Forecasting stock market volatility: Further international evidence

Abstract

This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. First, standard (symmetric) loss functions are used to evaluate the performance of the competing models: mean absolute error, root mean squared error, and mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. Asymmetric loss functions are employed to penalize under-/over-prediction. When under-predictions are penalized more heavily, ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily, the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.     

Operational risk and equity prices

We use an empirical model to categorize firms into portfolios based on operational risk. Using these portfolios, we show that a strategy of buying firms in the highest decile of operational risk and shorting firms in the lowest decile of operational risk earned a positive but insignificant risk-adjusted average return of 0.72% per month from 1990 to 2000. However, from 2001 to 2010, the same strategy earned a significantly negative risk-adjusted average return of ?1.50% per month. This change occurred during a time characterized by an increasing number of high profile operational losses and regulatory changes surrounding operational risk.     

股市周期的模拟预报

不同于传统股市周期理论，本使用傅里叶谱分析技术来模拟预报股市周期。在说明股市周期运行特征的辨识和季节调整的方法之后，使用四年的数据建立了股市模型。在详细介绍了傅里叶周期分析法之后，检验了其对所建模型预报结果的准确性。     

The role of current and non-current accruals in the relation between stock returns and earnings

This study examines the role of accruals in the relation between stock returns and earnings for intervals of one to four years. We argue that the roles of current and non-current accruals differ because the former turn over more frequently while the latter include long term timing differences and permanent differences. Accordingly, the roles of both categories of accruals are examined over intervals within and beyond the cycle of current accruals. The results suggest that accruals strengthen the association between stock returns and earnings and that they are more important for shorter intervals. Further, non-current accruals play a dominant role in the relation between stock returns and earnings while the effect of current accruals is negligible for all intervals examined.     

The January effect across volatility regimes

Using a Markov regime switching model, this article presents evidence of the well-known January effect on stock returns. The specification allows a distinction to be drawn between two regimes: one with high volatility and another with low volatility. We obtain a time-varying January effect that is, in general, positive and significant in both volatility regimes. However, this effect is larger in the high-volatility regime. In sharp contrast with most of the previous literature, we find two major results: (1) the January effect exists for all sizes of portfolio; (2) the negative correlation between the magnitude of the January effect and portfolio size fails across volatility regimes. Moreover, our evidence supports a slight decline in the January effect for all sizes of portfolio except the smallest, for which it is even larger.     

Stock volatility predictability in bull and bear markets

The recent literature on stock return predictability suggests that it varies substantially across economic states, being strongest during bad economic times. In line with this evidence, we document that stock volatility predictability is also state dependent. In particular, in this paper, we use a large data set of high-frequency data on individual stocks and a few popular time-series volatility models to comprehensively examine how volatility forecastability varies across bull and bear states of the stock market. We find that the volatility forecast horizon is substantially longer when the market is in a bear state than when it is in a bull state. In addition, over all but the shortest horizons, the volatility forecast accuracy is higher when the market is in a bear state. This difference increases as the forecast horizon lengthens. Our study concludes that stock volatility predictability is strongest during bad economic times, proxied by bear market states.     

Do political factors affect stock returns during presidential elections?

This study investigates whether or not political factors such as government policy and political connections affected stock returns during the 2008 Taiwanese presidential election. We find that firms that benefitted from (were threatened by) the proposed Three-Links policy of the winning party experienced positive (negative) stock returns during the election. We use the sensitivities of firms’ returns to bilateral trade flows between Taiwan and China to measure the government-policy effect. Our results show that the effects of political connections weakly exist, but they become more significant when the support ratio of the winning party increased in polling data. We also find that only the government-policy effect holds for different crash-risk and corporate-governance levels. Finally, investment strategies based on both political factors can generate positive abnormal returns with respect to the Fama-French Three-factor Model.     

Oil price and reserve location—Effects on oil and gas sector returns

We provide market evidence of the effects of reserve location on oil and gas (O&G) company returns. Prior studies have shown that commodity sector stock returns are affected by commodity prices. In a new contribution to natural resource valuation literature, returns for 51 O&G companies are shown to be directly and negatively affected by exposures to (location specific) progressive fiscal terms. We add a reserve location proxy—‘R’—to the Fama–French framework; differentiating between companies' performances based on the proportion of oilfield assets subject to progressive tax terms. Companies with oilfield assets owned under progressive production sharing fiscal terms are unable to capture the benefits of oil price increases—and as result significantly under-perform companies with concession asset holdings.     

A 10-year retrospective on the determinants of Russian stock returns

We study factors influencing returns at the Russian stock market from 1995 to 2004, putting emphasis on how these evolved over time. We find that the relationship is highly unstable and this instability is not confined to financial crises alone. Most computed statistics exhibit constant ups and downs, but there has been recently a sharp rise in explainability of stock returns. Domestic factors have been playing a gradually diminishing role, while the importance of international factors has been increasing. In recent years, the effect of oil prices and foreign exchange rates has diminished, the impact of US stock prices and international and domestic interest rates has increased, while the influence of monetary aggregates such as gold reserves and credit balances has fallen to practically zero.     

An Analysis of Transactions Data for the Stock Exchange of Singapore: Patterns, Absolute Price Change, Trade Size and Number of Transactions

The availability of the transactions data of the Stock Exchange of Singapore allows us to examine intraday patterns and the relation among absolute price change, trade size and number of transactions. The presence of a trading halt in the mid-day results in two crude U-shaped return patterns but, contrary to Brock and Kleidon's (1992) model, it does not cause volume to be unusually high right before or after the halt. We find a positive relationship between absolute price changes and the number of transactions for both the active and inactive stocks. This supports the findings of Jones, Kaul and Lipson (1994) that these relationships also hold at the intraday level and in a market with different market architecture.     

机构持股、特质风险与股票收益的实证研究

机构投资者的投资行为对股票市场的风险与收益产生了极大的影响,机构投资者的大量参与有助于股票市场的稳定、特质风险的分散以及超额收益的减少。文章选取机构投资者持股占股票市值比例这一指标来刻画机构投资者的行为,检验机构持股比例、特质风险和股票收益之间的关系。实证发现,在中国股市,特质风险与股票收益呈显著的正相关关系;机构大量持股有助于降低股票的特质风险;机构投资者持股比例越低的股票,特质风险越大,股票预期收益越高。     

Foreign exchange risk and the cross-section of stock returns

We examine the relation between the cross-section of US stock returns and foreign exchange rates during the period from 1973 to 2002. We find that stocks most sensitive to foreign exchange risk (in absolute value) have lower returns than others. This implies a non-linear, negative premium for foreign exchange risk. Sensitivity to foreign exchange generates a cross-sectional spread in stock returns unexplained by existing asset-pricing models. Consequently, we form a zero-investment factor related to foreign exchange-sensitivity and show that it can reduce mean pricing errors for exchange-sensitive portfolios. One possible explanation for our findings includes Johnson's [2004. Forecast dispersion and the cross-section of expected returns. Journal of Finance, 59, 1957–1978] option-theoretic model in which expected returns are decreasing in idiosyncratic cashflow volatility.