimism in the equity markets on account of positive news flow from the Eurozone and IVIX dropped to the levels that one saw only prior to the US downgrade. At this point the IVIX dropped by 7.93% to close at 2.89. Apart from the global factors, India s domestic problems were also accountable for the under performance of the markets in 211. The main issues were the supply constraints in important sectors such as energy, food and mineral resources and policy paralysis which brought a halt to the fresh investments in the country. Association between Volatility Index (VIX) and S&P CNX Nifty in India 65

S&P CNX Nifty Index Returns (Absolute VAlue, %) Indian Implied Volatility Index Figure 3.3: Daily closing IVIX and S&P CNX Nifty index return absolute values during the period from March, 29 through June, 212* 18 16 14 UPA Govt election victory in India Satyam Accounting 6 5 12 1 8 6 Dubai debt worries/ restructuring Greek government Ireland's crisis & Chinese stock fell rate hike Highest inflow of FDI in 39 months in Indian stock markets S& P Downgrade US credit rating IIP for Apr-Oct, 211-12 stood at 3.5% against 8.7%last yr GDP slumped to a 9-yr - low of 5.3% & depreciat es against $ 4 3 2 4 2 CAG unearths 2G scam which leads to arrest of ex-telecom minister A Raja and cancellation of 1 Date S&P CNX Nifty Returns IVIX Source: Authors *Note: The S&P CNX Nifty returns are reported on the basis of the log index relative multiplied 1 Association between Volatility Index (VIX) and S&P CNX Nifty in India 66

In 212, India s economic expansion plunged to a near-decade low of 5.3% in March quarter as the debt crisis in Europe, its top trading partner, curbed exports and policy gridlocks deterred investments. Until mid-212, the IVIX behavior pattern suggested a bullish trend in the markets. IVIX in April-May had retested an intermediate high of 29.12. From the first week of June, it started declining, and had broken below the crucial support level of 2. After this level, bulls started gaining strength. During the period covered, the spikes in IVIX were usually accompanied by large moves, up and down in the underlying stock index levels. However, there were few exceptions for instance there was no change in IVIX value at the time of Satyam accounting fraud. Again in October 212, the Index of Industrial Production (IIP) witnessed a negative growth of (-5.6%) and IVIX declined by.88% to 29.27. Thus, barring few exceptions, the IVIX remained fairly stable when higher changes were observed in S&P CNX Nifty. 3.2.3 IVIX normal range: In this section, the normal and abnormal behavior of IVIX shall be studied in a probabilistic sense (Table 3.1). The low, high and normal readings of IVIX vary in different kinds of market conditions. Over the entire sample period, the median daily closing level of IVIX is 24.15. In the sample set, 5% of the times IVIX closed between 2.7225 and 28.675 (a range of 7.885 points), 75% of the times IVIX closed between 18.919 and 37.659 (a range of 18.74 points) and 9% times IVIX closed between 17.9845 and 42.4975 (a range of 24.513 points). Table 3.1 also reports a great variation in what is considered normal from year to year. For example in 21, the median closing level of IVIX is 2.98 and 5% of the time IVIX remained in the range of 19.36 and 23.28 and 9% of the time between 29.933 and 16.996. The widest range was experienced in 29 when the IVIX closed between 25.314 and 51.8795 (range of 26.57 index points). The second widest range was experienced in 211 the year in which stock markets followed the bearish trend. The 5% and 95% percentile values indicate that the range of daily IVIX levels was from 19.131 to 27.277. These results are similar to the findings of Dhanaiah et al. (212) for the Indian stock market. Association between Volatility Index (VIX) and S&P CNX Nifty in India 67

Table 3.1: Normal range for daily levels of IVIX over sample period, March, 29 through June, 212 and year-wise Period Obs 5% 1% 25% 5% 75% 9% 95% Mar 9-Jun 12 83 17.985 18.919 2.723 24.15 28.68 37.66 42.498 Sub-periods Mar 9-Dec 9 24 25.314 26.242 28.35 36.575 4.98 48.395 51.88 21 252 16.996 17.632 19.36 2.98 23.28 28.87 29.933 211 247 18.23 18.65 2.41 23.16 26.79 29.374 32.7 Jan 12- Jun 12 127 19.131 2.66 21.74 23.55 25.95 26.362 27.277 An important way of judging market anxiety is to examine the persistence with which IVIX remains above the extraordinary levels during the entire sample period. From Table 3.1, it is observed that the chance of IVIX remaining above 42.498 is 5%. The history of IVIX is re-examined to count the number of consecutive days IVIX remained above 42.498 index points. Three periods: March 2, 29 to March 4, 29 (3 days); April 8, 29 to May 26, 29 (29 days) and June 17, 29 to July 3, 29 (1 days) can be demarcated for demonstrating abnormal behavior in the history of IVIX. 3.2.4 Statistical properties of Indian volatility index: In this section the statistical properties of implied volatility index changes and its underlying index returns are described. The empirical analysis of volatility index is based on the changes or innovations in the implied volatility denoted by:... (1) where, V t denotes implied volatility index at level at time t. There are three considerations which support the use of change in the implied volatility index. Firstly, the academicians and practitioners are interested in changes or innovations in the expected volatility. They want to know how security valuation is influenced with changes in expected volatility. Secondly, if stock prices follow a random walk (Figure 3.1 & 3.2), estimation of relationship between volatility and stock indexes at level may produce spurious results. Thirdly, Figure 3.1 shows that IVIX at levels appear to follow a near-random walk pattern. Further, Table 3.2 & 3.3 states the descriptive properties and unit root statistics of daily IVIX and S&P CNX Nifty at levels. The value of autocorrelation function are positive and significant 1% levels, which means Association between Volatility Index (VIX) and S&P CNX Nifty in India 68

