Relevant bibliographies by topics / Price volatility

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Academic literature on the topic 'Price volatility'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Price volatility"

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Ben Abdallah, Marwa, Maria Fekete Farkas, and Zoltan Lakner. "Analysis of meat price volatility and volatility spillovers in Finland." Agricultural Economics (Zemědělská ekonomika) 66, No. 2 (February 24, 2020): 84–91. http://dx.doi.org/10.17221/158/2019-agricecon.

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Unforeseen important changes in price can present a significant risk in the market. The price fluctuation of agricultural commodities has raised concern for studying the volatility of different agricultural products. A persistent volatility in prices causes continued uncertainty in the market. Higher price volatility is to be mitigated by higher management costs and the higher cost of risk mitigation is often converted into higher producer prices. The aim of this paper is to investigate the price volatility of producer and consumer meat prices and to capture the volatility spillover along the Finnish meat supply chain. The Generalised Autoregressive Conditional Heteroskedasticity – Baba, Engle, Kraft and Kroner (GARCH-BEKK) model is applied to analyse shocks and volatilities of the prices and to estimate whether the price volatility is flowing from the first price level (producer) to the second price level (consumer), using monthly price indices. An asymmetric volatility spillover effect was detected in the poultry meat and a unidirectional, volatility spillover effect, from consumer to producer, is observed for pork prices. The findings of this study could serve as a tool for forecasting meat producer and consumer prices, which could assist the Finnish government with endorsing policy options to alleviate the price volatility impact, to protect both consumers and producers from its negative effects.
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Nugrahapsari, Rizka Amalia, and Idha Widi Arsanti. "Analisis Volatilitas Harga Cabai Keriting di Indonesia dengan Pendekatan ARCH GARCH." Jurnal Agro Ekonomi 36, no. 1 (September 18, 2018): 25. http://dx.doi.org/10.21082/jae.v36n1.2018.25-37.

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<strong>English</strong><br />Chili includes a strategic commodity in Indonesia because of its high price volatility that makes it a major determinant of national inflation dynamics. The government always tries to improve its capability in implementing the chili price stabilization policy. The objective of the study is to assess the volatility of curly chili price volatility in Indonesia by using the ARCH GARCH approach with daily price data of January 2011 to December 2015. The results showed that the right model to calculate chili price volatility is ARCH (1). The price volatility was low and price movement was only influenced by the volatility in the previous day, not by the price variant, so the chili price volatility in the future will be smaller. Low volatility indicates that demand and supply characteristics were predictable. Price changes gradually and predictable. Farmers’ protection policy through import restrictions improves stability of domestic supply. The policy reduces the risk of drastic decline in prices due to imported chili, so the price volatility of chili in the period 2011–2015 was lower than the previous period. However, the seasonal price variation remains. Therefore, the policy should be supported with all season chili availability assurance.<br /><br /><br /><strong>Indonesian</strong><br />Cabai termasuk komoditas strategis di Indonesia karena harganya volatil sehingga menjadi salah satu penentu utama dinamika inflasi nasional. Untuk itu, pemerintah senantiasa berusaha meningkatkan kemampuannya dalam melaksanakan kebijakan stabilisasi harga cabai. Penelitian ini bertujuan untuk mengkaji volatilitas harga cabai keriting di Indonesia dengan pendekatan ARCH GARCH dan data harga harian cabai keriting periode Januari 2011 hingga Desember 2015. Hasil penelitian menunjukkan bahwa model yang tepat untuk menghitung volatilitas harga cabai keriting adalah ARCH(1). Hasil pendugaan model menunjukkan volatilitas harga cabai keriting rendah dan pergerakan harga hanya dipengaruhi oleh volatilitas pada satu hari sebelumnya, tidak dipengaruhi varian harga, sehingga diperkirakan volatilitas harga cabai keriting di masa datang akan semakin kecil. Volatilitas yang rendah menunjukkan karakteristik waktu permintaan dan penawaran cabai keriting dapat diprediksi. Perubahan harga terjadi bertahap dan dapat diperkirakan. Kebijakan perlindungan petani melalui pembatasan impor cabai menyebabkan penyediaan cabai di dalam negeri menjadi lebih stabil. Kebijakan ini mengurangi risiko penurunan harga secara drastis akibat masuknya cabai impor, sehingga volatilitas harga cabai pada periode 2011–2015 lebih rendah dibandingkan periode sebelumnya. Namun, masih terdapat variasi harga musiman. Oleh karena itu, kebijakan ini perlu diperkuat dengan upaya jaminan sediaan cabai sepanjang musim.
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Klepacz, Matthew. "Price Setting and Volatility: Evidence from Oil Price Volatility Shocks." International Finance Discussion Paper 2021, no. 1315 (April 30, 2021): 1–70. http://dx.doi.org/10.17016/ifdp.2021.1316.

