As Christine Lagarde perfectly coined it, “Markets love volatility.” Until automation is perfected and capable of making sound investment decisions, the free market is dominated by humans and their intuition for a financial advantage. Emotions and common sentiments across a population play a critical role in the market movement. Analyzing the common thinking or sentiment of a population towards a certain trend or idea would be the logical concept to look out for in a market where buying and selling, determines the outcome. In this paper, we will be largely focusing on the role of human emotions namely fear, which is the primary unit for emotion in the market. A lack of fear indicates a strong confidence in a position, on the contrary, an abundance of fear results in instability in a position. We can use levels of fear to gauge how investors think, make decisions, and react to events in the economy. In our study, we will be using the Chicago Board Options Exchange Volatility Index (VIX) which is termed the “investor fear gauge,” to determine and gauge future market, sector, stock, and equity performance. And how these common practices can be applied to predict trends, automate trends, and hopefully educate the public on the use of volatility as a trading strategy. In the next couple of pages, we will be introducing financial and business terms which are necessary to understand further technical details and strategies.
This study compares parametric and non-parametric techniques in terms of their forecasting power on impliedvolatility indices. We extend our comparisons using combined and model-averaging models. The forecasting models are applied on eight impliedvolatility indices of the most important stockmarket indices. We provide evidence that the non-parametric models of Singular Spectrum Analysis combined with Holt-Winters (SSA-HW) exhibit statistically superior predictive ability for the one and ten trading days ahead forecasting horizon. By contrast, the model-averaged forecasts based on both parametric (Autoregressive Integrated model) and non-parametric models (SSA-HW) are able to provide improved forecasts, particularly for the ten trading days ahead forecasting horizon. For robustness purposes, we build two trading strategies based on the aforementioned forecasts, which further confirm that the SSA-HW and the ARI-SSA-HW are able to generate significantly higher net daily returns in the out-of-sample period.
This paper describes the impliedvolatility function computed from options on the Italian stockmarket index between 1995 and 1998 and tries to find out potential explanatory variables. We find that the typical smirk observed for S&P500 stock index characterizes also Mib30 stock index. When potential determinants are investigated by a linear Granger Causality test, the important role played by option’s time to expiration, transacted volumes and historical volatility is detected. A possible proxy of portfolio insurance activity does poorly in explaining the observed pattern. Further analysis shows that the dynamic interrelation between the impliedvolatility function and some determinants could be, to a certain extent, non-linear.
Other researchers to have studied S&P 500 options include Ederington and Guan (2002a), who examined how well the impliedvolatility forecasted future stockmarketvolatility in an activemarket that was a subject to less measurement error. It is a result where the impliedvolatility deviate from the true marketvolatility due to the bid-ask spread, non-synchronous price and minimum price increment. The S&P 500 options market trades side-by-side with its underlying future, hence minimise the condition that can give rise to the measurement error.They found that the impliedvolatility had strong predictive power and subsumed the information in the historical volatility. Furthermore, the forecasting results were quite sensitive to the forecasting horizon. In a different paper, Ederington and Guan (2002b) compared the averages of implied volatilities used in Latane and Rendleman (1976), Beckers (1981), and Whaley (1982) papers, with the one used by the commercial vendors such as Bloomberg in the S&P 500 option market. The found that most of the impliedvolatility averages provided better forecasts than the time-series and naïve models, but the differences between the averages were small. The study also indicated that the impliedvolatility was upward biased in its measurement of expected volatility; however, the bias became stable over time. Furthermore, Ederington and Guan (2002c) examined the volatility smile caused by using the wrong pricing model to calculate the impliedvolatility. All the options, however, shared similar implied volatilities. They computed the impliedvolatilityusing Black’s (1976) model and the evidence indicated that high-implied-volatility options were significantly overpriced in relation to low-implied-volatility options. This reflected the demand for out-of-the-money puts to hedge against the market that failed to push up the impliedvolatility on the low-strike options.
