The impact of individual and institutional investor sentiment on the market price of risk

We examine the effect of individual and institutional investor sentiment on the market price of risk derived from DJIA and S&P500 index returns. Consistent with behavioral asset pricing models, we find significant positive response of rational sentiment suggesting greater incentive for rational investors to engage in arbitrage when the compensation for taking risk is greater. Further, an increase in irrational optimism leads to a significant downward movement, but an increase in rational sentiment does not lead to a significant change market price of risk. These results are robust for both market indexes, DJIA and S&P500 and for both individual and institutional investor sentiment.     

The impact of option listing on the price volatility and trading volume of underlying OTC stocks

This paper examines the impact of an initial option listing on the price volatility and trading volume of underlying OTC stocks. The sample is divided by market value to determine whether larger firms are impacted differently by option listing than smaller firms. We find relative trading volume increases significantly, with the small and medium market value firms showing the largest gain. However, the tests show no evidence of changes in price volatility following option listing. No significant changes were found in either the firms' betas or variance following option initiation. The results provide further evidence that option listing does not destabilize the market for the underlying stock.     

Availability and settlement of individual stock futures and options expiration-day effects: evidence from high-frequency data

This study examines whether the expiration-day effects of stock options traded in Australian Stock Exchange on return, volatility, trading volume, and temporary price changes of individual stocks vary with the availability and the settlement method of individual stock futures contracts. Using transaction data of the stocks that have both options and futures contacts from 1993 to 1997, we find that options expiration has significant effects on return and volatility of the underlying stocks in absence of individual stock futures. After introduction of a cash-settled stock futures contract, the effects decrease notably. However, the switch of a futures contract from cash settlement to physical delivery promotes the expiration effects on return and volatility and boosts temporary price changes on expiration days. Finally, options expiration has little effect on trading volume. Trading activity tends to behave normally regardless whether stock futures contracts are available or not.     

Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?

We study the potential merits of using trading and non-trading period market volatilities to model and forecast the stock volatility over the next one to 22 days. We demonstrate the role of overnight volatility information by estimating heterogeneous autoregressive (HAR) model specifications with and without a trading period market risk factor using ten years of high-frequency data for the 431 constituents of the S&P 500 index. The stocks’ own overnight squared returns perform poorly across stocks and forecast horizons, as well as in the asset allocation exercise. In contrast, we find overwhelming evidence that the market-level volatility, proxied by S&P Mini futures, matters significantly for improving the model fit and volatility forecasting accuracy. The greatest model fit and forecast improvements are found for short-term forecast horizons of up to five trading days, and for the non-trading period market-level volatility. The documented increase in forecast accuracy is found to be associated with the stocks’ sensitivity to the market risk factor. Finally, we show that both the trading and non-trading period market realized volatilities are relevant in an asset allocation context, as they increase the average returns, Sharpe ratios and certainty equivalent returns of a mean–variance investor.     

An application of nonparametric volatility estimators to option pricing

We discuss the impact of volatility estimates from high frequency data on derivative pricing. The principal purpose is to estimate the diffusion coefficient of an Itô process using a nonparametric Nadaraya–Watson kernel approach based on selective estimators of spot volatility proposed in the econometric literature, which are based on high frequency data. The accuracy of different spot volatility estimates is measured in terms of how accurately they can reproduce market option prices. To this aim, we fit a diffusion model to S&P 500 data, and successively, we use the calibrated model to price European call options written on the S&P 500 index. The estimation results are compared to well-known parametric alternatives available in the literature. Empirical results not only show that using intra-day data rather than daily provides better volatility estimates and hence smaller pricing errors, but also highlight that the choice of the spot volatility estimator has effective impact on pricing.     