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How do changes in aggregate volatility alter the impulse response of output to monetary policy? To analyze this question, I study whether individual prices in Producer Price Index micro data are more likely to change and to move in the same direction when aggregate volatility is high, which would increase aggregate price exibility and reduce the effectiveness of monetary policy. Taking advantage of plausibly exogenous oil price volatility shocks and heterogeneity in oil usage across industries, I find that price changes are more dispersed and less frequent, implying that prices are less likely to move in the same direction when aggregate volatility is high. This contrasts with findings in the literature about idiosyncratic volatility. I use a state-dependent pricing model to interpret my findings. Random menu costs are necessary for the model to match the positive empirical relationship between oil price volatility and price change dispersion. This is the case because random menu costs reduce the extent to which firms with prices far from their optimum all act in a coordinated fashion when volatility increases. The model implies that increases in aggregate volatility do not substantially reduce the ability of monetary policy to stimulate output.
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Ogunmola, Omotoso Oluseye, Abiodun Elijah Obayelu, and Sakiru Oladele Akinbode. "Volatility and Co‑movement: an Analysis of Food Commodity Prices in Nigeria." Agricultura Tropica et Subtropica 50, no. 3 (September 26, 2017): 129–39. http://dx.doi.org/10.1515/ats-2017-0014.

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AbstractThis study explains volatility as a measure and interaction of the possible movement in a particular economic variable. Prices change rapidly in adjustment to market circumstances. Food prices hike experienced overyears has resulted in widespread menace which led to increase in food price volatility. However, volatility and co-movement had generally been lower for the past two decades than for the previous ones. Wide price movements over a short period of time connote high volatility, rendering the producers and consumers vulnerable. Excess volatility can be subjected to sector ineffectiveness and is commodity specific. Producers and processors are mostly concerned about increased price volatility, which greatly exposed them to unpredictable risks and uncertainty associated with price changes. This study examined the volatility and co-movement of food commodity prices in Nigeria using price series data on rice, maize, sorghum, cassava and yam for the period of 1966 to 2013. The data were analysed using Vector Autoregressive Model to forecast food price volatility and to examine the food commodity prices that Granger cause food price volatility in other food commodities. The GARCH regression model is used to estimate the magnitude of volatility which revealed that, food commodity prices exhibit high volatility and there is persistent increase in prices over the period of study. The Nigerian food commodity prices have experienced high fluctuations over the period; therefore, the study recommends proper storage facilities and infrastructure for the food distribution corporations in Nigeria.
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Onour, Ibrahim. "Dynamics of Crude Oil Price Change and Global Food Commodity Prices." Finance & Economics Review 3, no. 1 (April 28, 2021): 38–50. http://dx.doi.org/10.38157/finance-economics-review.v3i1.248.

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Purpose: This study investigates the effect of crude oil price fluctuations (price change as well as volatility) on wheat, sugar, corn, and fertilizers price changes. Methods: The study employs Markov switching dynamic regression, Dynamic Conditional Correlation (DCC), and Generalized Autoregressive Conditional Hetrosekadicity (GARCH) on monthly data covering the period from January 1988 to April 2018. Results: The findings of the research support evidence of two states. State 1, pertains to the low volatility of crude oil price, and state 2 belong to the case of the high volatility of crude oil prices. Our results indicated that at state 1, an increase in crude oil prices leads to a decline in food commodity prices, while in state 2, an increase in crude oil price levels causes an increase in food commodity prices. Results of Dynamic Conditional Correlation (DCC) GARCH estimates indicate the coefficients of oil price levels are significant and positively associated with the conditional volatility of the four commodity prices. Implications: The findings of the research imply that volatility in global food commodity prices is not due to oil price volatility but due to the oil price levels attained at extreme points. Originality: The paper investigates the impact of different volatility levels of crude oil prices on global food commodity prices.
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Gilbert, C. L., and C. W. Morgan. "Food price volatility." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1554 (September 27, 2010): 3023–34. http://dx.doi.org/10.1098/rstb.2010.0139.

