Future Price Volatility Research Articles

Measuring the response of clean energy stock price volatility to extreme shocks

The development of clean energy has made the green finance market attractive to investors, but existing studies lack an analysis of the impact of uncertain environmental shocks on the volatility of green finance stocks. This paper focuses on the impact of major events on clean energy stock returns by using a mixed-frequency model. Specifically, to identify asymmetric effects and extreme information spillovers, we construct extreme positive returns/positive returns and extreme negative returns/negative returns, respectively. The in-sample findings suggest that asymmetric effects and extreme shocks contain valid information about future clean energy stock price volatility. The out-of-sample analysis shows that the extended model can successfully predict the volatility of clean energy stock markets. More importantly, the extended model can generate predictive gains from both statistical and economic aspects. This study can provide a reference for implementing and adjusting energy policies, which is important for optimizing the energy structure and developing the green energy industry.

Multidimensional Risk-Based Real Options Valuation for Low-Carbon Cogeneration Pathways

Energy price fluctuations pose a significant risk and uncertainty to financial investments for new developments in conventional power and freshwater cogeneration facilities. This study attempts to address the problem of making robust valuation for low-carbon energy project investments subject to multi-dimensional price risk, particularly looking at some key research questions: (a) how does the correlation structure, or independence, between the price risks affect the project value; and (b) does adding flexibility in investment enhance or worsen the project valuation, given (a). This study identified three price factors with significant fluctuations that impact conventional power generation, namely: wholesale electricity spot price, natural gas spot price, and CO2 market price. The price factors were used to construct a multidimensional risk model and evaluate investment decisions for cogeneration project expansion in the future based on a low-carbon energy mix. To this end, five cogeneration configurations using combined-cycle gas turbine (CCGT) integrated with solar photovoltaics (PV) and carbon capture and storage (CCS) technologies were assessed. A combined price risk was initially estimated by transforming the given price factors representing maximum covariance using principal component analysis (PCA). The trend and volatilities in the major principal component scores (the combined price risk indicator) were modelled using the geometric Brownian motion stochastic process, whose parameters were determined and then used to perform time-series simulation and generate multiple realisations of the principal component. A back transformation was then applied to obtain the simulated values representing future uncertainties in the price factors. The effect of price risk and uncertainties were subsequently evaluated using a recombining binomial lattice model for real options analysis (ROA). There were financial gains when PV was mixed with conventional natural gas-fired technology. Investment in cogeneration configurations with (a) 25% PV share provided a 53% gain in the extended net present value (e–NPV); and (b) 50% PV share provided a 124% e–NPV gain when compared to the baseline cogeneration system with no PV shares. The analyses demonstrate that PV technology is a better hedging option than CCS against future market uncertainty and price volatility.

What Causes Future Gold Price Volatility in Indonesia?

The purpose of this study is to determine the factors that influence the future volatility of gold commodities in Indonesia. Methods of quantitative research as well as secondary data are utilized in this study. The years 2011 to 2018 are used in this study example. On the official website, there is a total of 32 data collected quarterly. SPSS version 24 is the software that is being utilized. The findings, the exchange rate is the only factor that has impact on future gold commodities. Other factors, including as the consumer price index and the stock market index, have no such impact. Research can be used to determine the reasons of price volatility from macroeconomic influences on future gold commodities

Volatility Forecasting of Copper Futures Based on HAR-RV Model

As an important part of the international futures market, copper price prediction is important for international financial market research. This paper selects the high-frequency data every 5 minutes from the database and uses the HAR-RV model based on realized volatility. By introducing investor sentiment and the day of week effects, we have established three new types of non-uniform autoregressive models. Empirical analysis shows that the weekly and monthly fluctuations of copper futures prices are relatively small, while the daily fluctuations are relatively large. The prediction model is more accurate when predicting the long-term volatility, and the stability test shows that the HAR-RV model is relatively stable when predicting the long-term volatility. Investor sentiment has a negative impact on the price volatility of copper futures in the medium and long-term forecasts. Weekend effects have a negative impact on the medium and long-term forecasts of copper futures. This paper complements the existing literature and improves the prediction ability of copper price fluctuations, which is very important to promote effective hedging, risk transfer, and price discovery in the futures market.

U.S. grain commodity futures price volatility: Does trade policy uncertainty matter?