that the series is first order serially correlation i.e. autoregression of order one (AR (1)) or volatility persistent behavior. The unit root statistics show that daily IVIX levels series is non-stationary and first difference series are stationary (Table 3.3).Thus, the presence of high degree of autocorrelation may affect the inference in finite samples. Table 3.2: Descriptive statistics for daily S&P CNX Nifty and IVIX at levels Series S&P CNX Nifty IVIX Mean 514.957 26.158 Median 5186.1 24.15 Maximum 6312.45 56.7 Minimum 2573.15 15.22 Std. Dev. 646.393 7.73 Skewness -1.41 1.447 Kurtosis 6.174 4.896 Jarque-Bera 619.944 414.2 Probability.. Observations 83 83 ρ(1).986*.978* ρ(2).971*.958* ρ(3).956*.939* Note: *, **, *** represents 1%, 5% and 1% level of significance Table 3.3: Unit root test statistics Test/ Series ADF PP KPSS Levels 1 2 3 1 2 3 1 2 S&P CNX Nifty -2.878 (1) IVIX -3.64 (1) First Difference ΔS&P CNX Nifty -7.96 (9)* Δ IVIX -21.239 (1)* Asymptotic critical values -3.449 (1) -3.353 (1) -11.35 (6)* -21.232 (1)*.8 (1) -1.243 (4) -7.852 (9)* -21.239 (1)* -2.675 (12) -2.873 (12) -27.746 (11)* -31.384 (16)* -3.38 (13) -3.183 (1) -27.922 (14)* -31.386 (16)*.86 (9) -1.432 (17) -27.665 (9)* -31.366 (16)* 1.296 (23)* 1.43 (23)*.645 (14).66 (18).673 (23)*.611 (23)*.131 (14).3 (18) 1% level -3.463-4.4-2.576-3.462-4.3-2.576.739.216 5% level -2.876-3.432-1.942-2.875-3.432-1.942.463.146 1% level -2.574-3.14-1.616-2.574-3.139-1.616.347.119 Note: ADF is the Augmented Dickey-Fuller, PP is the Phillips-Perron, and KPSS is Kwiatkowski, Phillips, Schmidt, and Shin test. Model Specification: 1.Intercept, 2. Intercept+trend, 3.None. The Null hypothesis for ADF and PP test: H = Variable is non-stationary and for KPSS: H = Variable is stationary. *, ** and *** indicate the rejection of the null hypothesis at the 1%, 5% and 1% significance levels, respectively. The proper lag order for ADF test is chosen by considering Akaike Information Criteria, representing in parenthesis. For KPSS and PP tests, the bandwidth is chosen using Newey West method and spectral estimation uses Bartlett kernel represented in parenthesis. Association between Volatility Index (VIX) and S&P CNX Nifty in India 69

The univariate properties of changes in implied volatility index are considered first. Summary statistics for daily and weekly IVIX changes over the entire sample period and for calendar-year sub-periods are estimated. Table 3.4 reports the properties of change in daily closing implied volatility index. The mean volatility change over the entire sample period is -.291 and ranges from -.98 (29) to.43 (211) for the calendar sub-periods. The standard deviation of the daily volatility changes (volatility of volatility) for the entire sample period is 1.4929. The standard deviation in the calendar sub-periods ranges from 1.168 in 212 to 1.855 in 29. Not surprisingly, higher value of standard deviation reported in the year 29 is due to financial crisis of a premier bank of America, Lehman Brothers, which enveloped the whole world in 28. The autocorrelation structure of the daily IVIX for lag one, two and three are provided in the Table 3.4. The autocorrelation structure of changes in daily IVIX varies substantially from year to year, unlike mean and standard deviation which are found to be relatively stable during the calendar-year sub-periods. The first-order autocorrelations for the sub-periods, range from -.153 in 212 to -.29 in 211 and is significant at 1% level in the year 212. The autocorrelation functions (ACF) for the entire sample period at one through three lags are -.63, -.11 and -.71 respectively and are significant at 1% level for lag one and three. The values for the ACF are near to zero for all lags which indicates randomness in the dataset. The autocorrelation at lag 1 is negative and significant in the year 212 and at lag 2 it is positive in the year 21, 211 and 212. Similarly, ACFs at lag 3 is negative and significant in 211, hence indicating that changes in volatility follow a mean reverting process. Similar results have been documented by Kumar (212). Table 3.4 also summarizes the properties of daily S&P CNX Nifty 5 index returns. The mean value of index returns during the entire sample period is.82 and ranges from -.114 in 211 to.328 in 29. The volatility of market returns represented by standard deviation during the entire sample is 2.99.The volatility of returns is changing over the sample period, providing some preliminary evidence of timevarying volatility. The stock return distribution is found to be non-normal and is positively skewed. The ACF for one through two lags is near zero and positive. Association between Volatility Index (VIX) and S&P CNX Nifty in India 7

Table 3.4: Statistical properties of daily closing Indian volatility index level changes and S&P CNX Nifty returns a Volatility Index Changes S&P CNX Nifty Cross Correlations b Period Obs. Mean SD ρ(1) ρ(2) ρ(3) Mean SD ρ(1) ρ(2) ρ(3) -2-1 1 2 Entire Sample Mar,9-Jun, 12 829 -.291 1.4929 -.63*** -.11 -.71***.82 1.459.35.1 -.3.41.165 -.482 -.17 -.46 Year wise 29 23 -.98 1.855 -.76 -.12.41.328 2.99.18 -.7 -.22.15 -.11 -.238.293 -.23 21 252 -.27 1.233 -.58.85 -.55.66 1.24 -.36.41 -.7.26.6 -.677.21 -.18 211 247.43 1.554 -.29.37 -.245* -.114 1.321.12***.45 -.114***.56.25 -.711 -.81 -.76 212 127 -.63 1.168 -.153***.3.31.14 1.118 -.73.81 -.4 -.22.2646 -.614.1154 -.98 Note: a The mean, standard deviation (SD), and autocorrelation (ρ ) for the first three lags are provided for the changes in the volatility index and S&P CNX Nifty returns along with the cross correlation between the volatility index innovations and S&P CNX Nifty index returns. b Correlations for negative (positive) lags/lead denotes correlation between the volatility index changes and past (future) S&P CNX Nifty index returns.*, **, *** denotes 1%, 5% and 1% level of significance Table 3.5: Statistical properties of weekly Indian volatility index level changes and S&P CNX Nifty returns a Volatility Index Changes S&P CNX Nifty Cross Correlations b Period Obs. Mean SD ρ(1) ρ(2) ρ(3) Mean SD ρ(1) ρ(2) ρ(3) -2-1 1 2 Entire Sample Mar,9-Jun, 12 173 -.124 2.386.43 -.21 -.69.39 2.799.172**.138**.77**.896 -.361 -.3824 -.144 -.39 Year wise 29 43 -.413 2.767.28 -.153 -.145 1.581 3.93.5.159.33.1863 -.1178 -.69 -.19.15 21 52 -.127 2.22.38.119 -.42.299 1.833.281**.111*** -.3 -.792.89 -.612 -.81 -.956 211 52.178 2.469 -.127 -.33 -.29 -.496 2.535.137 -.137 -.7.993.146 -.66 -.212 -.4 212 26 -.243 1.865 -.21.89 -.46.375 1.97.326***.263***.2***.1866 -.1118 -.449 -.13 -.115 Note: a The mean, standard deviation (SD), and autocorrelation (ρ ) for the first three lags are provided for the changes in the volatility index and S&P CNX Nifty returns along with the cross correlation between the volatility index innovations and S&P CNX Nifty index returns. b Correlations for negative (positive) lags/lead denotes correlation between the volatility index changes and past (future) S&P CNX Nifty index returns.*, **, *** denotes 1%, 5% and 1% level of significance Association between Volatility Index (VIX) and S&P CNX Nifty in India 71

