Investor sentiment, realized volatility and stock returns

PurposeThis paper examines the relationship between volatility, sentiment and returns in terms of levels and changes for both lower and higher data frequencies using quantile regression (QR) method.Design/methodology/approachIn the first step, the study applies the Granger causality test to understand the causal relationship between realized volatility, returns and sentiment as levels and changes. In the second step, the study employs a QR method to investigate whether investor sentiment and returns can predict realized volatility. This regression method gives robust results irrespective of distributional assumptions and to outliers in the dependent variable.FindingsEmpirical results show that the VIX volatility index is a better fear gauge of market-wide investors' sentiments and has a predictive power for future realized volatility in terms of levels and changes for both higher and lower data frequencies. This study provides evidence that the relationship between realized volatility, investor sentiment and returns, respectively, is not symmetric for all quantiles of QR, as opposed to OLS regression. Furthermore, this work supports the behavioral theory beyond leverage hypothesis in explaining the asymmetric relation between returns and volatility at higher and lower data frequencies.Originality/valueThis paper adds to the limited understanding of investor sentiment’s impact on volatility by proposing a QR model which provides a more complete picture of the relationship at all parts of the volatility distribution for both higher and lower data frequencies and in terms of levels and changes. To the author knowledge, this is the first paper to study the volatility responses to positive and negative sentiment changes for developed market and to use both lower and higher data frequencies as well as data in terms of levels and changes.

Predictability of OTC Option Volatility for Future Stock Volatility

This study explores the information content of the implied volatility inferred from stock index options in the over-the-counter (OTC) market, which has rarely been studied in the literature. Using OTC calls, puts, and straddles on the KOSPI 200 index, we find that implied volatility generally outperforms historical volatility in predicting future realized volatility, although it is not an unbiased estimator. The results are more apparent for options with shorter maturity. However, while implied volatility has strong predictability during normal periods, historical volatility is superior to implied volatility during a period of crisis due to the liquidity contraction of the OTC options market. This finding suggests that the OTC options market can play a role in conveying important information to predict future volatility.

Indices for Financial Market Volatility Obtained Through Fuzzy Regression

The measurement of volatility is of fundamental importance in finance. Standard market practice adopted for volatility estimation from option prices leads to a considerable loss of information and the introduction of an element of arbitrariness in the volatility index computation. We propose to adopt fuzzy regression methods in order to include all the available information from option prices, and to obtain an informative volatility index. In fact, the obtained fuzzy volatility indices not only offer a most possible value, but also a lower and an upper bound for the interval of possible values, providing investors with an additional source of information. We also propose a defuzzification procedure to select a representative value within this interval. Moreover, we investigate the occurrence of truncation and discretization errors in volatility index computation by adopting an interpolation-extrapolation method. We also test the forecasting power of each volatility index on future realized volatility.

The magic of an Allaisian appraisal: implied and historical volatility revisited the VIX: once bitten, twice shy?

Many contributions have dealt with the relation between implied and historical volatility in reference to the S&P100 index and on mostly limited samples of data. A large part of this literature finds that implied volatility defined directly from option prices is a biased estimator of future realized volatility, although some dissent has been expressed Christensen and Prabhala (1998). We investigate the issue on the larger market of the S&P500, using the VIX index as the measure of implied volatility and on a much larger sample (314 months), extending from January 1990 to December 2016. Our results are in line with most of the literature inasmuch as they invalidate the efficient market hypothesis. More originally, however, we use a time series analysis derived from Maurice Allais’s “lost” work on monetary theory and show that the VIX incorporates a subtle version of perceived and memorized past data – the “missing link” in relating implied to realized volatility - rather than reflecting any kind of “rational expectation” of future realized volatility. Incidentally, we show that the VIX seems to have been over-valued until the middle of the first decade of our century and to be since then averagely under-valued. Amazingly enough, this trend of affairs seems to be steadily confirmed by the financial market, which calls for additional research, even if we offer two possible explanations.

A Sharpe-ratio-based measure for currencies

The Sharpe Ratio offers an excellent summary of the excess return required per unit of risk invested. This work presents an adaptation of the ex-ante Sharpe Ratio for currencies where we consider a random walk approach for the currency behavior and implied volatility as a proxy for market expectations of future realized volatility. The outcome of the proposed measure seems to gauge some information on the expected required return attached to the “peso problem”.

Volatility Forecasting Using Financial Statement Information

ABSTRACT This paper examines whether financial statement information can predict future realized equity volatility incremental to market-based equity volatility forecasts. I use an analytical framework to identify accounting-based drivers of realized volatility. My main hypothesis is that accounting-based drivers can be used to forecast future realized volatility incremental to either past realized volatility or option-implied volatility. I confirm this empirically and document abnormal returns to an option-based trading strategy that takes a long (short) position in firms with financial statement information indicative of high (low) future realized volatility. These results suggest that accounting-based volatility drivers may serve as useful indicators of variance risk. Finally, I demonstrate that the incorporation of accounting-based fundamental information into forecasting models yields lower forecast errors relative to models based solely on past realized volatility.