The outbreak and continuation of the COVID-19 pandemic have affected the trade policies of various countries and influenced global food security. This paper aims to use U.S. major grain commodity futures price and trade policy uncertainty (TPU) index data to examine the impact of TPU on the volatility of U.S. grain futures prices under the GARCH-MIDAS framework. The in-sample estimates confirm the impact of TPU on the volatility of US grain commodity futures prices. Out-of-sample testing further reveals that considering TPU could improve predictions of future price fluctuations for different grain commodities. Finally, we also consider other uncertainty indices. Since the grain market is often used as a tool to hedge financial risks, this article can provide some advice for investors in times of policy instability and especially trade policy uncertainty.

Impact of Geopolitical Risk on the Volatility of Natural Resource Commodity Futures Prices in China

Impact of Geopolitical Risk on the Volatility of Natural Resource Commodity Futures Prices in China

Volatilnost prinosa na berzi - primena Monte Karlo simulacije

Stock exchange is the "mirror" of the economy and helps industry (and commerce) to accelerate the development of the country. The prices on the stock exchanges increase or decrease over the particular period and that rate represents stock market volatility. Higher stock price volatility is often associated with higher risk and indicates future fluctuations to investors in order to evaluate them. Predicting future stock price volatility can provide important information to market participants and enable them to make adequate decisions. The aim of this paper is to evaluate the stock price volatility of the Apple Company using the Monte Carlo simulation.

Cryptocurrency price volatility and investor attention

This study examines the relationship between the price volatility of cryptocurrencies and investor attention. Using a large dataset of approximately 25 million tweets about 23 of the largest cryptocurrencies, I show that investor attention, as proxied by the number of tweets, retweets, and favorites, corresponds to greater cryptocurrency price volatility. I use a Vector Autoregression (VAR) framework to show that investor attention predicts future price volatility. Additionally, days on which investors are “distracted” because of attention-grabbing events correspond to lower price volatility in cryptocurrency markets. The results suggest that increased investor attention to cryptocurrencies has the undesirable effect of increasing price volatility.

Impact of the forecast price on economic results for methanol production from olive waste

The development of circular economies due to the limitation of natural resources is becoming a common strategy of paramount importance among different countries. In Spain, given the strategic nature of its olive industry, trying to value one of its main residuals (olive pomace) alone or together with other residues through its chemical transformation in methanol is a promising research line. One of the key variables that would make the investment advisable or not is the future value of the methanol price. However, most of the literature do not consider its future price volatility on the economic evaluation of the chemical processes. This work bridges that gap by proposing three econometric models based on Unobserved Components to forecast the methanol price over the life cycle plant. Those probabilistic forecasts feed a Monte Carlo simulation that provides an exhaustive investment risk assessment in terms of Net Present Value, Internal Rate of Return and Value at Risk metrics. The results showed the relationship between forecasting models and the investment profitability with an average Internal Rate of Return ranging from 23% to 31%. Additionally, the previous analysis was completed by adding other variables subject to uncertainty (olive pomace feed, capital investment, feedstock price, labor costs and discount rate). In this case, assuming a potential underestimation error up to 100% of the capital cost, the probability of obtaining a profitable investment was significantly reduced ranging the Value at Risk from 48% to 98%.

Volatility patterns of short-term interest rate futures

A general question in finance is whether the volatility of the price of futures contracts follows any particular trend over the contract’s life. In this study, we contribute to the debate by empirically analyzing the trend of the term structure of the volatility of short-term interest rates (STIR) futures prices. Using data on the Eurodollar, Euribor, and Short-Sterling futures contracts for the period between 2000 and 2018, we model the volatility of each individual contract considering time to expiration and trading activities. Furthermore, we investigate whether these trends change according to overall economic conditions. We find that STIR futures behave differently than futures on other underlying assets and that, most of the time, STIR futures price volatility declines as the contract approaches expiration. Moreover, the relation between volatility and time to maturity depends on market conditions and trading activities, and it is non-linearly related to the observation period.