Consistent with Hypothesis I, there exists negative contemporaneous correlation of -.482 between changes in IVIX and S&P CNX Nifty returns. The highest negative correlation of -.711 is found in the year 211 and lowest of -.238 in year 29. Positive correlations are reported between IVIX and lead and lagged index returns. However, the magnitude of correlation in the intertemporal periods is small in significant number of cases. As the theory predicts, there is fall (rise) in stock prices when expected volatility rises (falls). The correlation dampens out quickly to small levels at non-contemporaneous lags. As per Fleming et al. (1995) and Dowling & Muthuswany (25) the magnitude of negative correlation reported for America and Australia is higher, being.615 and.453 respectively. According to Fleming et al. (1995) the intertemporal correlations are positive and small across all the sub-periods. A similar phenomenon is observed for the Indian markets - indicating that they are heading to become efficient as IVIX tries to adjust rapidly with the stock index returns. The statistical properties of weekly volatility changes and market returns are summarized in Table 3.5. The mean and standard deviation of weekly data for changes in volatility index and stock market returns exhibit patterns similar to those apparent in daily data. The mean of weekly volatility index changes is -.124 for the entire sample and ranges from -.413 in 29 to.178 in 211, which is approximately four times their corresponding level in Table 3.4. The volatility of the weekly IVIX changes ranges from a low of 1.865 in 212 to 2.767 in 29. The mean of market returns ranges from -.496 in 211 to 1.581 in 29. The autocorrelation structure of the weekly changes in IVIX and index returns are also provided in Table 3.5. The weekly autocorrelation function of changes in IVIX at lag one is positive and insignificant. The autocorrelation function for market returns for lags one through three is positive and significant at 5% level for the entire sample. Unlike daily autocorrelations, the weekly correlation for market returns is positive and significant in most of the cases. The temporal association between volatility changes and stock market returns, reported in Table 3.4 for the daily observations, is also present in Table 3.5 for weekly observations. The contemporaneous correlations is -.3824 for the entire sample period, and a moderate range of -.4482 in 212 to -.66 in 211 is observed in the calendar-years except for 29 in which very small value of correlation is reported. Positive relationship between implied volatility and past-week and future-week stock Association between Volatility Index (VIX) and S&P CNX Nifty in India 72

Frequency Frequency Frequency Frequency market returns is also reported in Table 3.5. The cross-correlations for the noncontemporaneous lags show greater variability from year to year. 3.2.5 Distributional properties - Non-parametric test: Figure 3.4, 3.5 and 3.6 draws the histograms, corresponding kernel density function and Q-Q plot respectively to present the distributional properties of IVIX, changes in IVIX, S&P CNX Nifty and S&P CNX Nifty returns. Histograms provide three very important pieces of information about distribution of data values: shape, central location (the middle), and spread. The histograms are constructed in order to make them comparable with kernel density functions, for the relative frequency scaled by each bin width. The kernel densities is non-parametric test in which window width, or the smoothing parameter or bandwidth consists of Silverman bandwidth (Silverman, 1986), and the linear binning method is chosen. The Epanecknikov kernel function is chosen for optimal bandwidth as it tends to minimize the Approximate Mean Integrated Squared Error (AMISE criterion) (Wand & Jones, 1995). In addition, the Q-Q Plots are drawn, which depicts of the percentiles (or quintiles) of a standard normal distribution against the corresponding percentiles of the observed data. If the observations follow approximately a normal distribution, the resulting plot should be roughly a straight line with a positive slope. Figure 3.4: The histograms of IVIX, changes in IVIX, S&P CNX Nifty and S&P CNX Nifty returns. 2 IVIX 16 Changes in IVIX 16 12 12 8 4 8 4 1 2 3 4 5 6-8 -4 4 8 1 8 S&P CNX Nifty 3 25 S&P CNX Nifty returns 6 4 2 2, 3, 4, 5, 6, 7, 2 15 1 5-1 -5 5 1 15 2 Association between Volatility Index (VIX) and S&P CNX Nifty in India 73

Quantiles of Normal Quantiles of Normal Quantiles of Normal Quantiles of Normal Density Density Density Density Figure 3.5: The kernel density distributions of IVIX, changes in IVIX, S&P CNX Nifty and S&P CNX Nifty returns..1 IVIX.4 Changes in IVIX.8.3.6.4.2.2.1. 1 2 3 4 5 6. -8-4 4 8.1 S&P CNX Nifty.4 S&P CNX Nifty returns.8.3.6.4.2.2.1. 2, 3, 4, 5, 6, 7,. -1-5 5 1 15 2 Figure 3.6: The Q-Q Plot of IVIX, changes in IVIX, S&P CNX Nifty and S&P CNX Nifty returns. 6 5 IVIX 6 4 Changes in IVIX 4 2 3 2 1 1 2 3 4 5 6 Quantiles of IVIX -2-4 -6-8 -4 4 8 Quantiles of Changes in IVIX 8, 7, S&P CNX Nifty 6 4 S&P CNX Nifty returns 6, 2 5, 4, 3, 2, 2, 3, 4, 5, 6, 7, Quantiles of S&P CNX Nifty -2-4 -6-1 -5 5 1 15 2 Quantiles of S&P CNX Nifty returns Association between Volatility Index (VIX) and S&P CNX Nifty in India 74

The combination of histograms, kernel density and Q-Q plots (Figure 3.4, 3.5 and 3.6) are the graphical methods of studying the probability distributions of a data series. The histograms (Figure 3.4) show that the IVIX series at levels has a large positive skewness as its tails extends toward the right side whereas the S&P CNX Nifty at levels is skewed towards left side. The histograms for the changes in IVIX and index returns show that the series follow a normal distribution. The bump of kernel density functions (Figure 3.5) of IVIX and S&P CNX Nifty at levels is more widened to that of first difference series of IVIX and S&P CNX Nifty. The Q-Q plots (Figure 3.6) of the data at level show deviation from the straight line, which depicts that the data is non-normal. These Q-Q plots confirm the results of histograms of IVIX and S&P CNX Nifty at levels. It shows that the data series of IVIX is skewed toward right and S&P CNX Nifty is skewed towards left and both have heavier tails as compared to normal distributions. The Q-Q plots for first difference series have approximately a normal distribution, as both series follow a straight line with positive slope pattern. 3.2.6 Seasonal patterns in IVIX: Having considered some of the statistical properties of IVIX, the issue of whether the IVIX contains any seasonal or predictable patterns is addressed here. Figure 3.7 shows the intraweek volatility index behavior; it shows the average IVIX on different days of a week. The average level of IVIX starts high on Monday and falls systematically during the week. The total drop in average level of IVIX from Monday to Friday is.65 basis points. The average volatility index level increases from Tuesday to Wednesday and decreases from Thursday to Friday. However this increase and decrease is around.2 and.7 basis points respectively. A regression equation in which daily changes in IVIX on dummy variables for the different days of the week is performed to determine the seasonality in IVIX changes is:....(2) where is close-to-close changes in the volatility index, i indicates the day of the week (i = 1 is Monday, etc.) and is the dummy variable for each day of the week, Monday through Friday, which is equal to 1 on day i and otherwise. Therefore, the regression indicates whether the average change from one day to the next is significant for different days of the week. Association between Volatility Index (VIX) and S&P CNX Nifty in India 75

