Changes In Price Volatility Research Articles

Environmental Risks and Agricultural Commodities

As natural disasters are expected to increase both in severity and frequency, their impact on agricultural commodities and the food supply chain is likely to rise in tandem. We use a unique data set of natural disasters that occurred globally between 1970 and 2015 and study their impact on the price and price volatility of eight globally traded agricultural commodities that account for more than 75% of the food supply in the world. Our results show that price and the price volatility of commodities are impacted around the occurrence of a natural disaster in producing countries. In addition, we find that the affected country’s production shares, the total damage caused by a natural disaster relative to the affected country’s GDP, and the demand pressure for the commodity have an impact on the magnitude of price and price volatility changes of a given commodity around the occurrence of a natural disaster.

Tác động của thanh khoản đến biến động giá cổ phiếu: Nghiên cứu trường hợp các ngân hàng thương mại cổ phần Việt Nam

Stock price is considered the top priority factor in attracting the attention of investors when investing in the stock market. In developed markets, changes in liquidity can help predict future changes in price volatility. The paper was conducted with the aim of analyzing the impact of banking liquidity on the stock price volatility of commercial banks listed on the Vietnam stock market. With a data sample of 27 commercial banks in the period from 2012 to 2022. By regressing panel data through system- GMM estimation (SGMM), the research results show that banking liquidity (LIQ) and bank size (SIZE) have a negative impact on banking stock price volatility (SPV). Meanwhile, changes in the deposit interest rate (IR) and changes in the exchange rate (EXR) have a positive impact on the volatility of banking stock prices (SPV). Besides, the study does not have enough evidence to prove that allowance for credit losses (AFCL), the price-to-earnings ratio (P/E) and the economic growth (GDP) have an impact on banking stock price volatility (SPV).

CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

Cryptocurrency is a virtual payment instrument currently popular as an investment alternative. One type of cryptocurrency widely used as an investment is Bitcoin due to its high-profit potential and risk due to unstable exchange rate fluctuations. This high exchange rate fluctuation makes trading transactions in the crypto market speculative and highly volatile. To overcome this volatility factor, this research used the Generalized Autoregressive Conditional Heteroscedasticity forecasting method to describe the heteroscedasticity factor, as well as a Recurrent Neural Network (RNN) with long-short-term memory that has feedback in modeling sequential data for time series analysis. The two methods are combined to overcome the dependency of time series data in the long term and the heteroscedastic effect of the volatility of price changes. The results of the GARCH-LSTM hybrid model in this study show a Mean Absolute Percentage Error (MAPE) value of 15.69%. The accuracy value is obtained from the division of training data by 80% and testing data by 20%, with the number of neurons as many as three and epochs of 100 using the Adam optimizer. The MAPE accuracy results show a good prediction in predicting the value.

The oil price uncertainty effect on stock returns of the Indian renewable energy firms under different market conditions

AbstractThis study investigates the oil price uncertainty effect on stock return of the Indian renewable energy firms under different market conditions. We use the Oil Volatility Index (OVX) as a proxy of oil price uncertainty. All renewable energy companies listed on the National Stock Exchange of India are considered. Monthly data are analysed from January 2009 to December 2019 using panel quantile regression. In the whole sample of the renewable energy firms, we find that the impact of oil price volatility on stock return is negative only in extreme market conditions. In subgroups of renewable energy firms, oil price volatility has negative coefficients in most quantiles. The oil price volatility change is decomposed into positive and negative changes to investigate the asymmetric effect. In subgroups of renewable energy firms' analysis, most of the asymmetric effects of oil price volatility are seen in the bearish market condition. The policymakers can use the findings to formulate policies as per the oil price volatility movement to ensure the growth of the renewable energy sector. Investors and portfolio managers can make appropriate strategies based on oil price volatility to maximise the investment return across market conditions.

Asymmetric volatility dynamics in cryptocurrency markets on multi-time scales

This study investigates the scale-dependent structure of asymmetric volatility effect in six representative cryptocurrencies: Bitcoin, Ethereum, Ripple, Litecoin, Monero, and Dash. By developing the dynamical approach of DFA-based fractal regression analysis, we detect whether the volatility of price changes is positively or negatively related to return shocks at different time scales. We find that the asymmetric volatility phenomenon varies by scale and cryptocurrency, and the structure is time-varying. Contrary to what is typically observed in equity markets, minor currencies show an “inverse” asymmetric volatility effect at relatively large scales, where positive shocks (good news) have a greater impact on volatility than negative shocks (bad news). The consequences are discussed in the context of who is trading in the market and heterogeneity of the investors.