The early proposals which used VI as an underlying asset for tradingvolatility goes back to Gastineau (1977), Galai (1979), and Brenner and Gali (1993). Whaley (1993) showed how volatility derivative can be used by option market makers, portfolio managers and covered call writers for hedging the marketvolatility risk. Thus, in 1993, Chicago Board Options Exchange (CBOE) officially introduced its first implied VI, ticker symbol VIX, which has become the benchmark for risk measurement of the US Equity markets. Flemming et al (1995) investigated a strong contemporaneous negative correlation between the index returns and VIX changes. Whaley (2000) found that VIX as an indicator of expected future stockmarketvolatility and hence termed it as “The Investors Fear gauge”.
This paper provides a comparative evaluation of the ability of a wide range of GARCH, IV and RV models to forecast stock index return volatility for a number of US and European indices. Recent literature has shown that IV follows a predictable pattern. Therefore, this paper analyzes whether the IV forecasts are good predictors for the stockmarketvolatility. A total of ten GARCH models are considered, GARCH, GJR, EGARCH, CGARCH and ACGARCH model and the encompassing variants of these models including IV as a regressor in the variance equation. Additionally, six ARMA models have been taken into consideration for forecasting IV indices and realized volatility. The results show that both the IV and RV forecasts contain significant information regarding the future volatility. With regard to the forecasting ability of IV itself, we find that IV forecasts are statistically significant. When the IV model accounts for the contemporaneous asymmetric effect its forecast strictly outperforms the random walk. As for the GARCH models, the inclusion of IV in the GARCH variance equations improves both the in-sample and out-of-sample performance of the GARCH models with an asymmetric GARCH to perform best. Encompassing regressions indicate that a linear combination of GARCH, IV and RV improves the forecasts. Finally, with regard to VaR forecasts, the ACGARCH combined with the realized volatility when the latter is forecasted by the ARFIMAX model is preferred followed by a combination of ACGARCH with ARFIMA for both IV and RV.
Second set of regressions in both panels test weekly predictability using non-overlapping weekly observations. For the value-weighted returns in Panel A, the coe¢ cients of the volatility spread measures are still signi…cantly negative ranging from -0.0311 to -0.0826. In other words, when volatility spread measures increase by 1%, one-week ahead aggregate stock returns decrease by 3.11 to 8.26 basis points. However, two of the four volatility spread measures are not signi…cant at conventional levels. When we focus on equal-weighted market returns in Panel B, we …nd that all volatility spread measures have a signi…cantly negative relation with expected weekly market returns. The coe¢ cient estimates are between -0.0418 and -0.1156 and the corresponding t-statistics are between -1.92 and -2.64. Extending the measurement window for expected market returns to non-overlapping two weeks or one month takes away signi…cance of the slope coe¢ cients on volatility spread measures. For the value-weighted returns, at the two-week horizon, the coe¢ cient of HOVS (HVVS) has the lowest (highest) statistical signi…cance with a t-statistic of -0.09 (-1.31), whereas for the one-month horizon, the coe¢ cients of the volatility spread measures become positive but they are still insigni…cant. For the equal-weighted returns reported in Panel B, although we observe some signi…cantly negative coe¢ cients at the two-week horizon, the results are qualitatively similar to those reported for the value-weighted returns in Panel A. Collectively, these results suggest that there is an economically and statistically signi…cant relation between volatility spreads and market returns and this predictability extends to a weekly horizon. We believe that the weekly predictability that the results indicate is consistent with our information-based explanation as option and equity markets typically assimilate information quickly and it is not likely that it would take more than one week for any information revealed in the option market to be re‡ected in the stockmarket.