Average IVIX Figure 3.7: Intraweek behaviour of IVIX 26.6 Average IVIX 26.4 26.2 26 25.8 Average IVIX 25.6 25.4 Monday Tuesday Wednesday Thursday Friday Day Regression results reported in Table 3.6 show that two days in a week namely Monday and Thursday, are associated with significant changes in IVIX. The coefficient of Monday is significant in the entire sample which indicates the presence of weekend effect in IVIX returns, a finding similar to that of Kumar (212) and Fleming et al. (1995). It is found that IVIX decreases significantly from Monday to Friday, similar to the findings of Frijns et al. (21) for Australian markets. However, presence of seasonality is weak considering the adjusted R 2 of the regression (.2). The regression results for various sub-periods show that in 29 and 21 a significant Monday and Thursday effect is found, Friday effect is found significant in 29 and Wednesday effect is not found significant in the entire sample periods. The range of adjusted R 2 is found to be.1 to.62 for various calendar years. The poor values of R 2 which is representative of goodness of fit of a model given in Table 3.6, reflect that the results of this model should be read with caution. Table 3.6: Day-of-the-week effect in Indian Volatility Index Level changes a Period Obs. Monday Tuesday Wednesday Thursday Friday R 2 Adj. R 2 All 829.43* -.167 -.93 -.22** -.93.24.2 (3.217) (-1.472) (-.88) (-2.95) (-.8) Mar. 29 23.93** -.83 -.267 -.513** -.56**.81.62 (2.562) (-.313) (-.999) (-2.74) (-1.955) 21 252.248*** -.4.113 -.398* -.46.31.16 (1.637) (-.243) (.643) (-2.488) (-.22) 211 247.252 -.82 -.296.187.159.17.1 (.947) (-.347) (-1.583) (1.29).723 Jun-12 127.372 -.737*.188 -.1 -.7.14.74 (1.366) (-3.289) (1.98) (-.384) (-.342) Note: a In this table the parameter estimated are presented for the regression of daily volatility index changes on day-of-the-week dummy variables (equation 2). t-statistics are provided in the parentheses and are based on Newey-West(1987) method for correction of heteroscedasticity and autocorrelation. The levels of significance are represented with asterisks (* = 1%, ** = 5% and *** = 1%). Association between Volatility Index (VIX) and S&P CNX Nifty in India 76

3.2.7 Inter-temporal return-volatility relation: Fleming et al. (1995) were the first to measure the kinked yet linear inter-temporal risk-return relation for changes in CBOE implied volatility index and stock market returns. This methodology has been adopted by various academicians (Frijns et al., 21; Sarwar, 21) to examine the volatility-return relationship in various countries. A similar methodology has been adopted here to analyze the relationship between the innovations in implied volatility index of India and S&P CNX Nifty returns. The following conventional multivariate regression model proposed by Fleming et al. (1995) in which changes in volatility index is regressed on two lag, two lead, and the contemporaneous stock market returns, as well as the absolute value (magnitude) of the contemporaneous return, is used for the Indian markets:....(3) This model focuses on inter-temporal relationship between stock market returns and changes in implied volatility. The regression model is estimated across different subperiods for daily and weekly data through various calendar years, from March, 29 to June, 212. The weekly observations are calculated from the daily data on the basis of average of closing values of trading days of each week. For estimated model, a significant negative (positive), i= -2 2, coefficient indicates that increases (decreases) in expected volatility at time t are accompanied by stock market declines (advances) at time t+i. If coefficient is positive (negative) and significant, it indicates that stock markets are independent of their direction and are associated with increase (decreases) in expected volatility. Further, summation of and measures the asymmetry in the relationship of changes in expected volatility and stock market return. As CBOE VIX is regarded as an investor s fear gauge, it is expected that India VIX can also show similar characteristics. The theories of CAPM models contributed by Merton (1973), Sharpe (1964) and Linter (1965) suggest that with rises in expected volatility, stock prices fall - a prediction consistent with the negative values of. Black (1976) and Christie (1982) suggest that drop in stock prices increase the leverage which is associated with increase in the implied volatility. Banerjee et al. (27) reported an inverse relationship between return and implied volatility index. Association between Volatility Index (VIX) and S&P CNX Nifty in India 77

The estimated results for equation (3) using daily data are reported in Table 3.7. The t-statistic provided for regression is based on the Newey & West (1987) method for correction of heteroscedasticity and autocorrelation in the residuals. The results show that for the entire sample period there is a significant negative relationship between market returns and changes in IVIX (the coefficient of is significant at 1%). The coefficient is consistent with the cross-correlation results reported in Table 3.4, as a negative correlation is found between and ΔIVIX. The coefficient of contemporaneous returns is negative and significant at 1% level for the sub-periods 21, 211 and 212 and range between.622 to.857 except for the year 29, in which the coefficient is negative and significant at 1% level ( =.272). The coefficient at lag one for the entire sample is positive and significant but is smaller in magnitude as compared to that of contemporaneous coefficient. Thus, the contemporaneous negative relationship between changes in the IVIX and market returns dominates the relationship of expected volatility with past and future stock market returns. This pattern is consistent for all the sub-periods also, as the lag one coefficient is significant in the year 211 and 212, and lag two is significant only for the year 211. Finally, the value of coefficient is.277 and is significant at 5% level. Apparently, the changes in the expected stock market volatility are positively affected by the size of a stock market. The estimates of in Table 3.7 indicate a significant asymmetric contemporaneous volatility-return relation. If the stock market generates positive returns, the coefficient impacting the change in volatility is, or -.273. An increase in stock market returns is associated with a decrease in expected volatility. On the other hand, if market generates negative returns, the coefficient is, or -.827. This implies that a decrease in stock market returns is expected to bring an increase in future volatility. The difference in the magnitude of coefficients ( is more than three times the size of ), indicates an asymmetry in volatility-return relation. The asymmetry in risk-return relation implies that negative stock market moves generate changes in volatility index that are much larger in magnitude than those generated by the positive stock market moves. This asymmetric relationship also has been observed by Whaley (2), Giot (25) and Carr & Wu (26) for American markets. Association between Volatility Index (VIX) and S&P CNX Nifty in India 78