The effect of futures markets on the stability of commodity prices

Do futures markets have a stabilizing or destabilizing effect on commodity prices? The empirical evidence is inconclusive. We try to resolve this question by means of a learning-to-forecast experiment in which a futures market and a spot market are coupled. The strength of the coupling depends positively on the number of speculators on the futures market and negatively on storage costs and speculator risk aversion. We find that the spot price volatility changes non-monotonically with the strength of the coupling, resulting in a stabilizing effect on spot prices for weakly coupled markets and a destabilizing effect when the coupling with the futures market is strong.

Modeling the Price Volatility of Cassava Chips in Thailand: Evidence from Bayesian GARCH-X Estimates

Thailand is a significant global exporter of cassava, of which cassava chips are the main export products. Moreover, China was the most important export market for Thailand from 2000 to 2020. However, during that period, Thailand confronted fluctuations in the cassava product price, and cassava chips were a product with significant price volatility, adapting to changes in export volumes. This study aims to analyze the volatility of the price of cassava chips in Thailand from 2010 to 2020. The data were collected monthly from 2010 to 2020, including the price of cassava chips in Thailand (Y), the volume of cassava China imported from Thailand (X1), the price of the cassava chips that China imported from Thailand (X2), the price of the cassava starch that China imported from Thailand (X3), the substitute crop price for maize (X4), the substitute crop price for wheat (X5), and Thailand’s cassava product export volume (X6). The volatility and the factors affecting the volatility in the price of cassava chips were calculated using Bayesian GARCH-X. The results indicate that the increase in X1, X2, X3, X4, and X6 led to an increase in the rate of change in cassava chip price volatility. On the other hand, if the substitute crop price for wheat (X5) increases, then the rate of change in the volatility of the cassava chip price decreases. Therefore, the government’s formulation of an appropriate cassava policy should take volatility and the factors affecting price volatility into account. Additionally, the government’s formulation of agricultural policy needs to consider Thailand’s macro-environmental factors and its key trading partners, especially when these environmental factors signal changes in the price volatility of cassava.

Climate change and small pelagic fish price volatility

Price volatility, an indicative measure of price uncertainty, is an important factor influencing fisheries cost, income, food security, and pressure on fish stocks. An increase in price volatility means that prices are varying more, making it more difficult to adjust the activities accordingly. This paper explores price volatility changes for the small pelagic fish—a key group for food security—related to the potential outcomes of climate change, such as tropical widening. With the tropicalization of temperate and polar marine ecosystems due to species composition changes, one would expect that overall price volatility for the small pelagics may be reduced over time because volatility in the tropical region is on average the lowest. However, we find an empirical evidence that price volatility for small pelagic species has increased relatively in the tropical and sub-polar regions beyond what can be explained by changes in landing levels and the variation in the landings.

Asymmetric volatility in commodity markets

The paper studies the return–volatility relationship in a range of commodities. We develop a commodity price model and show that the volatility of price changes can be positively or negatively related to demand shocks. An “inverse leverage effect”—the volatility is higher following positive price shocks—is found in more than half of the daily spot prices. The effect is weaker in the three-month futures market, the period after mid-2000s and monthly historical volatility measures. Only crude oil exhibits a “leverage effect”—higher volatility follows a negative shock—and the reason is explored in the context of its special market structure.

Asymmetric Volatility in Commodity Markets

The paper studies the return-volatility relationship in a range of commodities. We develop a commodity price model and show that the volatility of price changes can be positively or negatively related to demand shocks. An “inverse leverage effect” – the volatility is higher following positive price shocks – is found in more than half of the daily spot prices. The effect is weaker in 3-month futures market and monthly historical volatility measures. Only crude oil exhibits a “leverage effect” – a higher volatility follows a negative shock, and the reason is explored in the context of its special market structure.