In the way of Whaley , the relation between the VIX and SPX is asymmetric, so the VIX is an investors’ fear gauge in a market fall rather than an investors’ excitement gauge in a market rally. As a contrarian indicator, VIX is more relevant at market bottom 5 . Ralf Becker, Adam E. Clements and Andrew McClelland  consi- dered two issues relating to the information content of the VIX 6 . Silmai  investigated the information spillov- er between VIX changes and SPX returns. N. Bada and Y. Sakurai  investigated whether macroeconomic va- riables can predict the regime switches in the VIX index 7 . Jianhua Gang and Xiang Li  used the bivariate semi-nonparametric (SNP) model by Gallant and Tauchen  to study the contemporaneous relationship be- tween the innovation of VIX and the expected SPX returns 8 . Ghulam Sarwar  have examined whether the relation between stockmarket returns and VIX has changed over time 9 . Kozyra and Lento  provided an in- sight into the relation between the VIX and technical analysis 10 .
Butterworth  studied the impact of futures trading on underlying stock index volatility in the FTSE, UK market and argued that introduction of the futures market leads to more complete market enhancing the information flow. Ryoo and Smith  argued that introduction of index futures trading have destabilized the spot market. They captured time varying nature of volatility phenomena in the data. The results implies futures trading increases the speed at which information is impounded into the spot market prices, otherwise reduces the persistence of information and increase the spot marketvolatility. The information implied from derivative prices are about the risk-neutral distribution of the underlying asset. Bhuyan and Chaudhury  asserts that apart from the derivative prices, non-price measures of activity in the derivatives market such as the open interest contain information about the future level of the underlying asset. The results suggest that the open-interest based trading strategies have the potential to generate enhanced trading returns or lower trading losses. Thereby, open interest based activetrading strategies generate better returns compared to the passive benchmarks. Kim  examined the relationship between trading activities of the Korea Stock Price Index 200 derivative contracts and their underlying stockmarketvolatility. He found positive relationship between stockmarketvolatility and derivative volume while the relationship is negative between volatility and open interests. Robbani and Bhuyan  examined the effect of introduction of future & option on the DJIA on the volatility & trading volume of its underlying stocks. The result shows that level of volatility and trading volume increased after the introduction of futures& options on the index. Sabri  examined the impact of change in trade volume on volatility of stock prices as expressed by unified Arab Monetary fund stock
In order to see whether option-impliedvolatility measures can predict stock returns after controlling for known firm-specific effects, we also include several firm-level control variables. To control for the size effect documented by Banz (1981), we use the natural logarithm of a company’s market capitalization (in thousands of USD) on the last trading day of each month. Following Fama and French (1992), we use the book-to-market ratio as another firm-level control variable. Jegadeesh and Titman (1993) document the existence of a momentum effect (i.e., past winners, on average, outperform past losers in short future periods). We use past one-month returns to capture the momentum effect. Stocktrading volumes are included as another variable (measured in hundred millions of shares traded in the previous month). The market beta reflects the historical systematic risk and is calculated by using daily returns available in the previous month using the standard CAPM
The main conclusions of this study are as follows. First, the KSM index exhibits strong volatility persistence and asymmetry. Second, the inclusion of contemporaneous trad- ing volume in the GJR-GARCH and EGARCH models results in a positive relationship between trading volume and volatility. Third, when contemporaneous and lagged trading volumes are included in the conditional variance equation, the former is positively correlated with volatil- ity but the latter is not. Thus, trading volume affects the flow of information, supporting the validity of MDH. Finally, the asymmetric effect of bad news on volatility is higher when contemporaneous trading volume is included, although market shocks, whether positive or negative, have similar effects on conditional volatility. Thus, we conclude that trading volume is a useful tool for predicting the volatility dynamics of the KSM.
Using high-frequency transaction data for the three largest European markets (France, Germany and Italy), this paper documents the existence of an asymmetric relationship between market liquidity and trading imbalances: when quoted spreads rise (fall) and liquidity falls (increases) buy (sell) orders tend to prevail. Risk-averse market-makers, with inventory- depletion risk being their main concern, tend to quote wider (narrower) spreads when they think bond appreciation is more (less) likely to occur. It is also found that the probability of being in a specific regime is related to observable bond market characteristics, stockmarketvolatility, macroeconomic releases and liquidity management operations of the monetary authorities.