Table 3.7: Intertemporal relationship between daily Indian volatility index level changes and S&P CNX Nifty index returns a Parameters Period Obs. Adjusted R 2 DW Mar,29-Jun,212 829 -.283**.16.193* -.55* -.17 -.35.277**.298.293 1.967 (-2.484) (.28) (5.49) (-7.271) (-.513) (-1.21) (2.213) 29 199 -.33*** -.3.264* -.272*** -.7.13.171).16.134 1.995 (-1.638) (-.239) (5.359) (-1.854) (-.132) (.217) (.9) 21 -.233*.94.68 -.85* -.34 -.91.321*.498.486 2.15 (-2.833) (1.419) (1.85) (-12.) (-.586) (-1.489) (3.282) 211 242 -.23***.15***.18** -.857*.5 -.41.156.531.519 2.156 (-1.829) (1.876) (2.6) (-12.235) (.77) (-.831) (1.394) 212 123 -.179.43.249** -.622*.43 -.41.173.431.42 1.868 (-1.571) (.594) (2.712) (-7.938) (.575) (-.544) (1.557) Note: a This table shows the parameter estimates for the regression of daily volatility index changes on two lags, two lead, and the contemporaneous S&P CNX Nifty index returns, as well as magnitude (absolute value) of contemporaneous market returns (equation 3). t-statistics are provided in the parentheses and are based on Newey-West(1987) method for correction of heteroscedasticity and autocorrelation. *, **, *** presents 1%, 5% and 1% levels of significance respectively. Table 3.8: Intertemporal relationship between weekly Indian volatility index level changes and S&P CNX Nifty index returns a Parameters Period Obs. Adjusted R 2 DW Mar,29-Jun,212 169 -.399***.115***.46 -.41* -.7.11.211.25.176 2.23 (-1.79) (1.868) (.378) (-3.696) (-1.12) (.134) (1.534) 29 39.39.172*** -.134.34 -.52.153 -.27.116 -.49 1.52 (.461) (1.826) (-.71) (.152) (-.462) (1.56) (-.911) 21 48.8 -.15.28 -.818*.133 -.151.76.436.354 1.572 (.198) (-.733) (1.614) (-4.264) (1.61) (-1.216) (.247) 211 47 -.616 -.38.267** -.664* -.64 -.11.245.4995.426 2.466 (-1.29) (-.419) (2.53) (-4.236) (-.618) (-.996 ) (.938) 212 21.575.319.61 -.549*.151.77 -.288.49.173 2.85 (.616) (1.523) (.287) (-2.78) (.811) (.47) (-.631) Note : a This table shows the parameter estimates for the regression of weekly volatility index changes on two lags, two lead, and the contemporaneous S&P CNX Nifty index returns, as well as magnitude (absolute value) of contemporaneous market returns (eq 3).The weekly observations are average of closing values of trading days of each week. t-statistics are provided in the parentheses and are based on Newey-West (1987) method for correction of heteroscedasticity and autocorrelation. *, **, *** presents 1%, 5% and 1% levels of significance respectively. Association between Volatility Index (VIX) and S&P CNX Nifty in India 79

The Table 3.8 reports the results of regression for the weekly IVIX changes and S&P CNX Nifty returns. The results for the weekly data show that there is negative relation between changes in implied volatility index and market returns. These results are in line with the findings of analysis done using daily data. The value of contemporaneous coefficient is found to be =.41, which reveals a strong volatility-return relationship. The coefficient of contemporaneous index return is found to be negative and significant in all sub-periods expect for 29 and the value of R 2 ranges from 4% to 49% approximately for the years 21, 211 and 212. The value of coefficient is.211 (t-statistic = 1.534) indicating a positive relationship between the size of weekly stock market moves and changes in expected market volatility. The and, show an asymmetric relation for the weekly data also, where = -.19 and = -.612. Overall, the lagged coefficients in eq (3) are insignificant or marginally significant, the leverage effect explanation does not hold true for the daily and weekly data in the Indian markets. 3.2.8 Multiple regression model for return-volatility relationship: The asymmetry in return-volatility relationship for financial market can be studied using the two partition asymmetric equation with returns being separated into upside and downside partitions (Low, 24). This regression equation is adopted for Indian stock market to investigate the relationship between changes in risk perception and stock index returns. The relationship between risk and return is estimated using the various forms embodied in the functional equation 4 to equation 9. The following linear OLS regression is specified:...(4) where, represents percentage change in IVIX and represents S&P CNX Nifty returns. For accommodating the asymmetry in Indian markets, the two partition asymmetric equation with positive and negative returns is used. The multiple regression equation is estimated: (5) The above regression equation is equivalent to two linear regression equations (upside and downside partition) of the following form:.(6).(7) where, and are %ΔIVIX and reduced by removing the days when < and where, and are %ΔIVIX and reduced by removing the days when. Association between Volatility Index (VIX) and S&P CNX Nifty in India 8

Table 3.9: Results of regressions of percentage changes in daily Indian volatility index on contemporaneous S&P CNX Nifty returns Regression Obs. Constant R t ( R 2 Adjusted R 2 DW A 829.199-1.879*.259.258 1.988 (1.328) (-5.176) B 432-1.819* (-3.726) C 397.148 (.268) D 432-1.86* (-3.239) E 397-1.218** (-2.52) -.575 (-1.595) -1.489* -3.72-2.589* (-4.34) -5.418* (-4.712).24.22 1.878.182.18 1.743 Association between Volatility Index (VIX) and S&P CNX Nifty in India 81.12* (4.325) -.853** (-2.36).6.56 1.844.232.228 1.946 Note: This table shows the parameter estimates for regressing of daily percentage changes in the implied volatility index on contemporaneous returns. the regression equations are A: ; B: ; C: ; D: ;and E:. and are %ΔIVIX and reduced by removing the days when < and and are %ΔIVIX and reduced by removing the days when. t-statistics are provided in the parentheses and are based on Newey-West (1987) method for correction of heteroscedasticity and autocorrelation. The levels of significance are represented with asterisks (* = 1%, ** = 5% and *** = 1%). Table 3.1: Results of regression of percentage changes in weekly Indian volatility index on contemporaneous S&P CNX Nifty returns Regression Obs. Constant R t ( R 2 Adjusted R 2 DW A 173.491-1.311*.165.161 2.38 (.767) (-3.861) B 99-3.281* -.78.1 -.1 1.879 (-3.754) (-.25) C 74.319 (.196) -2.236** (-2.84).158.146 2.451 D 99-3.192* (-2.719) E 74 -.62 (-.3) -.149 (-.186) -3.439 (-1.169).6 (.123) -.23 (-.322).1 -.2 1.873.162.139 2.62 Note: This table shows the parameter estimates for regressing of weekly percentage changes in the implied volatility index on contemporaneous returns. The regression equations are A: ; B: ; C: ; D: ;and E:. and are %ΔIVIX and reduced by removing the days when < and and are %ΔIVIX and reduced by removing the days when. t-statistics are provided in the parentheses and are based on Newey-West (1987) method for correction of heteroscedasticity and autocorrelation. The levels of significance are represented with asterisks (* =1%,**=5%, and *** = 1%)

