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There is empirical evidence that a firm's addition to S&P 500 results in significant abnormal returns and an increase in a stock's liquidity. The purpose of this paper is to argue that changes in the information environment after the year 2000 due to the implementation of Regulation Fair Disclosure (FD), decimalization and Sarbanes Oxley Act, should result in reduced abnormal returns in the post‐2000 period.

The authors compare the abnormal returns and liquidity changes around the announcement day of firm's addition to S&P 500 in the pre‐ and post‐2000 periods. Univariate and multivariate tests are used to control for factors that research shows affect the abnormal returns around additions to S&P 500.

It is found that the reduction in informational asymmetry in the post‐2000 period has resulted in a significant decrease in the abnormal return on the announcement day of additions to S&P 500 index and changes in the stock's liquidity in the post announcement period are now marginal.

Existing literature related to changes in the abnormal returns around additions to S&P 500 does not account for changes in the information environment in the two sub periods, pre‐ and post‐2000. The results may have implications for studies related to additions to S&P 500 where the sample period spans over the two sub periods.

The purpose of this paper is to illustrate a profitable and original index options trading strategy.

The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second criterion if the option premium is underpriced or overpriced. A buy or sell and hold strategy is finally implemented.

The paper demonstrates the valuable contribution of this option trading strategy when trading call and put index options. It especially demonstrates that the ARIMA forecasting method is a valid method for forecasting the S&P 500 Composite index and is superior to the GARCH model in the context of an application to index options trading.

The strategy was applied in the aftermath of the 2008 credit crisis over 60 months when the volatility index (VIX) was experiencing a downtrend. The strategy was successful with puts and calls traded on the USA market. The strategy may have a different outcome in a different economic and regional context.

When a stock is added into the S&P 500 Index, it in effect becomes cross-listed in the Index derivative markets. When index-based trading strategies such as index arbitrage are executed, the component stocks are directly affected by such trading. We find increased volatility of daily returns, plus increased trading volume for the underlying stocks. Utilizing a list of S&P 500 Index composition changes over the period September 1976 to December 2005, we study the market-adjusted volume turnover and return variance of the stocks added to and deleted from the Index. The results indicate that after the introduction of the S&P 500 Index futures and options contracts, stocks added to the S&P 500 experience statistically significant increase in both trading volume and return volatility. Both daily and monthly return variances increase following index inclusion. When stocks are removed from the index, though, neither volatility of returns nor trading volume experiences any significant change. So, we have new evidence showing that Index inclusion changes a firm's return volatility, and supporting the destabilization hypothesis.

Many papers have argued that there are long‐run downward‐sloping demand curves (LRDDC) for stocks. The purpose of this paper is to analyze this hypothesis using a new, unique, and ostensibly information‐free event: the re‐weighting of the Standard & Poor (S&P) 500 index from market based to free‐float based, which involves a significant shift in supply that, under the LRDDC, should result in significant and permanent price movements.

Event study methodology is used to examine abnormal returns and trading activity around the free‐float weight implementation dates for S&P 500 firms with various investable weight factors.

As a result of S&P 500 index re‐weighting, affected stocks experience statistically significant excess returns of −1.54 percent during the event week. This return is reversed during the following 30 days as trading volume returns to normal levels. These results are contrary to previous studies that analyze ostensibly informational events and/or different exchanges.

Results of this study indicate that arbitrage appears to be effective in eliminating a long‐term mispricing, which challenges the validity of the LRDDC hypothesis.

This study contributes to the body of literature on the S&P 500 index firms by providing supporting evidence for the price‐pressure hypothesis.

Sees the objective of teaching financial management to be to help managers and potential managers to make sensible investment and financing decisions. Acknowledges that financial theory teaches that investment and financing decisions should be based on cash flow and risk. Provides information on payback period; return on capital employed, earnings per share effect, working capital, profit planning, standard costing, financial statement planning and ratio analysis. Seeks to combine the practical rules of thumb of the traditionalists with the ideas of the financial theorists to form a balanced approach to practical financial management for MBA students, financial managers and undergraduates.

The pricing kernel puzzle of Jackwerth (2000) concerns the fact that the empirical pricing kernel implied in S&P 500 index options and index returns is not monotonically decreasing in wealth as standard economic theory would suggest. Thus, those options are currently priced in a way such that any risk-averse investor would increase his/her utility by trading in them. We provide a representative agent model where volatility is a function of a second momentum state variable. This model is capable of generating the empirical patterns in the pricing kernel, albeit only for parameter constellations that are not typically observed in the real world.