On the price volatility of steel futures and its influencing factors in China

Steel futures have the function of price discovery and hedging. Steel related enterprises can judge the hedging strategy through the direction of steel futures price volatility, and reasonably avoid the risk brought by price volatility. Therefore, it is particularly important to study steel futures price volatility and its influencing factors. Because steel futures in China have characteristics of peak and rear tail aggregation, the paper constructs Model of GARCH (1,1) to make positive analysis of futures price volatility and its influencing factors of deformed steel bars and hot rolled coils, and the following conclusions have been drawn: (1) The volume and open interest of deformed steel bars have very significant explanatory ability to futures price volatility of deformed steel bars; (2) The volume and open interest of hot rolled coils also have very significant explanatory ability to futures price volatility of hot rolled coils; (3) The sustainable capacity of the price volatility of deformed steel bars and hot rolled coils is relatively small; (4) Iron ore price have no obvious explanatory ability to futures price volatility. Finally, some managerial implications and suggestions are derived from the analysis of the proposed model.

Industrial applications of selected JFS articles

Industrial applications of selected <i>JFS</i> articles

Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data

This article takes into account the form of mixed data as well as the peak and thick tail characteristics contained in the data characteristics, expands the GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling) model, establishes a new GARCH-MIDAS model with the residual term of the skewed-t distribution, and analyzes the influence factors of crude oil futures price volatility, which can better explain the changing laws of crude oil price volatility. The results show the following: First, the low-frequency factors include crude oil production, consumption, inventory, and natural gas spot price, and the high-frequency factors include on-market trading volume and off-market spot price, which can significantly explain the volatility of oil price. Second, low-frequency factors include crude oil inventory, consumption, crude oil production, and speculative factors, and high-frequency factors include crude oil spot price and substitute prices. The increase in the volatility of trading volume is significantly positively correlated with oil price volatility, and the overall volatility model outperforms the horizontal effect model. Third, from the perspective of the combined effect of a single factor level and volatility, we find that supply and demand are the low-frequency factors; the trading volume of on-market factors, natural gas price, and crude oil spot price of off-market factors, among the high-frequency factors, are the most important factors affecting oil price volatility. Fourth, from the perspective of high-frequency and low-frequency effects combined, there is no significant difference between the various factor models, which shows that the mixed effect model of high and low frequency models has advantages in terms of the stability of the estimation results.

Forecasting Realized Volatility of Crude Oil Futures Prices based on Variable Selection Approaches

We augment the HAR model with additional information channels to forecast realized volatility of WTI futures prices. These channels include stock markets, sentiment indices, commodity and FX markets, and text-based Google indices. We then apply four differing machine learning techniques to identify the most suitable endo- and exogenous factors which improve baseline model forecasts. We show that machine learning generated forecasts provide better forecasting quality and that portfolios which are constructed with these forecasts outperform their competing models. We find LASSO and SSVS to provide outperforming forecasts and portfolio weights. Analyzing the selection process, we show that variable choices vary across forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to the significance of information channels.

Practical Applications of Can Commodity Price Uncertainty Indexes Be Improved by Capturing Media Information? The Case of Oil Price Uncertainty

Practical Applications In Can Commodity Price Uncertainty Indexes Be Improved by Capturing Media Information? The Case of Oil Price Uncertainty from the Spring 2020 issue of The Journal of Alternative Investments, authors Yosef Bonaparte (of the J.P. Morgan Center for Commodities at the University of Colorado-Denver), Frank J. Fabozzi (of the EDHEC Business School in Nice, France), and David Koslowsky (of the University of Wisconsin-Superior) present a new and improved method of predicting how much oil prices will fluctuate in the future. Traditional oil price volatility indexes, such as the OVX index, only use historical pricing data on oil options and reflect the market’s expectation about future price volatility. The authors find that the OVX by itself tends to underpredict actual oil price volatility, and note that the changes in oil price volatility have correlated with changes in the frequency of news media reports on oil price uncertainty. They therefore create a new index, the OPUX, that includes data on the frequency of news media reports and social media posts on oil price uncertainty. They find that OPUX predicts actual oil price volatility more accurately than OVX, and they recommend using indexes that incorporate the frequency of news media reports for purposes of predicting future price volatilities. TOPICS:Commodities, mutual funds/passive investing/indexing, information providers/credit ratings

Housing Risk and Its Influence on House Price: An Expected Utility Approach

House price is affected by households’ expectation of future house price trend and volatility, where the expected volatility of housing capital return, indicated by variance, is defined as the housing market risk. Theoretically, risk element cannot be directly inserted in the standard housing models because most of the models are built on the underlying assumption of certainty. Extending the life-cycle model to a two-asset expected utility case with uncertainty, we show house price is affected by housing market risk premium, which is a function of households’ risk-aversion coefficient, real housing wealth, and expected housing volatility. Empirical analysis relying on China’s 2001–2018 provincial housing panel data supports the theoretical innovation. Despite the empirical results show that the effect of housing market risk on house price is tiny, simulations suggest that the consideration of risk is quite helpful in analyzing and predicting the long-run house price equilibriums.

