The information content of implied volatility, skewness and kurtosis: empirical evidence from long-term CAC 40 options

Implied standard deviation is widely believed to be the best available forecast of the volatility of returns over the remaining contract life (Jorion, 1995 ). In this paper, we take this result two steps further to the higher moments of the distribution (skewness and kurtosis) based on a Gram–Charlier series expansion of the normal distribution (Corrado and Su, 1996 ) using long-term CAC 40 option prices contract, named PXL. First, we found that implied first moments contain a substantial amount of information for future moments of CAC 40 returns although this amount decreases with respect to the moment's order. Secondly, we found that the different shapes of the volatility smile are consistent with different distribution of the underlying returns. Based on these results, we also observed that including other implied moments significantly improves the out-of-sample pricing performance of the Black–Scholes, (1973) model.     

Glamour Acquirers, Method of Payment and Post‐acquisition Performance: The UK Evidence

We study the effect of different acquirer types, defined by financial status and their payment methods, on their short and long‐term performance, in terms of abnormal returns using a variety of benchmark models. For a sample of 519 UK acquirers during 1983–95, we examine the abnormal return performance of acquirers based on their pre‐bid financial status as either glamour or value acquirers using both the price to earnings (PE) ratio and market to book value ratio (MTBV). Value acquirers outperform glamour acquirers in the three‐year post‐acquisition period. One interpretation is that glamour firms have overvalued equity and tend to exploit their status and use it more often than cash to finance their acquisitions. As we move from glamour to value acquirers, there is a greater use of cash. Our results are broadly consistent with those for the US reported by Rau and Vermaelen (1998). However, in contrast to their study, we find stronger support for the method of payment hypothesis than for extrapolation hypothesis. Cash acquirers generate higher returns than equity acquirers, irrespective of their glamour/ value status. Our conclusions, based on four benchmark models for abnormal returns, suggest that stock markets in both the US and the UK may share a similar proclivity for over‐extrapolation of past performance, at least in the bid period. They also tend to reassess acquirer performance in the post‐acquisition period and correct this overextrapolation. These results have implications for the behavioural aspects of capital markets in both countries.     

Random walk and breaking trend in financial series: An econometric critique of unit root tests

The present note sheds light on several pitfalls associated with unit root tests that are overlooked by a growing volume of literature in financial economics. Specifically, several studies have confused unit root tests with the Random Walk hypothesis. Unit root tests are not designed for such a task since they aim at investigating whether a time series is difference-stationary or trend-stationary and are not, therefore, predictability tests. Secondly, we emphasize some serious shortcomings associated with the widely used unit root test developed by Zivot and Andrews [Zivot, E. & Andrews, D.W.K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics, 10, 251–270.]. In particular, we stress that results from the Zivot–Andrews test are sensitive to the methods employed to calculate the critical values and to select the maxim lag k. Furthermore, Zivot–Andrews test imposes a one time structural break in a time series; however recent studies showed that not counting for other true structural breaks may bias the results and may cause a spurious rejection of the unit root null hypothesis. Finally, we support our arguments by an empirical example based on the findings of Narayan and Smyth [Narayan, K.P. & Smyth, R. (2004). Is South Korea’s stock market efficient? Applied Economics Letters, 11, 707–710.] with regards to the efficiency of South Korean stock market. We show that contrary to what the authors claim, the KSE (KOSPI) price index is predictable, and hence the South Korean stock market is not informationally efficient.     

Nonlinearity and Low Deterministic Chaotic Behavior in Insurance Portfolio Stock Returns

This article presents new empirical evidence indicating a deterministic component in the portfolio return dynamics of life‐health and property‐liability insurance company stocks. Our research is motivated by the fact that nonlinearities are a fact of economic life for many financial applications the source of which is logically apparent, yet empirical evidence of their existence is at best weak. The primary reason attributed to the weak findings of nonlinearities reported in previous research is the use of aggregate data that can hide nonlinearities at the micro level. Insurance sector stock returns are analyzed because unique institutional characteristics indicate the possibility of identifying nonlinear dynamics. Tests based on the correlation dimension partially confirm the presence of nonlinearity. However, the more powerful Brock, Dechert, and Scheinkman (BDS) statistic strongly suggests the presence of nonlinearities in the insurance stock portfolio data. The BDS statistic applied to the standardized residuals of exponential generalized auto regressive conditional heteroskedasticity (EGARCH) models strongly rejects the null of independent and identically distributed, indicating that conditional heteroskedasticity is not responsible for the presence of the nonlinear structures in the data. In addition, tests for chaos based on locally weighted regressions indicate that insurance stock portfolio returns indicate low‐complexity chaotic behavior. This is an important result since most previous research has failed to report evidence of chaotic behavior in the time series of stock returns. Important contributions of this article are the application of tests of nonlinearities and chaos to more desegregated data sets and the findings of statistically significant evidence indicating nonlinearities and low‐deterministic chaotic behavior in insurance stock portfolio returns.     