Exposure to Daily Price Changes and Inflation Expectations

We show that, when forming expectations about aggregate inflation, consumers rely on the prices of goods in their personal grocery bundles. Our analysis uses novel representative micro data that uniquely match individual expectations, detailed information about consumption bundles, and item-level prices. The data also reveal that the weights consumers assign to price changes depend on the frequency of purchase, rather than expenditure share, and that positive price changes loom larger than similar-sized negative price changes. Prices of goods offered in the same store but not purchased (any more) do not affect inflation expectations, nor do other dimensions such as the volatility of price changes. Our results provide empirical guidance for models of expectations formation with heterogeneous consumers.

Macroeconomic Uncertainty and Oil Price Volatility

AbstractThis paper shows that higher macroeconomic uncertainty causes higher oil price volatility. Regimes of low and high uncertainty are identified in a threshold VAR model in which the effects of structural oil demand and supply shocks are estimated. The results show that higher macroeconomic uncertainty, as measured by global industrial production volatility, significantly increases the sensitivity of oil prices to shocks in oil demand and supply. This occurs as uncertainty lowers the price elasticity of oil demand and supply. The difference in the estimated oil price elasticities is economically meaningful as the price impact of a similar change in oil production might double when it hits the economy in uncertain times. As such, varying uncertainty can explain why oil price volatility is typically higher during periods such as financial crises and recessions, and why oil price volatility changes over time more generally.

A closer insight into the causality between short selling trades and volatility

This study addresses the Granger causality between short selling activities and stock price volatility in the US stock market, utilizing daily data and advanced methodologies.The results denote a bilateral causality between the short selling trades and the volatility of price changes in the US stock market at individual stock as well as at market level. The bilateral causality relationship persists during different market conjunctures.The findings imply that measures to curb excess price volatility stemming from short selling trades were relatively ineffective and the policy-makers need to impose more stringent restrictions to prevent market destabilization.

신선 물오징어 소매가격 변동성의 구조변화와 비대칭성 검증

This study analyzed structural changes and asymmetry of price volatility during the period before and after a point of structural change in price volatility, using the Korean fresh common squid daily retail price data from January 1, 2004 to September 30, 2015. This study utilized the following analytical methods: the unit-root test was applied to ensure the stability of the data, the Quandt-Andrews breakpoint test was applied to find the point of structural change, and the Glosten-Jagannathan-Runkle GARCH and EGARCH models were applied to investigate the asymmetry of price volatility. The empirical results of this study are as follows. First, ADF, PP, KPSS and Zivot-Andrews tests showed that the daily retail price change rate of the Korean fresh common squid differentiated by logarithm was stable. Secondly, the ARIMA (2,1,2) model was selected by information criteria such as AIC, SC, and HQ. Thirdly, the Quandt-Andrews breakpoint test found that a single structural change in price volatility occurred on June 11, 2009. Fourthly, the Glosten-Jagannathan-Runkle GARCH and EGARCH models showed that estimates of coefficients within the models were statistically significant before and after structural change and also that asymmetry as a leverage effect existed before and after structural change.

Why do carbon prices and price volatility change?

Abstract An asymmetric information microstructural pricing model is proposed in which price responses to information and liquidity vary with every transaction. bid-ask quotes and price components account for learning by incorporating changing expectations of the rate of transacted volume (trading intensity) and the risk level of incoming trades. Analysis of European carbon futures transactions finds expected trading intensity to simultaneously increase the information component and decrease the liquidity component of price changes, but at different rates. This explains some conflicting results in prior literature. Further, the expected persistence in trading intensity explains the majority of the autocorrelations in the level and the conditional variance of price change; helps predict hourly patterns in returns, variance and the bid-ask spread; and differentiates the price impact of buy versus sell and continuing versus reversing trades.

The Impact of Price Volatility of Agricultural Commodities in Polandon Alternative Incomes of Conventional, Ecological and AgritourismFarms

The objective of this paper is to recognize price volatility of main commodities and to measure the impact of price level on agricultural incomes of farms and the number of farms having alternative income sources. First we focused on price volatility changes in the European Union and Poland. Economic and econometric analysis tools were used to measure the impact of the price volatility. We collected observations from the Ministry of Agriculture and Rural Development in Poland. The analysis found big differences in price volatility for Polish commodities. The price volatility was connected with the global crisis. The Polish agricultural sector faced global problems, but could not fully overcome them. The rise of agricultural commodity prices created market problems such as increased bread and food prices. Polish consumers adjusted their purchases and their expenditures on food. This situation is common for many EU states and the price volatility can be reduced only through the Common Agricultural Policy. Poland’s markets are global and stabilization is achievable only by active EU rural policy.