Several studies on other index options have been carried out using the same sampling procedure as Christensen and Prabhala (1998). Hansen (2001) analyses the information content of options on the Danish KFX share index. This option market is very illiquid compared to the OEX options market. It is shown that when error-in- variable problem is controlled by instrumental variable techniques, call impliedvolatility still contains more information about future realized volatility than historical volatility in such an illiquid option market. More recently, Shu and Zhang (2003) examine the options on S&P 500 index, and also report that impliedvolatility outperforms the subsequently historical index return volatility. Szakmary et al (2003) examine 35 futures options markets across eight separate exchanges and find that for a large majority of the commodities studied, impliedvolatility is a better predictor of future realized volatility than historical volatility.
The efficient market hypothesis (EMH) assumes that investors are rational and value securities rationally. A rational investor would value a security by its net present value; the price of a stock in this framework is based on the discounted cash flow or the present value model. Although the EMH-based model is partially successful in computing fundamental stock prices, other anomalies such as high trading volume, high volatility, and stockmarket bubbles remain unexplained. These models assume rational investors who are utility maximizers. But some investors behave irrationally or against the predictions, and in the aggregate they become irrelevant. In this paper, we relax the assumption of investor rationality, and attempt to explain high volatility, high trading volume, and stockmarket bubbles by incorporating investor sentiment into the already existing asset pricing model.
Our sample represents the US equity option market by comprising the stock options traded at the American Stock Exchange and the Chicago Board Options Exchange (CBOE) for the period from January 2001 to De- cember 2010. The data to undertake the research was collected from different sources. 1) The daily implied vo- latility for each individual company and the option open interest were collected from Tick Data and Option Me- trics; 2) Stock returns, share prices, and the number of shares outstanding are from Tick Data and CRSP and eq- uity book value are from Tick Data and Compustat; 3) daily returns for the the Carhart (1997) momentum factor (UMD) and three Fama and French (1993) factors (MKT, SMB, HML) were collected from Kenneth French’s website.
The sample period extends from December 8, 1999 to July 28, 2002, representing a total of 138 weeks. All variables are calculated weekly. Return data are in percent. RETSPX is the weekly return to the S&P 500 index. RETNASD is the weekly return to the NASDAQ composite index. VOLS is the total trading volume of small-sized trades in billions of shares. NTS is the total number of small-sized trades. OIS is the total order imbalance of small-sized trades in billions of shares. OINUMS is the total order imbalance of small- sized trades measured by the number of trades. RETSPX is the weekly return to the S&P 500 index. RETNASD is the weekly return of the NASDAQ composite index. OL is the average daily number of unique visitors (thousands) to six leading online brokers’ websites. These six online brokers are Ameritrade, Datek, E*trade, Fidelity, Schwab, and TD Waterhouse. OL is our proxy for online trading. Transactions data are obtained from the NYSE Trade and Quote (TAQ) database. Small trades are defined as trades of 500 shares or less. Web traffic data are drawn from Media Metrix. In each regression, the first row gives the OLS coefficient estimates. The second row (in parentheses) contains the Newey-West standard errors. *, ** and *** represent statistical significance at the 10 percent, 5 percent, and 1 percent levels respectively.
The …rst decomposition allows us to analyze the e¤ect order ‡ow has on prices when, for instance, no party has a speed advantage, i.e. both parties are humans or both parties are computers, and when either the maker has a speed advantage, CH, or the taker has a speed advantage, HC. This distinction may be particularly useful when analyzing the cross-rates, where computers likely have a clear advantage over humans in detecting short-lived triangular arbitrage opportunities. This decomposition may also allow us to study whether the liquidity supplier, who is traditionally assumed to be “uninformed”, is posting quotes strategically. This situation is more likely to arise in our database, a pure limit order book market, than in a hybrid market like the NYSE, because, as Parlour and Seppi (2008) point out, the distinction between liquidity supply and liquidity demand in limit order books is blurry. 7 Still, in our exchange rate data as in other …nancial data, the net of trades signed by who the taker is (the standard de…nition of order ‡ow) is clearly highly positively correlated with exchange rate returns, so that the taker is considered to be more "informed" than the maker. Thus we also consider prominently the more traditional second decomposition, human-taker and computer-taker order ‡ow, in our analysis of whose trades impact prices more. The third decomposition, human-maker and computer-maker order ‡ow, allows us, for instance, to determine whether computers or humans are more likely to provide liquidity when it is needed the most, e.g. during periods of high exchange rate volatility. Lastly, the fourth decomposition, computer-participation and human-human order ‡ow, allows us to determine whether any type of participation by computers, passive or active, is linked to excess volatility in the market.