How does HFT activity impact market volatility and the bid-ask spread after an exogenous shock? An empirical analysis on S&P 500 ETF

In this paper, we empirically analyse infra-second datasets of the SPDR S&P 500 ETF (specifically, the ETF of the S&P 500 exchanged on BATS, named SPY.Z) in order to explain how high-frequency trading (HFT) activities (aggressive and passive) impact market volatility and the bid-ask spread before and after an exogenous shock (i.e., the 2016 US presidential election). Using SPDR S&P 500 ETF datasets as a proxy for the market on regular volume trading days (November 3, 2016) and on high-volume trading days (November 9, 2016), we show that HFT, on average, has a disturbing action mainly on regular volume trading days, whereas on high-volume trading days, it appears to have a stabilizing effect by balancing both the volatility and bid-ask spread. That is, HFT as a whole has a more neutral impact on the market’s volatility and bid-ask spread than the single aggressive and passive components. In fact, aggressive HFT has a consistent negative effect that increases, on average, both the volatility and bid-ask spread, whereas passive HFT displays a positive effect that decreases, on average, the volatility and bid-ask spread.     

Price support and spreads in the IPO aftermarket: An empirical microstructure study

Using the inventory components of spreads as a measure of inventory holding-risk, we test the hypothesis of Hanley et al. [Hanley, K. W., Kumar, A., & Seguin, P. J. (1993). Price stabilization in the market for new issues. Journal of Financial Economics, 34, 177–197] that price supports reduce market makers’ inventory holding-risk in the aftermarket of initial public offerings (IPOs). We find that both spreads and their inventory components are significantly smaller in the earlier periods of the IPO aftermarket than those in the later periods. More importantly, the inventory components of spreads are significantly smaller for stocks without over-allotment options (OAOs) exercised, and for stocks with lower or negative initial returns which are more likely to have price supports. The results are consistent with the price support hypothesis.     

Options and earnings announcements: an empirical study for the European Options Exchange

In this paper we give an introduction in option pricing theory and explicitly specify the Black-Scholes model. Although market participants use this and similar models to price options, they violate one of the fundamental assumptions of the model. They do not set a constant value for the volatility of the underlying asset over time, but change the volatility even during a day. By means of event study methodology we investigate the volatility of the underlying asset and the volatility implicit in option prices around earnings announcements by firms. We find that the volatility in option prices increases before the announcement date and drops sharply afterwards. The volatility of the underlying stocks is higher only at the announcement dates and we do not observe a higher volatility around these dates. Hence, the constant volatility of the underlying asset, which is one of the assumptions in the Black-Scholes model, does not hold. However, the market seems to correctly anticipate the change in volatility, by correcting option prices.     

The ‘COVID’ crash of the 2020 U.S. Stock market

We employed the log-periodic power law singularity (LPPLS) methodology to systematically investigate the 2020 stock market crash in the U.S. equities sectors with different levels of total market capitalizations through four major U.S. stock market indexes, including the Wilshire 5000 Total Market index, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index, representing the stocks overall, the large capitalization stocks, the middle capitalization stocks and the small capitalization stocks, respectively. During the 2020 U.S. stock market crash, all four indexes lost more than a third of their values within five weeks, while both the middle capitalization stocks and the small capitalization stocks have suffered much greater losses than the large capitalization stocks and stocks overall. Our results indicate that the price trajectories of these four stock market indexes prior to the 2020 stock market crash have clearly featured the obvious LPPLS bubble pattern and were indeed in a positive bubble regime. Contrary to the popular belief that the 2020 US stock market crash was mainly due to the COVID-19 pandemic, we have shown that COVID merely served as sparks and the 2020 U.S. stock market crash had stemmed from the increasingly systemic instability of the stock market itself. We also performed the complementary post-mortem analysis of the 2020 U.S. stock market crash. Our analyses indicate that the probability density distributions of the critical time for these four indexes are positively skewed; the 2020 U.S. stock market crash originated from a bubble that had begun to form as early as September 2018; and the bubble profiles for stocks with different levels of total market capitalizations have distinct temporal patterns. This study not only sheds new light on the makings of the 2020 U.S. stock market crash but also creates a novel pipeline for future real-time crash detection and mechanism dissection of any financial market and/or economic index.     