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The high food prices experienced over recent years have led to the widespread view that food price volatility has increased. However, volatility has generally been lower over the two most recent decades than previously. Variability over the most recent period has been high but, with the important exception of rice, not out of line with historical experience. There is weak evidence that grains price volatility more generally may be increasing but it is too early to say.
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Sholihah, Fathimah, and Nunung Kusnadi. "Dampak Pengembangan Biofuels terhadap Volatilitas Harga Beberapa Komoditas Pangan di Pasar Dunia." Jurnal Agro Ekonomi 37, no. 2 (April 20, 2020): 157. http://dx.doi.org/10.21082/jae.v37n2.2019.157-170.

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<strong>English</strong><br />Agricultural product based biofuels are the connecting points of the linkages between the global agricultural commodity, energy, and financial markets. Since the global energy markets and financial markets are volatile in nature, rapid expansion of biofuels industry results in increasing volatility of agricultural commodity prices, particularly food prices. The aims of this research is to review price volatility of some food commodities (wheat, corn and soybean) in in the world markets and to analyze the impact of global biofuels development on the price volatility. The price volatility is analyzed using the ARIMA and ARCH GARCH methods. The results show that prices of food commodities have been more volatile since the United States of America imposed the Renewable Fuel Standard Mandate-2 policy in 2007. The Corn and soybean price volatilities are higher than rice and wheat. The stronger are their linkages with biofuels development, the higher are their price volatilities. Increasing food price volatility and level should be considered as challenges and opportunities for accelerating food production growth through technological innovation and land expansion toward the achievement food self-sufficiency such that the national food security system is resilient against global market disturbances.<br /><br /><br /><strong>Indonesian</strong><br />Biofuels berbahan baku hasil pertanian menjadi komoditas penghubung antara pasar komoditas pertanian dengan pasar energi, dan selanjutnya dengan pasar finansial dunia. Oleh karena pasar energi dan pasar finansial dunia rentan gejolak maka pengembangan biofuel secara besar-besaran berdampak pada peningkatan volatilitas harga komoditas pertanian, utamanya komoditas pangan pokok. Penelitian bertujuan untuk meninjau volatilitas harga jagung, gandum, beras dan kedelai di pasar dunia serta untuk menganalisis dampak pengembangan biofuels terhadap volatilitas harga tersebut. Analisis volatilitas harga dilakukan dengan metode ARIMA dan ARCH GARCH. Penelitian menunjukkan bahwa harga komoditas pangan lebih volatil setelah Amerika Serikat menerapkan kebijakan Renewable Fuels Standard Mandate-2 tahun 2007. Volatilitas harga jagung dan kedelai lebih tinggi daripada beras dan gandum. Semakin besar keterkaitan komoditas dengan pengembangan biofuels maka semakin besar pula volatilitas harga komoditas tersebut. Peningkatan volatilitas dan level harga tersebut dapat dipandang sebagai tantangan dan peluang untuk memacu peningkatan produksi pangan melalui pengembangan teknologi dan ekstensifikasi lahan pertanian guna meningkatkan kemandirian pangan sehingga sistem ketahanan pangan nasional lebih tahan menghadapi gejolak pasar global.
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Chalimatusadiah, Chalimatusadiah, Donny Citra Lesmana, and Retno Budiarti. "Penentuan Harga Opsi Dengan Volatilitas Stokastik Menggunakan Metode Monte Carlo." Jambura Journal of Mathematics 3, no. 1 (April 28, 2021): 80–92. http://dx.doi.org/10.34312/jjom.v3i1.10137.

