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1.
Recent research suggests that volatility has an important role to play in the appearance of the compass rose pattern. The introduction of decimal prices on the New York Stock Exchange (NYSE) provides an ideal opportunity to test this hypothesis using actual market data. The empirical evidence presented in this paper suggests that the 85 per cent reduction in the tick/volatility ratio resulting from the decimalisation of prices was not sufficient to eliminate the compass rose pattern. 相似文献
2.
Plotting daily stock returns against themselves with one day's lag reveals a striking pattern. Evenly spaced lines radiate from the origin; the thickest lines point in the major directions of the compass. This “compass rose” pattern appears in every stock. It is caused by discreteness. However, counter-examples demonstrate that the existence of exchange-imposed tick sizes (e.g. eighths) is neither necessary nor sufficient for the compass rose. The compass rose cannot be used to make abnormal profits: it is structure without predictability. Among other consequences, the compass rose may bias estimation of ARCH models, and tests for chaos. 相似文献
3.
The increasing availability of financial market data at intraday frequencies has not only led to the development of improved volatility measurements but has also inspired research into their potential value as an information source for volatility forecasting. In this paper, we explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day). First, we consider unobserved components (UC-RV) and long memory models for realised volatility which is regarded as an accurate estimator of volatility. The predictive abilities of realised volatility models are compared with those of stochastic volatility (SV) models and generalised autoregressive conditional heteroskedasticity (GARCH) models for daily return series. These historical volatility models are extended to include realised and implied volatility measures as explanatory variables for volatility. The main focus is on forecasting the daily variability of the Standard & Poor's 100 (S&P 100) stock index series for which trading data (tick by tick) of almost 7 years is analysed. The forecast assessment is based on the hypothesis of whether a forecast model is outperformed by alternative models. In particular, we will use superior predictive ability tests to investigate the relative forecast performances of some models. Since volatilities are not observed, realised volatility is taken as a proxy for actual volatility and is used for computing the forecast error. A stationary bootstrap procedure is required for computing the test statistic and its p-value. The empirical results show convincingly that realised volatility models produce far more accurate volatility forecasts compared to models based on daily returns. Long memory models seem to provide the most accurate forecasts. 相似文献
4.
We test the relation between expected and realized excess returns for the S&P 500 index from January 1994 through December 2003 using the proportional reward‐to‐risk measure to estimate expected returns. When risk is measured by historical volatility, we find no relation between expected and realized excess returns. In contrast, when risk is measured by option‐implied volatility, we find a positive and significant relation between expected and realized excess returns in the 1994–1998 subperiod. In the 1999–2003 subperiod, the option‐implied volatility risk measure yields a positive, but statistically insignificant, risk‐return relation. We attribute this performance difference to the fact that, in the 1994–1998 subperiod, return volatility was lower and the average return was much higher than in the 1999–2003 subperiod, thereby increasing the signal‐to‐noise ratio in the latter subperiod. 相似文献
5.
In this paper, we investigate IPO first-day returns in French market. Our focus is to assess the relationship between equity risk, corporate leverage and IPO initial returns. Based on data of 254 French IPOs, traded on Euronext/Alternext markets over the period 2006 and 2016, we find that estimated beta and idiosyncratic volatility are strongly and negatively related to book and market net gearing ratios. We also find that the interaction terms between equity risk measures and corporate leverage ratios are inversely related to IPO first-day returns. In addition, we highlight that industry and macroeconomic environment variables are significant predictors of equity initial returns. Robustness check of our findings indicates less relevant results for corporate leverage when it is estimated as independent variable. 相似文献
6.
This paper examines three important issues related to the relationship between stock returns and volatility. First, are Duffee's (1995) findings of the relationship between individual stock returns and volatility valid at the portfolio level? Second, is there a seasonality of the market return volatility? Lastly, do size portfolio returns react symmetrically to the market volatility during business cycles? We find that the market volatility exhibits strong autocorrelation and small size portfolio returns exhibit seasonality. However, this phenomenon is not present in large size portfolios. For the entire sample period of 1962–1995, the highest average monthly volatility occurred in October, followed by November, and then January. Examining the two sub-sample periods, we find that the average market volatility increases by 15.4% in the second sample period of 1980–1995 compared to the first sample period of 1962–1979. During the contraction period, the average market volatility is 60.9% higher than that during the expansion period. Using a binary regression model, we find that size portfolio returns react asymmetrically with the market volatility during business cycles. This paper documents a strongly negative contemporaneous relationship between the size portfolio returns and the market volatility that is consistent with the previous findings at the aggregate level, but is inconsistent with the findings at the individual firm level. In contrast with the previous findings, however, we find an ambiguous relationship between the percentage change in the market volatility and the contemporaneous stock portfolio returns. This ambiguity is attributed to strongly negative contemporaneous and one-month ahead relationships between the market volatility and portfolio returns. 相似文献
7.
In this paper, I propose a novel approach to derive a firm‐specific measure of expected return. It builds on recent accounting‐based valuation models developed by Clubb (2013) and Ashton and Wang (2013). The measure is intrinsically linked to commonly used financial ratios, including book‐to‐market, (forward) earnings yield, and dividend‐to‐price, as well as growth and past returns. The empirical evidence shows that it is significantly positively associated with future realized stock returns and also significantly correlated with commonly used risk characteristics in a theoretically predictable manner. The results are likely to be of interest to practitioners and managers in making capital allocation decisions and to academics in need of proxies for firms’ discount rates and expected returns. 相似文献
8.
This study tests whether the volatility of bid‐ask spreads is positively related to expected returns. After controlling for market‐risk factors, we find that the average risk‐adjusted excess return for stocks in the highest spread volatility quintile is around 50 basis points per month. In a variety of multivariate tests, we find robust evidence of a return premium associated with spread volatility that is both statistically significant and economically meaningful. Our results are robust to controls for a variety of stock characteristics, different tick‐size regimes, and other measures of liquidity volatility. 相似文献
9.
This paper explores the return volatility predictability inherent in high-frequency speculative returns. Our analysis focuses on a refinement of the more traditional volatility measures, the integrated volatility, which links the notion of volatility more directly to the return variance over the relevant horizon. In our empirical analysis of the foreign exchange market the integrated volatility is conveniently approximated by a cumulative sum of the squared intraday returns. Forecast horizons ranging from short intraday to 1-month intervals are investigated. We document that standard volatility models generally provide good forecasts of this economically relevant volatility measure. Moreover, the use of high-frequency returns significantly improves the longer run interdaily volatility forecasts, both in theory and practice. The results are thus directly relevant for general research methodology as well as industry applications. 相似文献
10.
We use daily survey data on Chinese institutional investors’ forecasts to measure investors’ sentiment. Our empirical model uncovers that share prices and investor sentiment do not have a long-run relation; however, in the short-run, the mood of investors follows a positive-feedback process. Hence, institutional investors are optimistic when previous market returns were positive. Contrarily, negative returns trigger a decline in sentiment, which reacts more sensitively to negative than positive returns. Investor sentiment does not predict future market movements—but a drop in confidence increases market volatility and destabilizes exchanges. EGARCH models reveal asymmetric responses in the volatility of investor sentiment; however, Granger causality tests reject volatility-spillovers between returns and sentiment. 相似文献
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