Narrow Results

This paper explores the relationship between volume and volatility in the Australian Stock Market in the context of a generalized autoregressive conditional heteroskedasticity (GARCH) model. In contrast to other studies who only examine the interaction of GARCH and volume effects on a small number of stocks, we examine these effects on the entire available data for the Australian All Ordinaries Index. We also emphasize on the impact of firm size and trading volume. Our results indicate that GARCH model testing and estimation is impacted by firm size and trading volume. Specifically, our analysis produces the following major findings. First, generally, daily trading volume, used as a proxy for information arrival time, is shown to have significant explanatory power regarding the variance of daily returns. Second, the actively traded stocks which may have a larger number of information arrivals per day have a larger impact of volume on the variance of daily returns. Third, we find that low trading volume and small firm lead to a higher persistence of GARCH effects in the estimated models. Fourth, unlike the elimination effect for the top most active stocks, in general, the elimination of both autoregressive conditional heteroskedasticity (ARCH) and GARCH effects by introducing the volume variable on all other stocks on average is not as much as that for the top most active stocks. Fifth, the elimination of both ARCH and GARCH effects by introducing the volume variable is higher for stocks in the largest volume and/or the largest market capitalization quartile group. Our findings imply that the earlier findings in the literature were not a statistical fluke and that, unlike most anomalies, the volume effect on volatility is not likely to be eliminated after its discovery. In addition, our findings reject the pure random walk hypothesis for stock returns.

This paper derives an adjusted Black–Scholes pricing formula. In separating risk and uncertainty using the robust control technique, we find that both uncertainty and risk raise management's subjective evaluation of real options. We suggest a simple method to filter the risk of the project and to acquire a more reliable value of real options without the influence of uncertainty. In addition, we propose that an investment opportunity may be postponed inappropriately, as under uncertainty the exercise of investment may be delayed by the project manager. To our knowledge, any similar quantitative methods have not hitherto been mentioned in terms of isolating uncertainty from risk in real options analysis that we consider here.

The paper studies the modeling of time series with the prescribed dependence of the volatility on the sampling frequency. This dependence is often observed for financial time series. We suggest to model the dependence of volatility on sampling frequency via delay equations for the underlying prices. It appears that these equations allow to model the price processes with volatility that increases when the sampling rates increase. In addition, these equations are able to model the inverse phenomena where the volatility decreases with the increase in sampling frequencies.

The review paper provides a strategy for determining carbon emissions pricing in China to guide how carbon emissions might be mitigated to reduce fossil fuel pollution. China has promoted the development of clean energy, including hydroelectric power, wind power, and solar energy generation. In order to involve companies in carbon emissions control, regional and provincial carbon markets have been established since 2013. As China’s carbon market is organized domestically, and not necessarily using market principles, there has been little research on China’s carbon price and volatility. This paper provides an introduction to China’s regional and provincial carbon markets, proposes how to establish a national market for pricing carbon emissions, discusses how and when these markets might be established, how they might perform, and the subsequent prices for China’s regional and national carbon markets. Power generation in manufacturing consumes more than other industries, with more than 40% of total coal consumption. Apart from manufacturing, the northern China heating system relies on fossil fuels, mainly coal, which causes serious pollution. In order to understand the regional markets well, it is necessary to analyze the energy structure in these regions. Coal is the primary energy source in China, so that provinces that rely heavily on coal receive a greater number of carbon emissions permits. In order to establish a national carbon market for China, a detailed analysis of eight important regional markets is presented. The four largest energy markets, namely, Guangdong, Shanghai, Shenzhen, and Hubei, traded around 82% of the total volume and 85% of the total value of the seven markets in 2017, as the industry structure of the western area is different from that of the east. The China National Development and Reform Commission has proposed a national carbon market, which can attract investors and companies to participate in carbon emissions trading.

In this paper we provide alternative methods for pricing European and American call and put options. Our contribution lies in the simplification attempted in the models developed. Such simplification is feasible due to our observation that the value of the option can be derived as a function of the underlying stock price, strike price and time to maturity. This route is supported by the fact that both the risk-free rate and the volatility of the stock are captured by the move of the underlying stock price. Moreover, looking at the properties of the Brownian motion, widely used to map the move of the stock price, we realize that volatility is well depicted by time. Last but not the least, the value of an option is an increasing function of both time and volatility. We find simplified option pricing formulas depending on the underlying asset (price and strike price) and the time to maturity only. We test our formulas against the S&P 500 index options; the advantage of the approach is that less simplifying assumptions are needed and much simpler methods are produced. We provide alternative formulas for pricing European- and American-type options.

This study aims to find the impact of change in economic policy uncertainty (EPU) on the returns and volatilities of 11 CRSP Ziman value-weighted US real estate investment trusts (REITs) during 1985–2016. The results indicate that the change in EPU has a positive relationship with volatility and a negative one with the REITs returns. Among EPU components, news-based component has the major impact than the others. Change in economic policy uncertainty has a significant impact on the returns of all the indices except hybrid, healthcare and unclassified REITs after controlling for macroeconomic variables. Whereas, the volatility is mainly explained by its own past values and macroeconomic variables.

We use a quasi-out-of-sample forecasting experiment to study the predictive value of a short-term real interest rate for the volatility of gold-price returns. To this end, we use monthly U.S. data for the sample period from 1990/1 to 2022/2, and we study a standard effective-federal-funds-based real interest rate as well as a shadow real interest rate, which accounts for the recent extended zero-lower-bound period. We find that the real interest rate has predictive value for the subsequent realized volatility, and this predictive value turns out to be stronger in several specifications of our forecasting experiment for the shadow real interest rate than for the standard real interest rate. We evaluate the predictive value of forecasts in terms of an asymmetric loss function. Because gold is considered as a safe-haven asset, our results provide some important implications for portfolio decisions of investors.