양식 넙치가격 변동성의 구조변화와 비대칭성 검증

This study is to analyse the timing of the structural change of price volatility and the asymmetry of price volatility during the period before and after the timing of the structural change of price volatility using Jeju Farming Olive Flounder's production area market price data from January 1, 2007 to June 30, 2013. The analysis methods of Quandt-Andrews break point test and Threshold GARCH model are employed. The empirical results of this study are summarized as follows: First, the result of Quandt-Andrews break point test shows that a single structural change in price volatility occurred on May 4, 2010 over the sample period. Second, during the period before structural change, daily price change rate has averagely positive value which means price increase, but during the period after structural change daily price change rate has averagely negative value which means price decrease. Also, daily volatility of price change rate during the period before structural change is higher than during the period after structural change. This indicates that price volatility decreases after structural change. Third, the estimation results of Threshold GARCH Model show that the volatility response against price increase is larger during the period after structural change than during the period before structural change. Also the result shows the volatility response against price decrease is larger during the period after structural change than during the period before structural change. And, irrespective of the timing of structural change, price increase has an larger effect on volatility than price decrease. This means volatility is asymmetric at price increase.

Asymmetric Information Spillovers between Trading Volume and Price Changes in Malaysian Futures Market

This study aims to examine the dynamics of price changes and trading volume of Kuala Lumpur Options and Financial Futures Exchange (KLOFFE) from 2000 to 2008. With augmented analysis, our results support two hypotheses. First, under information spillover, our findings support noise traders' hypothesis as the time span for variance of past trading volume to cause variance of current return is found to be asymmetric under bull and bear markets. Second, looking at the dynamic relation between volume and volatility of price changes, our findings support Liquidity-Driven Trade hypothesis as past trading volume and subsequent volatility of return exhibit positive correlation. In terms of investors' behavior in response to the news, we find that investors are more risk taking in bull market and more risk reverse in bear market. Our study suggests that investors should adjust their strategy in the futures market in a dynamic manner as the time span of new information arrival is not consistent. Also, uninformed investors with information asymmetry should expect noninformational trading from informed investors to establish their desired positions for better liquid position.

Multi-period experimental asset markets with distinct fundamental value regimes

In this methodological study we analyze price adjustment processes in multi-period laboratory asset markets with five distinct fundamental value \((\hbox {FV})\) regimes in a unified framework. Minimizing the effect of between-treatment variations we run markets with deterministically decreasing, constant, randomly fluctuating and—as main innovation—markets with deterministically increasing \(\hbox {FV}\)s. We find (i) efficient pricing in markets with constant \(\hbox {FV}\)s, (ii) overvaluation in markets with decreasing \(\hbox {FV}\)s, and (iii) undervaluation in markets with increasing \(\hbox {FV}\)s. (iv) Markets with randomly fluctuating fundamentals show overvaluation when \(\hbox {FV}\)s predominantly decline and undervaluation when \(\hbox {FV}\)s are mostly upward-sloping. Finally, we document that (v) bid-ask spreads and volatility of price changes are positively correlated with mispricing across regimes. The main contribution of the paper is to provide clean comparisons between distinct \(\hbox {FV}\) regimes, in particular between markets with increasing \(\hbox {FV}\)s and other regimes.

Hedging price changes in the S&P 500 options and futures contracts: the effect of different measures of implied volatility

We evaluate the performance of delta, delta-gamma and delta-vega hedges on the S&P 500 futures options with a particular focus on importance of daily volatility updating and the use of price-change implied volatility. Our findings indicate that the hedging performance of Black’s model improves with daily updating of implied volatility and fitted price-change implied volatility for both calls and puts. Surprisingly, neither directly estimated implied price-change volatility nor introduction of additional traded option to the hedging portfolio seems to improve the hedging performance.