Forecast on silver futures linked with structural breaks and day-of-the-week effect

Silver future is crucial to global financial markets. However, the existing literature rarely considers the impacts of structural breaks and day-of-the-week effect simultaneously on the volatility of silver future price. Based on heterogeneous autoregressive (HAR) theory, we establish six new type heterogeneous autoregressive (HAR) models by incorporating structural breaks and day-of-the-week effect to forecast the volatility. The empirical results indicate that new models’ accuracy is better than the original HAR model. We find that structural breaks and the day-of-the-week effect contain much forecasting information on silver forecasting. In addition, structural breaks have a positive effect on the silver futures’ volatility. Day-of-the-week effect has a significantly negative influence on silver futures’ price volatility, especially in the mid-term and the long-term. Our works is the first to combine the structural breaks and day-of-the-week effect to identify more market information. This paper provides a better forecasting method to predict silver future volatility.     

交易量,日历效应与股价波动性——基于2001～2005年中国证券市场的经验分析

以我国深沪股市的大盘指数为研究对象,分析了“日历效应”对交易量与股价波动性关系的特殊影响,并且分别考虑将原始交易量、包含自相关性的交易量、以及进行“好消息”与“坏消息”划分后的交易量引入到GARCH模型以及非对称性GARCH模型中进行研究。研究发现:原始交易量对股票指数的股价波动性开始具备了一定的解释效应,但是考虑了自相关性后交易量却无法有效解释股价波动的GARCH效应;股价的日历效应对于上海市场中交易量对股价波动性的解释有着推波助澜的作用,而“好消息”与“坏消息”的划分后的交易量可以对非对称性的股价波动性进行较好的解释。     

Spread determinants and the day-of-the-week effect

In this paper, we examine the determinants of the dollar bid–ask spread for each day of the week over the period 1998–2008. Using a panel cointegration approach, we estimate the determinants of the spread in both the short-run and long-run. Our main findings suggest that: (1) there are day-of-the-week effects for certain groups of firms; (2) the panel error correction model also reveals day-of-the-week effects, and the speed of adjustment to equilibrium following a shock is faster on Fridays; and (3) the effects of volume and volatility on the spread are mixed, with only some sectors experiencing the day-of-the-week effect.     

The day-of-the-week effect in conditional correlation

The day-of-the-week effect in the first and second moments of the return distribution is a well researched area. However, not many studies have attempted to identify this effect in the comovement or correlation of the markets. This paper models the day-of-the-week effect in the returns and the conditional correlation for some Asia-Pacific equity markets. The paper finds a Monday, Wednesday and Friday effects in the returns for some of the markets. The effect is totally absent in the returns for Australia, Japan and Korea. For the fifteen conditional correlation series estimated, a predominant Tuesday effect is detected for five series. Three series exhibit a Monday effect. A Thursday effect is detected between the Singapore market and the markets of Australia, Hong Kong and Thailand. The paper finds no consistent day-of-the-week effect in the returns and the correlations for this region. JEL Classification G15 · G14     

The day-of-the-week effect in foreign exchange markets: multi-currency evidence

To investigate to what extent transaction mechanism matters, we examine the daily returns of 29 foreign exchange rates in the New York market. This paper finds that the day-of-the-week effect existed in the 1980s for some, not all, currencies. The fact that the day-of-the-week effect existed for only some currencies suggests that the US transaction mechanism alone cannot explain the anomaly. Furthermore, this paper finds that the day-of-the-week effect disappears for almost all currencies in the 1990s. This latter result is consistent with previous studies on anomalies in the stock markets.     

Volatility transmission between Japan,UK and USA in daily stock returns

In the past, there are a lot of studies which conclude that the holiday, asymmetry and day-of-the-week effects influence stock price volatility. Most of the studies are based on a class of generalized auto-regressive conditional heteroskedasticity (GARCH) models. No one examines these effects simultaneously using stochastic volatility (SV) models. In this paper, using the SV model, we examine whether these effects play an important role in stock price volatilities. Furthermore, we consider spillover effects between Japan, UK and USA, where spillover effects in price level as well as volatility are taken into account. We are grateful to two anonymous referees for suggestions and comments. We also acknowledge Toshiaki Watanabe who gave us a lot of helpful suggestions and comments in the preliminary version of this paper. This research is partially supported from Japan Society for the Promotion of Science (Grant-in-Aid for Scientific Research (C) #18530158, 2006–2009, and Grant-in-Aid for COE Research) and the Zengin Foundation (Grant-in-Aid for Studies on Economics and Finance), which are acknowledged by H. Tanizaki.     

Presidential administrations and the day-of-the-week effect in stock returns

This study examines the presence of a day-of-the-week effect over different presidential administrations. The results indicate that the day-of-the-week effect prevails during the Democratic and Republican administrations. However, the pattern of the day-of-the-week effect differs between the two presidential administrations. Specifically, the negative returns on Monday are more pronounced during the Republican than during the Democratic administrations. Therefore, explanations for the day-of-the-week effect should take into account the changing pattern of the day-of-the-week effect across presidential administrations.     

Adding dummy variables: A simple approach for improved volatility forecasting in electricity market

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity volatility based on existing HAR models. The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon, whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily, weekly, and monthly horizons. The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility, and in most cases, dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects. The out-of-sample results were robust across three different methods. More importantly, we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.     

High frequency correlation dynamics and day-of-the-week effect: A score-driven approach in an emerging market stock exchange

Intraday correlation dynamics poses challenges to financial econometricians, especially in recently popular high frequency domain, due to non-synchronous trading and market microstructure noise. Traditional models fail to address the issues inherent to the nature of the data, which is riddled with noisy signals and missing values. We employ a recently developed method based on Generalized Autoregressive Score (GAS) framework and State-Space Modeling to remedy these characteristics of high frequency data and estimate intraday correlations in Turkish equity market Borsa Istanbul. Our findings reveal that average intraday conditional correlation rises as trading commences and lingers around certain altitude for some time, with the eigenvalues associated with market factor becoming progressively more dominant. An upward trend closes out the trading day on Mondays, which we attribute to the US market opening, whereas the rest of the week does not show a generalizable closing-time effect. Assessment of the findings across different market conditions and days of the week reveals elevated correlation levels in volatile markets as well as a distinguishable path for the beginning of the week. Beyond the scholarly contribution, the methodology can be used as a nowcasting tool and the findings are of interest to various parties like high-frequency traders, risk and portfolio managers and regulatory agencies in formulating their high frequency trading practices, hedging, portfolio construction schemes and margin requirements, respectively.