A quadratic term is added to the upside and downside return partition to estimate nonlinear return-volatility relation. The non-linear regression equation estimated is: (8).(9) The results obtained for daily and weekly data by estimating equations 4, 6, 7, 8 and 9 are reported in Table 3.9 and Table 3.1 respectively. Table 3.9 reports the results obtained by regressing percentage changes in daily IVIX on contemporaneous S&P CNX Nifty 5 returns. The slope in the univariate linear regression for daily and weekly data is negative and significant at 1% level (Row 4 in table 3.9 and 3.1). This implies that when index returns are negative, IVIX goes up and when it is positive, IVIX goes down. The results from the two partition asymmetric regression are reported in Tables 3.9 and 3.1. The R 2 in the downside return partition (Row 7 in Tables 3.9 and 3.1) is higher than the R 2 reported for the upside returns partition (Row 6 in Tables 3.9 and 3.1). The difference in the slopes (β + - β - = 2.14 and β + - β - = 2.158) is statistically significant at 1% level and 5% level for the daily and weekly data respectively. The same has been examined using the incremental regression equation (Eq. 1 for daily data and 11 for weekly data): where, is a dummy variable which is equal to 1 when > otherwise. The t- statistic reported in the parenthesis are obtained using the Newey and West (1987) method. This asymmetric relationship for Indian markets is consistent with the notion of loss aversion phenomenon proposed in Kahneman & Tversky s (1979) Prospect Theory. Here, value function mentioned in the Prospect theory, is replaced by a metric called implied volatility index, that is often considered as the investor s fear gauge or a sentiment index. This concept had been also validated for American markets volatility index (Low, 24). Association between Volatility Index (VIX) and S&P CNX Nifty in India 82

3.2.8.1 Reclined S-curve: Tables 3.9 and 3.1 also report the results of the non-linear return-volatility relation estimate of daily and weekly data. A quadratic term is added to each of downside and upside return partition equations. The results of quadratic equations for daily data are reported in Table 3.9. The results show that the downside partition has convex profile ( >, < and significant) and the upside partition has a concave profile ( <, < and significant). The results shown in Table 3.1 presents a convex profile for the downside partition and concave profile for the upside partition but the coefficients are found to be insignificant and the value of R 2 is very less. This non-linear relation is best described by Low as a downward-sloping reclined S-curve. Similar results are found in the context of the Indian markets but the degree of association is less when compared to the American markets. In case of Indian markets, the quadratic fit for the downside return partition equation has a lower value (R 2 =.22) in comparison with American markets (R 2 =.77). The convexity in the downside return partition indicates accelerating increases in the IVIX; and concavity in the upside returns partition indicates accelerating decreases in the IVIX. 3.2.9 QRM for return-volatility relation: To assess the short-term asymmetric return-volatility relationship at different levels of changes in implied volatility distribution, a quantile-regression model is applied. This model is a generalization of the multiple regression model (MRM) given by Low (24), Goit (25), Hibbert et al. (28) and Badshah (21). In this study, the standard MRM is extended by modelling the asymmetric return-volatility relation using the QRM to analyse the impact of negative and positive returns across the various quantiles of implied volatility changes. The major drawback of MRM specification is that the effect of negative and positive returns is static across various levels of IVIX changes. So the MRM model would miss important information across quantiles of IV changes that could otherwise be captured using the QRM. Further, the descriptive statistics show that stock returns and changes in IVIX have fat tails and the series are not symmetric. Thus, the QRM can be used for modelling conditional quantiles in IVIX as a function of independent variables. QRM method effectively estimates the rate of change in all parts of distribution of a response variable. This framework allows for the heteroscedasticity in the error terms in order to obtain different coefficients at Association between Volatility Index (VIX) and S&P CNX Nifty in India 83

different quantiles. The QRM equation is estimated using the quantile-regression method proposed by Kronear and Bassett (1978). For analysis, the following regression framework is formulated in which is daily percentage change in volatility index, IVIX and is the daily percentage continuously compounded returns of S&P CNX Nifty index: { { Thus, the standard MRM for asymmetric relation has the following form: where, is the intercept term, is the coefficient for lagged implied volatility index IVIX, L=1-3, and are the coefficients for positive and negative stock market returns, and L=-3 for both type of returns. is the error term which is assumed to be independent and identically distributed (iid) with mean zero. The following q th QRM specification which is generalization of MRM specification (12) for measuring the asymmetric relation is estimated: where, is the intercept term, is the coefficient for lagged implied volatility index IVIX, L=1-3. and are the coefficients for positive and negative stock market returns and L=-3 for both type of returns. is the error term which is assumed to be independent and derived from the error distribution Φ q ( with q th quantile equal to zero. The prime feature of QRM is that the coefficients capture the effect of independent variable on dependent variable across each of the q th quantile within the range q ϵ (, 1). The results of MRM and QRM for asymmetric return-volatility relation between IVIX changes and S&P CNX Nifty returns are presented in Table 3.11. The estimated model includes 11 co-variates and an intercept term. The following inferences can be drawn from the estimated values: Association between Volatility Index (VIX) and S&P CNX Nifty in India 84

The results show that the sign of slope coefficients are significantly negative in either direction around the centre of the distribution (q=.5). This implies that there is an inverse relation between returns and changes in implied volatility index. The results are in concurrence with other studies investigating return-volatility relation, using traditional multiple regression models (Fleming et al., 1995 and Whaley, 28) or quantile regression models (Kumar, 212). The estimated coefficients of and in Table 3.11 present the contemporaneous return-volatility relation. Table 3.11 also presents their comparison with the coefficients of lagged covariates i.e. lagged positive returns, negative returns and VIX changes. The results show that the contemporaneous returns coefficients are significant at 1% level across all quantiles except q=.5 for market declines and q=.7,,.95 in upward direction. It is also observed that the coefficients of lagged covariates are mostly insignificant or contribute marginally to changes in implied volatility index. The above results supports that the behavioural explanations, such as representativeness and affect heuristic dominates the return-volatility relation. This confirms the hypothesis II that the contemporaneous returns are important determinants of changes in IVIX. However, the fundamental theories of leverage hypothesis and volatility feedback hypothesis fail to explain the short-term returnvolatility relationship. These findings are consistent with the results of Hibbert et al. (28) and Badshah (212). Further, this relationship is not statistically significant at the tails and the independence is more pronounced at right tail rather than left tail. The absolute difference in the estimated coefficient of covariates and in Table 3.11 depicts that there is evidence of asymmetry in all quantile estimates including the OLS estimate. The absolute value of is higher than the absolute values of. The Wald test (Table 3.12) is applied to determine the statistical difference between the coefficients and in equation (13). The null hypothesis (contemporaneous positive and negative returns coefficients are equal) is rejected at each quantile except at extreme quantiles (q=.5,.25). This shows that asymmetric relationship is observed at q=.3,.95. In these Association between Volatility Index (VIX) and S&P CNX Nifty in India 85