Information transmission between small and large stocks in the National Stock Exchange in India: An empirical study

The study investigates return and volatility spillover effects between large and small stocks in the national stock exchange in India using daily index data on S&P CNX Nifty, CNX Nifty Junior and CNX Midcap. The VAR model together with the variance decomposition (VDC) and the impulse response function (IRF) analysis have been employed to uncover both casual and dynamic relationship between the large stocks and small stocks. The results show that there are very significant return spillovers from the market portfolio of large stocks to the portfolio of small stocks. To investigate the volatility spillover the study has used standard BEKK model and asymmetric BEKK model. Although, based on the standard BEKK model we have observed unidirectional volatility spillovers from the portfolio of large stocks to the portfolio of small stocks, the finding was less reliable. The more reliable finding, which is based on asymmetric BEKK model, is that there is bidirectional volatility spillover between the portfolio of large stocks and the portfolio of small stocks.     

The rise of passive investing and index-linked comovement

I introduce a general equilibrium model with active investors and indexers. Indexing causes market segmentation, and the degree of segmentation is a function of the relative wealth of indexers in the economy. Shocks to this relative wealth induce correlated shocks to discount rates of index stocks. The wealthier indexers are, the greater the resulting comovement is. I confirm empirically that S&P 500 stocks comove more with other index stocks and less with non-index stocks, and that changes in passive holdings of S&P 500 stocks predict changes in comovement of index stocks.     

Impact of leveraged ETF trading on the market quality of component stocks

We investigate the effect of leveraged ETF trading on the trading activity and market quality of their component stocks. The results show that both quoted and effective spreads of component stocks increase about 0.2–3.0 basis points after the inception of leveraged ETFs, while other liquidity measures do not show significant changes. The trading volume of component stocks is positively and significantly correlated with the trading volume of leveraged ETFs, but the volatility of component stocks is not affected by ETF trading either at the daily level or during the last hour of trading. In addition, the volatility of component stocks decreases slightly after ETF inception. These findings do not support the previous claim that the trading of leveraged ETFs increases price volatility of component stocks.     

The VIX,the variance premium and stock market volatility

We decompose the squared VIX index, derived from US S&P500 options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then examine the predictive power of the VIX and its two components for stock market returns, economic activity and financial instability. The variance premium predicts stock returns while the conditional stock market variance predicts economic activity and has a relatively higher predictive power for financial instability than does the variance premium.     

Predicting volatility using the Markov-switching multifractal model: Evidence from S&P 100 index and equity options

In this paper, we evaluate the performance of the ability of Markov-switching multifractal (MSM), implied, GARCH, and historical volatilities to predict realized volatility for both the S&P 100 index and equity options. Some important findings are as follows. First, we find that the ability of MSM and GARCH volatilities to predict realized volatility is better than that of implied and historical volatilities for both the index and equity options. Second, equity option volatility is more difficult to be forecast than index option volatility. Third, both index and equity option volatilities can be better forecast during non-global financial crisis periods than during global financial crisis periods. Fourth, equity option volatility exhibits distinct patterns conditional on various equity and option characteristics and its predictability by MSM and implied volatilities depends on these characteristics. And finally, we find that MSM volatility outperforms implied volatility in predicting equity option volatility conditional on various equity and option characteristics.     

Model risk and option hedging

This paper presents a theoretical approach to option hedging and valuation when traders are facing model risk. Model risk is restrictively defined as the financial risk resulting from the choice of an approximating model to proxy for the true but ex-ante unknown state space of the underlying security process. A generalized model is defined for estimating the appropriate volatility markup, which is dependent on the noisiness of the volatility estimate over time. Delta neutral hedge portfolios are created using simulated S&P 500 option prices to demonstrate that using a volatility markup in the traditional binomial model reduces model risk.     