Having defined in Section (5.2) a consistent model with a global fit to prices under constraints, prices can now be generated such that the No-Dominance principle is preserved, and one can safely assess relative value between them. Bloch  showed that the dynamics implied by the single parametric model for the entire volatility surface in Equation (9) were those of a mixture diffusion process associated to an uncertain-volatility model. That is, similarly to the SABR models, our model already assumes some dynamics of the volatility and the underlying asset expressed by a system of stochastic differential equations. In that model, the instantaneous volatility being a deterministic function of the spot price and time, we can simulate the underlying process with a local volatility model. However, the impliedvolatility surface being neither stationary nor Markovian but stochastic, we are going to com- bine the two different approaches to model the dynamics of the IV surfaces described in Section (6.1) by providing memory to the parameters of our model. Hence, we now consider the impliedvolatility surface to be a stochastic process driving the option prices and choose to model its dynamics in its full term and strike structure. Option prices deriving their values from an underlying security, it is natural to use mathematical tools to infer their dynamics from that of the underlying stock process. One approach is to model the impliedvolatility with dynamics based on a statistical analysis of its behaviour through time. It naturally leads us to model the stock price process discreetly with Markov chains. We impose that future smile surfaces should be compatible with today’s prices of calls and puts. Formalising the Kolmogorov- Compatibility condition, we impose that the future density is actually a conditional density. Knowing that when the number of fixing dates in a model is finite there is an infinity of conditional densities, we choose to satisfy this infinity of solution by giving the forward smile a shape consistent with its historical evolution. Following
Prices of scrip’s in the stockmarket oscillate daily on the account of continuous trading (Buying and Selling). Technical analysis applied to the scrip’s to identify the current trends and risks associated with the scrip at par with the market. “Technical analyses believe that the historical performance of scripts & markets are indicators of future performance.” Mainly the movement of technical analysis examines the four dimensions, namely price, volume, time and breadth. Changes in price reflect changes in investor attitude. And price, the first dimension, indicates the attitude level of investors. It is helpful to observe price indicators such as price advances versus declines and price pattern of shares compared to the market index. Volume, the second dimension, reflects the intensity of changes in investor attitudes. The level of enthusiasm is implied by a price rise on low volumes and vice versa. Time, the third dimension, measures the length of cycles in investor psychology. Change in confidence goes through distinct cycles, some long and some short, as investors’ swing from excessive optimism towards deep pessimism.
We examine whether the dynamic relationship between lagged returns and trading activity is symmetric in the sense of being driven equally by positive and negative return shocks. The linear VAR structure we have used thus far rules out any asymmetric dynamics, and non- linear specifications have to be used to examine asymmetries. Unless a well-specified theoretical structure is developed, one has to choose among a large collection of non-linear multivariate time series (see Granger and Terasvirta (1993)). In this study we use a Threshold Vector Autoregression (TVAR) model for turnover, volatility, and returns. This model can also be seen as a piecewise linear autoregression in the threshold variable (but non-linear in time). 18 The TVAR specification allows, for instance, the dynamic behavior of turnover to be different depending on the sign and/or the magnitude of lagged returns. We choose the lagged return as the threshold variable, assume two regimes, and then estimate the system through the two-step conditional least-squares procedure suggested by Tsai (1998). As we did for the linear VAR, we then proceed to compute Generalized Impulse Response Functions (GIRF). The details for our computation of GIRFs are reported in the Appendix.