Table 3.11: Quantile regression results of changes in Indian volatility index and S&P CNX Nifty returns Q Intercept Pseudo R 2 Adjusted R 2.5-4.616* -2.156-2.21*.551 1.92 -.244 -.128 -.416 -.73 -.15** -.1 -.147**.166.155 (-5.459) (-1.442) (-2.872) (-.773) (-1.431) (-.462) (-.274) (-.663) (-1.228) (-1.775) (-.13) (-1.94).1-3.584* -2.37* -2.476*.719**.19 -.28 -.127.64 -.727 -.35.2 -.12.192.181 (-5.92) (-3.32) (-4.998) (-2.111) (.26) (-.79) (-.333) -.127 (-1.41) (-.633) -.37 (-1.581).15-3.114* -2.362* -2.128*.625.214 -.96.214 -.62 -.184 -.28 -.34 -.56.211.2 (-5.843) (-6.716) (-5.263) (-2.153) (-.585) (-.344) (-.843) (-.137) (-.356) (-.59) (-.599) (-1.89).2-2.634* -2.431* -1.97*.748*.324 -.123.244 -.11 -.18 -.32 -.17 -.5.225.215 (-5.85) (-6.886) (-6.892) (-2.822) (.988) (-.553) (-1.72) (-.24) (-.24) (-.835) (-.386) (-1.216).25-2.358* -2.694* -1.93*.695**.476 -.53.264 -.2 -.17 -.2 -.3 -.49.231.221 (-6.487) (-8.) -9.178 (-2.68) (1.414) (-.237) (-1.27) (-.4) (-.43) (-.518) (-.69) (-1.358).3-2.23* -2.91* -1.877*.431.413.85.353.157.17 -.21 -.5 -.31.228.218 (-5.756) (-8.751-7.361 (-1.1) (1.117) (.374) (1.486) (-.389) -.49) (-.457) (-.14) (-.852).35-1.783* -3.88* -1.819*.266.683**.97.552* -.9.32 -.26.14 -.25.227.216 (-4.183) (-8.238) (-6.19) (.687) (2.86) (.388) (2.65) (-.24) (-.95) (-.465) (-.379) (-.642).4-1.24* -3.193* -1.773*.282.535***.238.531**.132 -.125 -.28.8 -.22.228.218 (-2.98) (-8.46) (-5.538) (.783) (1.669) (1.69) (2.575) (-.413) (-.382) (-.49) (-.229) (-.556).45-1.236* -3.584* -1.484*.49.411.245.59**.274 -.23 -.12.16 -.29.232.222 (-3.157) (-9.499) (-4.361) (1.422) (1.361) (1.255) (2.473) (-.881) (-.77) (-.222) (-.42) (-.87).5 -.882** -3.733* -1.395*.255.521***.331***.491**.287 -.78 -.3.14 -.26.236.226 (-2.218) (-1.397) (-4.22) (.949) (1.818) (1.863) (2.352) (-.981) (-.263) (-.64) (-.352) (-.781).55 -.757* -3.961* -1.365*.264.46.387**.466*.448 -.2.3.32 -.21.239.229 (-1.922) (-11.549) (-4.312) (1.3) (1.236) (2.131) (2.5) (-1.568) (-.75) (-.56) (-.716) (-.586).6 -.355-4.61* -1.21*.36.54***.315.626**.371.3 -.22.63 -.11.24.229 (-.91) (-11.92 (-3.432) (1.217) (1.692) (1.386) (-2.531) (-1.285) -.112 (-.418) (-1.352) (-.284).65 -.37-4.21* -.995*.286.462.348.781*.529**.29 -.1.69.3.241.231 (-.835) (-12.192 (-2.591) (.98) (1.476) (1.4) (-2.619) (-2.31) (-.11) (-.21) (-1.371) (.7).7 -.34-4.455* -.648.27.696**.382.933*.61** -.5 -.23.19**.2.245.235 (-.1) (-13.175) (-1.544) (.676) (2.23) (1.464) (-3.18) (-2.384) (-.165) (-.495) (-2.162) (.51).75.398-4.63* -.38.369.827**.417 1.3*.65** -.132 -.7.139** -.26.252.242 (-.993) (-12.984) (-.893) (1.341) (2.29) (1.452) (-3.25) (-2.372) (-.41) (-.141) (-2.27) (-.586).8.84*** -4.598* -.99.456***.359.499.947*.688** -.322.11.132** -.37.261.251 (-1.664) (-11.625) (-.231) (-1.714) (.842) (1.359) (-2.819) (-2.5) (-.863) -.232 (-1.887) (-.74).85 1.19** -5.1* -.161.665***.329 -.18.893**.64.39 -.32.14 -.16.278.268 (-2.4) (-1.348) (-.486) (-1.87) (-.64) (-.46) (-2.22) (-1.584) (-.834) (-.525) (-1.38) (-.256).9 1.975* -5.312* -.225.669***.252.117 1.4**.722***.52 -.18.116.68.298.289 Association between Volatility Index (VIX) and S&P CNX Nifty in India 86