The stock performance of America's 100 Best Corporate Citizens

We consider the stock performance of America's 100 Best Corporate Citizens following the annual survey by Business Ethics. We examine both possible short-term announcement effects around the time of the survey's publication, and whether longer-term returns are higher for firms that are listed as good citizens. We find some evidence of a positive market reaction to a firm's presence in the Top 100 firms that are made public, and that holders of the stock of such firms earn small abnormal returns during an announcement window. Over the year following the announcement, companies in the Top 100 yield negative abnormal returns of around 3%. However, such companies tend to be large and with stocks exhibiting a growth style, which existing studies suggest will tend to perform poorly. Once we allow for these firm characteristics, the poor performance of the highly rated firms declines. We also find companies that are newly listed as good citizens and companies in the Top 100 but outside the S&P 500 can provide considerable positive abnormal returns to investors, even after allowing for their market capitalization, price-to-book ratios, and sectoral classification.     

On implied volatility for options—Some reasons to smile and more to correct

We analyze the properties of the implied volatility, the commonly used volatility estimator by direct option price inversion. It is found that the implied volatility is subject to a systematic bias in the presence of pricing errors, which makes it inconsistent to the underlying volatility. We propose an estimator of the underlying volatility by first estimating nonparametrically the option price function, followed by inverting the nonparametrically estimated price. It is shown that the approach removes the adverse impacts of the pricing errors and produces a consistent volatility estimator for a wide range of option price models. We demonstrate the effectiveness of the proposed approach by numerical simulation and empirical analysis on S&P 500 option data.     

Trading cost expectations: Evidence from S&P 500 index replacement stock announcements

This paper tests the effect on stock value of an expected change in future trading costs. The capitalized value of a reduction in trading costs is hypothesized to increase the stock value, a trading cost effect. Improved liquidity reduces trading costs. Inclusion as an S&P 500 Index replacement stock is an event hypothesized to increase liquidity. We use 114 observations between January 1, 1983 and October 12, 1989 of stocks added to the Index as replacements for stocks removed. The abnormal return of each stock is regressed against the ratio of the bidask spread to the price of the stock, the change in trading volume of the stock, and the open interest in the Index futures contracts at the close of the month prior to the replacement announcement. We find that the positive abnormal returns for replacement stocks are related to increased daily trading volume after inclusion in the Index. Further, the trading cost effect is proportional to percentage bid-ask spreads prior to inclusion. The trading cost effect increases as trading in derivatives of the Index increases. The volume and stock price changes after replacement are not transitory, indicating an improvement in liquidity. Three alternate hypotheses suggested in prior research to explain the abnormal returns for replacement stocks are tested. Testing each of the three models previously considered: price pressure, inelastic demand curves, and information, we find that none can be accepted with statistical confidence. The abnormal returns of Index replacement stocks are consistent with rational pricing of an anticipated reduction in future transaction costs. This anticipated reduction is capitalized in the value of the stock at the time of the replacement announcement. These results are consistent with a trading cost effect.     

Implied volatility and the risk-free rate of return in options markets

We numerically solve systems of Black–Scholes formulas for implied volatility and implied risk-free rate of return. After using a seemingly unrelated regressions (SUR) model to obtain point estimates for implied volatility and implied risk-free rate, the options are re-priced using these parameters. After repricing, the difference between the market price and model price is increasing in time to expiration, while the effect of moneyness and the bid-ask spread are ambiguous. Our varying risk-free rate model yields Black–Scholes prices closer to market prices than the fixed risk-free rate model. In addition, our model is better for predicting future evolutions in model-free implied volatility as measured by the VIX.     

Does the option market produce superior forecasts of noise‐corrected volatility measures?

This paper assesses the robustness of the relative performance of spot‐ and options‐based volatility forecasts to the treatment of microstructure noise. Robustness of the results to the method of constructing option‐implied forecasts is also investigated. Using a test for superior predictive ability, model‐free implied volatility, which exploits information in the volatility ‘smile’, and at‐the‐money implied volatility, which does not, are both tested as benchmark forecasts of a range of alternative volatility proxies. The results provide compelling evidence against the model‐free forecast for three Dow Jones Industrial Average stocks, over a 2001–2006 evaluation period. In contrast, the at‐the‐money implied volatility forecast is given strong support for the three equities over this period. Neither benchmark is supported for the S&P500 index. Importantly, the main qualitative results are invariant to the method of noise correction used in measuring future volatility. Copyright © 2008 John Wiley & Sons, Ltd.