Statistical properties of returns: Financial assets versus commercial real estate

This study examines the distribution of commercial real estate returns by region (east, midwest, south, and west), by property type (office, retail, R&D office, and warehouse) and in the aggregate, and compares their distributions to those of financial assets. Nominal and real returns are examined for quarterly, semiannual, and annual periods. The quarterly nominal returns on the financial assets are mostly normal with very little indication of autocorrelation. In contrast, non-normality and autocorrelation are present in most of the nominal quarterly real estate series. The non-normality is greatly reduced when semiannual or annual returns are considered or when the quarterly series are corrected for autocorrelation. The non-normality is also lower for real returns than it is for nominal returns.     

Implied volatility and future portfolio returns

Prior studies find that the CBOE volatility index (VIX) predicts returns on stock market indices, suggesting implied volatilities measured by VIX are a risk factor affecting security returns or an indicator of market inefficiency. We extend prior work in three important ways. First, we investigate the relationship between future returns and current implied volatility levels and innovations. Second, we examine portfolios sorted on book-to-market equity, size, and beta. Third, we control for the four Fama and French [Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56.] and Carhart [Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance, 52, 57–82.] factors. We find that VIX-related variables have strong predictive ability.     

The Impact of Settlement Procedures on Day-of-the-week Effects: Evidence from the Kuala Lumpur Stock Exchange

Using daily data from 1983 to 1993 for the Kuala Lumpur Stock Exchange Composite Index (KLSI) we examine the day-of-the-week effect. Our initial findings indicate that there is a marginally significant negative Monday effect (in keeping with US studies) and a significant positive Wednesday and Thursday effect for the whole period. We consider a number of possible explanations for these results including the impact of: closed market effects ; the time zone hypothesis ; market size and price ; the January Effect ; and the possibility of mismeasured risk . However, we believe that the most likely cause of the seasonal effects documented between 1983 and 1993, can be traced to the pre-1990 settlement procedures on the Kuala Lumpur Stock Exchange since we find that after this date nearly all of the seasonal variation in daily stock returns disappears. Thus we highlight in this paper the importance of considering the microstructure of financial markets in empirical tests of apparent market anomalies.     

Market timing and return prediction under model instability

Despite mounting empirical evidence to the contrary, the literature on predictability of stock returns almost uniformly assumes a time-invariant relationship between state variables and returns. In this paper, we propose a two-stage approach for forecasting of financial return series that are subject to breaks. The first stage adopts a reversed ordered Cusum (ROC) procedure to determine in real time when the most recent break has occurred. In the second stage, post-break data is used to estimate the parameters of the forecasting model. We compare this approach to existing alternatives for dealing with parameter instability such as the Bai–Perron method and the time-varying parameter (TVP) model. An out-of-sample forecasting experiment demonstrates considerable gains in market timing precision from adopting the proposed two-stage forecasting method.     

Explaining the accrual anomaly by market expectations of future returns and earnings

This study examines the accrual anomaly under the framework of the Campbell [Campbell, J.Y. (1991). A variance decomposition for stock returns. Economic Journal 101 (405), 157-179.] model. The Campbell (1991) model shows that realized asset returns are a joint function of 1) expected returns, 2) revisions in market expected future returns (i.e., return news), and 3) revisions in market expected future cash flows (i.e., cash flow news). The current study adopts the Easton [Easton, P. (2004). PE ratios, PEG ratios, and estimating the implied expected rate of return on equity capital. The Accounting Review 79 (1), 73-96.] model to estimate proxies for expected returns, return news, and cash flow news. The results show that firms with low accruals have lower expected returns than firms with high accruals, which is contradictory to prior research that argues that firms with low accruals are more risky. However, investors underestimate (overestimate) future earnings growth, a proxy for cash flow growth, for low (high) accrual firms. Further analysis demonstrates that earnings news (proxy for cash flow news) plays a major role in explaining abnormal returns associated with the accrual anomaly.     

In-sample vs. out-of-sample tests of stock return predictability in the context of data mining

We undertake an extensive analysis of in-sample and out-of-sample tests of stock return predictability in an effort to better understand the nature of the empirical evidence on return predictability. We find that a number of financial variables appearing in the literature display both in-sample and out-of-sample predictive ability with respect to stock returns in annual data covering most of the twentieth century. In contrast to the extant literature, we demonstrate that there is little discrepancy between in-sample and out-of-sample test results once we employ out-of-sample tests with good power. While conventional wisdom holds that out-of-sample tests help guard against data mining, Inoue and Kilian [Inoue, A., Kilian, L., 2004. In-sample or out-of-sample tests of predictability: which one should we use? Econometric Reviews 23, 371–402.] recently argue that in-sample and out-of-sample tests are equally susceptible to data mining biases. Using a bootstrap procedure that explicitly accounts for data mining, we still find that certain financial variables display significant in-sample and out-of-sample predictive ability with respect to stock returns.