(-3.226) (-9.23) (-.621) (-1.637) (-.381) (-.279) (-2.283) (-1.727) (-1.175) (-.251) (-1.398) (-.998).95 3.315* -6.88*.351 1.459* -.23 -.159 1.297*.657.23.127.214**.9.333.324 (-4.531) (-9.663) (-.724) (-2.851) (-.32) (-.298) (-3.225) (-1.456) (-.438) (-1.42) (-2.194) (-1.132) OLS -.763*** (-1.826) -3.431* (-5.995) -.844** (-2.126).573* (2.828).425 1.475.6.26.521* (3.112).21 (.6) -.299 (-.981) -.12 (-.34).32.832 -.6*** (-1.64).339.33 Panel B: Quantile slope equality test results: Only significant results of asymmetry are reported.2-.3**.2-.4**.2-.4**.25-.5*.25-.5*.25-.5*.3-.5*.3-.5**.4-.5**.4-.5**.4-.6*.4-.6**.5-.75*.5-.75*.5-.75*.5-.75*.6-.7**.6-.8**.75-.95**.75-.95*.75-.95**.75-.95**.75-.95***.8-.95**.8-.95**.8-.95**.8-.95**.8-.95**.9-.95***.9-.95***.9-.95** Note: the table shows the parameters estimated for the MRM and QRM specification 12 and 13 respectively, for the asymmetric return-volatility relation between changes in the IVIX and S&P CNX Nifty returns. In QRM specification 13, the standard errors are obtained using the bootstrap method; therefore robust t-statistics (in parenthesis) are computed for each of the quantile estimates. The MRM specification 12 is computed with Newey and West, 1987 correction for heteroscedasticity and autocorrelation. In last panel the results of quantile slope equality test are reported which rejects the null hypothesis of the equality of coefficients across quantiles. *,** and *** denotes the rejection of null hypothesis at 1%, 5% and 1% level of significance. Table 3.12: Wald tests for the equality of coefficients Quantiles.5.1.15.2.25.3.35.4.45.5.55.6.65.7.75.8.85.9.95 F-statistic..1.14.91 1.67 3.33 5.39 5.96 9.85 15.49 23.48 26.85 28.75 38.14 37.38 19.25 139.55 75.55 45.26 p-value.96.92.71.34.19.7.2.1........... Note: this table shows the parameters estimated for the Wald test. The null hypothesis, H o = The coefficients for the contemporaneous positive and negative returns are equal i.e. = Association between Volatility Index (VIX) and S&P CNX Nifty in India 87

quantiles the representative heuristic which is reinforced by affect heuristic, gets dominates because the investor associates higher levels of volatility with higher risk and negative returns with least benefits. This confirms the Hypothesis IV that the asymmetry exists for return-volatility relation. More specifically, looking at each row of Table 3.11 (i.e. each quantile of estimates), the impacts of positive and negative returns on IVIX distribution is changing and highly asymmetric. The dynamic nature quantile estimates provide an interesting picture of how the changes in the IVIX distribution depends on the contemporaneous and lagged covariates. The absolute value of is increasing monotonically when moving from a median quantile towards an upper quantile; that is the marginal effect of negative returns is much larger in upper quantiles (i. e. q=.95) and vice-versa for positive returns. The equality of slopes across quantiles is also tested using Wald Test. To examine this equality, quantile slope equality test is used in which coefficients of each variable across quantiles are compared; the null hypothesis that the coefficients of a particular covariate across quantile are same is tested. The test results are reported in Panel B of Table 3.11. Thus, these asymmetric responses across the quantiles of changes in IVIX confirm the Hypothesis V: that the contemporaneous negative returns are the major determinants of IV changes in the context of short-term return-volatility relationship. From this it can inferred that in short-term and during the period of extremely volatile market conditions, the affect heuristic and time pressure dominates the investor s judgement, and this is found to be consistent with the behavioural explanations of Finucane et al. (2). The coefficients of contemporaneous positive and negative returns, lagged returns and lagged implied volatility index covariates, with their 19 quantile-regression estimates, are plotted in Figure 3.8. Each plot on the x-axis represents the quantile (or q) scale, and y-axis indicates the percentage-point change of the covariate on volatility changes, holding other covariates constant. Association between Volatility Index (VIX) and S&P CNX Nifty in India 88

Figure 3.8: Graphical representation of the coefficients estimated for contemporaneous and lagged covariates across various quantiles for the response variable IVIX Quantile Process Estimates (95% CI) C PIVIX(-1) PIVIX(-2) PIVIX(-3) 6.3.4.3 4.2.3.2 2-2 -4.1. -.1.2.1..1. -.1-6 -.2 -.1 -.2-8..2.4.6.8 1. -.3..2.4.6.8 1. -.2..2.4.6.8 1. -.3..2.4.6.8 1. Quantile Quantile Quantile Quantile RNSEN RNSEN(-1) RNSEN(-2) RNSEN(-3) 2.5 3 1. -2-4 -6 2. 1.5 1..5. 2 1-1.5. -.5-8..2.4.6.8 1. -.5..2.4.6.8 1. -2..2.4.6.8 1. -1...2.4.6.8 1. Quantile Quantile Quantile Quantile RNSEP RNSEP(-1) RNSEP(-2) RNSEP(-3) 2 3 2 2. 1-1 -2-3 2 1-1 1-1 1.5 1..5. -.5-4..2.4.6.8 1. -2..2.4.6.8 1. -2..2.4.6.8 1. -1...2.4.6.8 1. Quantile Quantile Quantile Quantile Note: In graph the coefficients are represented as: c= intercept term; PIVIX(-1) = ; PIVIX(-2) = ; PIVIX(-3) = ; RNSEN = ; RNSEN(-1) = ; RNSEN(-2) = ; RNSEN(-3) = ; RNSEP = ; RNSEP(-1) = ; RNSEP(-2) = ; and RNSEP(-3) = Association between Volatility Index (VIX) and S&P CNX Nifty in India 89

3.3 Summary The dissemination of implied volatility index by NSE has provided the academic researchers a new measure for exploring volatility. This chapter describes the information content of implied volatility index of India over a duration of nearly three years. This chapter intends to investigate the univariate time-series properties, and seasonal patterns exhibited by IVIX. The risk-return relationship is studied using three methodologies which include regression of changes in implied volatility index on: lead-lag, contemporaneous and contemporaneous absolute stock market returns; on positive and negative returns; and finally, the short-term return-volatility relationship is determined using the quantile regression method. The time series properties of daily and weekly IVIX changes are found stationary. The autocorrelation function for the daily changes in IVIX is significantly negative and small, which reflects that it follows a mean reverting process. A significant Monday effect is observed in the IVIX series. The implied volatility exhibits a negative and asymmetric temporal relation with stock market returns. This absolute value of coefficient of non-linear regression shows that there exists an asymmetric and non-linear relationship, best described as downward sloping reclined S-curve by Low (24) but the degree of association is less for the Indian markets as compared to the American markets. On an average, a short-term negative asymmetric return-volatility relation is also found which is shown by quantile regression method. The behavioural explanation holds true while explaining the return-volatility relation. However, when sharp upside moves are observed in the market, changes in IVIX and Nifty returns move independent of each other. On the other hand, when the market moves down, the relationship becomes insignificant only for the steepest or the worst declines. Finally, it can be summarized that there is an asymmetric relationship between changes in implied volatility and index returns. The contemporaneous returns are found to be significantly negative and are the most important determinants of changes in current implied volatility. These results support the representativeness and affect heuristic of behavioural theories and reject the fundamental theories of leverage and volatility feedback. The main conclusion drawn from the above discussion is that the IVIX can act as a sentiment index or investor fear gauge for the Indian capital markets. It can therefore be used by practitioners both as a market timing tool and in portfolio management. Association between Volatility Index (VIX) and S&P CNX Nifty in India 9