This paper conducts a factor analysis using the implied variances of S&P 500 index options and KOSPI 200 index options. After estimating the factors that influence variance risks, we rotate the factors to decompose them into a common factor and local factors. The results show that 10~12 percent of variance risks in both markets is explained by the common factor and 65 percent of S&P 500 implied variances and 70 percent of KOSPI 200 implied variances are explained by each local factor, which is in contrast to the results for bond markets that the most variation of interest rates could be explained by a common factor. To figure out the source of common and local factors, additionally, we adopt the regression models that incorporate the risk-neutral (RN) variance, skewness, and kurtosis as explanatory variables. Approximately, the common factor is mainly determined by the RN variance of the S&P 500 index and RN higher moments of the KOSPI 200 index. In contrast, the S&P 500 local factor is influenced by the RN variance of the S&P 500 index and the KOSPI 200 local factor is explained by the RN higher moment of the KOSPI 200 index.

With the presence of pricing errors, the authors consider statistical inference on the variance risk premium (VRP) and the associated implied variance, constructed from the option prices and the historic returns.

The authors propose a nonparametric kernel smoothing approach that removes the adverse effects of pricing errors and leads to consistent estimation for both the implied variance and the VRP. The asymptotic distributions of the proposed VRP estimator are developed under three asymptotic regimes regarding the relative sample sizes between the option data and historic return data.

This study reveals that existing methods for estimating the implied variance are adversely affected by pricing errors in the option prices, which causes the estimators for VRP statistically inconsistent. By analyzing the S&P 500 option and return data, it demonstrates that, compared with other implied variance and VRP estimators, the proposed implied variance and VRP estimators are more significant variables in explaining variations in the excess S&P 500 returns, and the proposed VRP estimates have the smallest out-of-sample forecasting root mean squared error.

This study contributes to the estimation of the implied variance and the VRP and helps in the predictions of future realized variance and equity premium.

This study is the first to propose consistent estimations for the implied variance and the VRP with the presence of option pricing errors.

The purpose of this paper is to propose a new method for estimating continuous‐time stochastic volatility (SV) models for the S&P 500 stock index process using intraday high‐frequency observations of both the S&P 500 index and the Chicago Board Options Exchange (CBOE) implied (or expected) volatility index (VIX).

A primary purpose of the paper is to provide a framework for using intraday high‐frequency data of both the indices' estimates, in particular, for improving the estimation accuracy of the leverage parameter, that is, the correlation between the two Brownian motions driving the diffusive components of the price process and its spot variance process, respectively.

Finite sample simulation results show that the proposed estimator delivers more accurate estimates of the leverage parameter than do existing methods.

The focus of the paper is on the Heston and non‐Heston leverage parameters.

Finite sample simulation results show that the proposed estimator delivers more accurate estimates of the leverage parameter than do existing methods.

The research findings are important for the analysis of ultra high‐frequency financial data.

The paper provides a framework for using intraday high‐frequency data of both indices' estimates, in particular, for improving the estimation accuracy of the leverage parameter, that is, the correlation between the two Brownian motions driving the diffusive components of the price process and its spot variance process, respectively.

The purpose of this paper is to study the variance risk premium in corn and soybean markets, where the variance risk premium is defined as the difference between the historical realized variance and the corresponding risk-neutral expected variance.

The authors compute variance risk premiums using historical derivatives data. The authors use regression analysis and time series econometrics methods, including EGARCH and the Kalman filter, to analyze variance risk premiums.

There are moderate commonalities in variance within the agricultural sector, but fairly weak commonalities between the agricultural and the equity sectors. Corn and soybean variance risk premia in dollar terms are time-varying and correlated with the risk-neutral expected variance. In contrast, agricultural commodity variance risk premia in log return terms are more likely to be constant and less correlated with the log risk-neutral expected variance. Variance and price (return) risk premia in agricultural markets are weakly correlated, and the correlation depends on the sign of the returns in the underlying commodity.

Commodity variance (i.e. volatility) risk cannot be hedged using futures markets. The results have practical implications for US crop insurance programs because the implied volatilities from the relevant options markets are used to estimate the price volatility factors used to generate premia for revenue insurance products such as “Revenue Protection” and “Revenue Protection with Harvest Price Exclusion.” The variance risk premia found implies that revenue insurance premia are overpriced.

The empirical results suggest that the implied volatilities in corn and soybean futures market overestimate true expected volatility by approximately 15 percent. This has implications for derivative products, such as revenue insurance, that use these implied volatilities to calculate fair premia.