Britten-Jones and Neuberger (2000) derived a model-free implied volatility under the diffusion assumption. In this article, we extend their model-free implied volatility to asset price processes with jumps and develop a simple method for implementing it using observed option prices. In addition, we perform a direct test of the informational efficiency of the option market using the model-free implied volatility. Our results from the Standard & Poor's 500 index (SPX) options suggest that the model-free implied volatility subsumes all information contained in the Black-Scholes (B-S) implied volatility and past realized volatility and is a more efficient forecast for future realized volatility.

George J. Jiang, Nonparametric Modeling of U.S. Interest Rate Term Structure Dynamics and Implications on the Prices of Derivative Securities, The Journal of Financial and Quantitative Analysis, Vol. 33, No. 4 (Dec., 1998), pp. 465-497

Using intraday data, we identify the intensity of private information flow in the U.S. Treasury market. Our results show that the intensity of private information flow is highly correlated with public information shocks and higher for longer maturity bonds. More importantly, we find that bond price changes associated with high intensity of private information flow tend to be persistent, whereas those associated with low intensity of private information flow are more likely reversed. While public information and private information are the main determinants of bond price variations on days with news announcements, private information and liquidity shocks are important determinants of bond price variations on days with no significant events. Finally, we show that the depth of limit order book is inversely related to the intensity of private information flow. Nevertheless, informed dealers do not seem to use hidden orders to disguise their trading intentions.

In this paper, we identify jumps in U.S. Treasury-bond (T-bond) prices and investigate what causes such unexpected large price changes. In particular, we examine the relative importance of macroeconomic news announcements versus variation in market liquidity in explaining the observed jumps in the U.S. Treasury market. We show that while jumps occur mostly at prescheduled macroeconomic announcement times, announcement surprises have limited power in explaining bond price jumps. Our analysis further shows that preannouncement liquidity shocks, such as changes in the bid-ask spread and market depth, have significant predictive power for jumps. The predictive power is significant even after controlling for information shocks. Finally, we present evidence that post-jump order flow is less informative relative to the case where there is no jump at announcement.

We identify a sample of firms with directors employed by institutional investors and examine the effect of a direct channel of institutional monitoring. Using difference-in-differences tests, we find weak evidence that institutional directors have a positive effect on informational efficiency. More importantly, institutional directors have a significantly positive impact on stock returns over the long run. Further analysis shows that the presence of institutional directors leads to a slight increase of managerial entrenchment. Consistent with the notion that entrenched managers reduce risk-taking, we also find significant decreases in R&D investments and financial leverage, and significant increases in payouts for firms with institutional directors. The findings suggest that aligning with the interest of long-term investors, institutional directors deter firms from pursuing potentially value-decreasing investment projects and influence firms to return value to investors through lower debts and higher payouts, mainly share repurchases.

We identify large discontinuous changes, known as jumps, in daily stock prices and explore the role of jumps in cross-sectional stock return predictability. Our results show that small and illiquid stocks have higher jump returns to the extent that cross-sectional differences in jumps fully account for the size and illiquidity effects. Based on value-weighted portfolios, jumps also account for the value premium. On the other hand, jumps are not the cause of momentum or net share issue effects. The findings of our study shed new light on stock return dynamics and present challenges to conventional explanations of stock return predictability.

Horizon-matched historical volatility is commonly used to forecast future volatility for option valuation under the Statement of Financial Accounting Standards (SFAS) 123R. In this paper, we empirically investigate the performance of using historical volatility to forecast long-term stock return volatility in comparison with a number of alternative forecasting methods. In analyzing forecasting errors and their impact on reported income due to option expensing, we find that historical volatility is a poor forecast for long-term volatility and that shrinkage adjustment toward comparable-firm volatility only slightly improves its performance. Forecasting performance can be improved substantially by incorporating both long memory and comovements with common market factors. We also experiment with a simple mixed-horizon realized volatility model and find its long-term forecasting performance to be more accurate than historical forecasts but less accurate than long-memory forecasts.

In this paper, we propose a nonparametric estimator of the short rate diffusion process using observations of a panel of yields. The proposed estimator can greatly reduce the bias of the nonparametric estimator proposed in Stanton (1997) that uses a single time series of short rate observations. Simulations confirm that the new method significantly attenuates the spurious nonlinearity of the drift function as documented in Chapman and Pearson (2000). We apply the method to estimate the U.S. short rate process using a panel of six Treasury yields. With 42 years’ daily observations of the panel of yields, the proposed drift function estimator achieves the same efficiency as the Stanton (1997) estimator based on 145 years of daily short rate observations. Finally, we show that the proposed estimator also has significant economic implications on the pricing of bonds and interest rate derivatives.

Using jumps in stock prices as a proxy for large information shocks, we provide evidence consistent with short-term underreaction in the US equity market. Strategies long (short) stocks with positive (negative) lagged jump returns earn significantly positive returns over the next one- to three-month horizons. The results based on intraday jumps, especially overnight jumps, provide further evidence consistent with underreaction. The underreaction is robust to controlling for other firm characteristics, extends stock return momentum over intermediate to short horizons, and captures market underreaction to information shocks beyond earnings surprises. We further show that limited investor attention contributes to short-term underreaction.

Britten-Jones and Neuberger (2000) derived a model-free implied volatility under the diffusion assumption. In this article, we extend their model-free implied volatility to asset price processes with jumps and develop a simple method for implementing it using observed option prices. In addition, we perform a direct test of the informational efficiency of the option market using the model-free implied volatility. Our results from the Standard & Poor’s 500 index (SPX) options suggest that the model-free implied volatility subsumes all information contained in the Black–Scholes (B–S) implied volatility and past realized volatility and is a more efficient forecast for future realized volatility.