Can Commodity Price Uncertainty Indexes Be Improved by Capturing Media Information? The Case of Oil Price Uncertainty

Uncertainty indexes are used in economics to assess the uncertainty regarding economic policy, and in finance to quantify the uncertainty associated with the price volatility of financial instruments and commodities. Existing uncertainty indexes are typically constructed using realized (historical) price volatility, implied volatility when options are available, and conditional volatility using various econometric models. In this article, the authors present an improved commodity price uncertainty index, an oil price uncertainty index that includes information from news articles and social media. They demonstrate the superior performance of their new index compared with existing indexes that do not include news and social media information. They also create an oil price volatility forecasting model based on media coverage and use it to trade an oil volatility portfolio, obtaining a return that exceeds the average monthly growth of oil price volatility. Altogether, media coverage is a powerful predictor of future price volatility in the oil market. TOPICS:Volatility measures, performance measurement Key Findings • Media information from newspapers and Twitter about oil price uncertainty is combined with the existing OVX oil ETF volatility index to create a new oil price uncertainty index (OPUX). • The OPUX model gives a significantly better prediction of 1-month forward oil price volatility, as measured by an increase in R-squared from 12.1% for OVX to 33.0% for OPUX. • A volatility trading model based on OPUX exceeds the average oil price volatility by 0.29% per month.

Futures hedging with stochastic volatility: a new method

The aim of this paper is to present a continuous-time dynamic model of futures hedging. In particular, we extend the theoretical and empirical literature (e.g., Alghalith, 2016; Alghalith et al., 2015; Corsi et al., 2008) in several important ways. First, we present a theory-based model. A significant empirical contribution is that we do not need data for the basis risk or the spot price. To the best of our knowledge, this is the first paper to assume that the volatility of futures price is stochastic and thus to estimate the volatility of volatility of futures price. Using daily futures data from the S&P500 index, we calculate an average daily volatility as well as the volatility of volatility of futures prices. We recommend that the managers of the futures market should report the stochastic volatility of the futures price (and its volatility), in addition to the traditional volatility.

A Theory of Rational Investment Screens

We develop a model to explain the value and consequences of investment screens, which are commonly employed by sophisticated investors. In the model, some stock-market investors are uncertain about the quality of private information before they acquire it and, in equilibrium, rationally use prior prices and public information as a screen to predict the returns from information acquisition. We find that larger price surprises lead to more information acquisition, which implies higher future price volatility and trading volumes. We also highlight the determinants of the equilibrium value of investment screens, such as informed trade, noise trade, and public information.

Modeling and forecasting the oil volatility index

AbstractThe increase in oil price volatility in recent years has raised the importance of forecasting it accurately for valuing and hedging investments. The paper models and forecasts the crude oil exchange‐traded funds (ETF) volatility index, which has been used in the last years as an important alternative measure to track and analyze the volatility of future oil prices. Analysis of the oil volatility index suggests that it presents features similar to those of the daily market volatility index, such as long memory, which is modeled using well‐known heterogeneous autoregressive (HAR) specifications and new extensions that are based on net and scaled measures of oil price changes. The aim is to improve the forecasting performance of the traditional HAR models by including predictors that capture the impact of oil price changes on the economy. The performance of the new proposals and benchmarks is evaluated with the model confidence set (MCS) and the Generalized‐AutoContouR (G‐ACR) tests in terms of point forecasts and density forecasting, respectively. We find that including the leverage in the conditional mean or variance of the basic HAR model increases its predictive ability. Furthermore, when considering density forecasting, the best models are a conditional heteroskedastic HAR model that includes a scaled measure of oil price changes, and a HAR model with errors following an exponential generalized autoregressive conditional heteroskedasticity specification. In both cases, we consider a flexible distribution for the errors of the conditional heteroskedastic process.