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ABSTRAKHal yang utama dalam perdagangan opsi adalah penentuan harga jual opsi yang optimal. Namun pada kenyataan sebenarnya fluktuasi harga aset yang terjadi di pasar menandakan bahwa volatilitas dari harga aset tidaklah konstan, hal ini menyebabkan investor mengalami kesulitan dalam menentukan harga opsi yang optimal. Artikel ini membahas tentang penentuan harga opsi tipe Eropa yang optimal dengan volatilitas stokastik menggunakan metode Monte Carlo dan pengaruh harga saham awal, harga strike, dan waktu jatuh tempo terhadap harga opsi Eropa. Adapun model volatilitas stokastik yang digunakan dalam penelitian ini adalah model Heston, yang mengasumsikan bahwa proses harga saham (St) mengikuti distribusi log-normal, dan proses volatilitas saham (Vt) mengikuti Proses Cox-Ingersoll-Ross. Hal pertama yang dilakukan dalam penelitian ini adalah mengestimasi parameter model Heston untuk mendapatkan harga saham dengan menggunakan metode ordinary least square dan metode numerik Euler-Maruyama. Langkah kedua adalah melakukan estimasi harga saham untuk mendapatkan harga opsi tipe Eropa menggunakan metode Monte Carlo. Hasil dari penelitian ini menunjukkan bahwa penggunaan metode Monte Carlo dalam penentuan harga opsi tipe Eropa dengan volatilitas stokastik model Heston menghasilkan solusi yang cukup baik karena memiliki nilai error yang kecil dan akan konvergen ke solusi eksaknya dengan semakin banyak simulasi. Selain itu, simulasi Monte Carlo memberikan kesimpulan bahwa parameter harga strike, harga saham awal dan waktu jatuh tempo memiliki pengaruh terhadap harga opsi yang konsisten dengan teori harga opsi. ABSTRACTWhat is important in options trading is determining the optimal selling price. However, in real market conditions, fluctuations in asset prices that occur in the market indicate that the volatility of asset prices is not constant, this causes investors to experience difficulty in determining the optimal option price. This article discusses the optimal determination of the European type option price with stochastic volatility using the Monte Carlo method and the effect of the initial stock price, strike price, and expiration date on European option prices. The stochastic volatility model used in this study is the Heston model, which assumes that the stock price process (S) follows the normal log distribution, and the stock volatility process (V) follows the Ingersoll-Ross Cox Process. The first thing to do in this study is to estimate the parameters of the Heston model to get stock prices using the ordinary least square method and the Euler-Maruyama numerical method. The second step is to estimate the share price to get the European type option price using a Monte Carlo Simulation. This study indicates that using the Monte Carlo method in determining the price of European type options with the Heston model of stochastic volatility produces a fairly good solution because it has a small error value and will converge to the exact solution with more simulations. Also, the Monte Carlo simulation concludes that the parameters of the strike price, initial stock price, and maturity date influence the option price, which is consistent with the option price theory.
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Lingesiya Kengatharan and Jeyan Suganya Dimon Ford. "Dividend Policy and Share Price Volatility: Evidence from Listed Non-Financial Firms in Sri Lanka." International Journal of Business and Society 22, no. 1 (March 24, 2021): 227–39. http://dx.doi.org/10.33736/ijbs.3172.2021.

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The prime objective of this research is to investigate the impact of dividend policy on share price volatility in Colombo Stock Exchange (CSE). A sample of 81 listed non -financial firms from CSE in Sri Lanka is examined using panel data analysis for a five years period from 2013 to 2017. Dividend policy of the firms has been measured by dividend pay-out, dividend yield and dividend per share and which are explanatory variables of the study after controlling for firm size and financial leverage. According to the random effect regression analysis, only 25% of the movements in share prices are explained by the explanatory variables considered in this study. Dividend yield shows significant positive impact on share price volatility whereas dividend per share shows the significant negative impact on share price movements. Firm size illustrates significant negative influence on share price volatility by indicating large size of companies share price volatility is high. But, dividend pay-out and financial leverage are not significantly persuaded on share price volatility in this study. Therefore, it is concluded that dividend yield, dividend per share and firm size have significant impact on price volatility in Sri Lankan context and findings of the study are in line with the dividend relevance theory. Dividend policy can be considered as the protective mechanism to maintain share price volatility in order to enhance the shareholders wealth.
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Anis Erma Wulandari, Harianto Harianto, Bustanul Arifin, and Heny K Suwarsinah. "The Impact of Futures Price Volatility to Spot Market : Case of Coffee in Indonesia." Jurnal Organisasi dan Manajemen 15, no. 1 (March 1, 2019): 1–15. http://dx.doi.org/10.33830/jom.v15i1.5.2019.

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Indonesia is the world 4th largest coffee producer after Brazil, Vietnam and Colombia with export potential and higher national consumption concluded in 2017 while the coffee production was relatively stagnant. This was led the producer to not only the production risk but also the price risk which then emphasize the importance of futures markets existence as price risk management. This study is performed to examine the impact of futures price volatility to spot market using ARCH-GARCH toward primary data of coffee futures and spot prices of 1172 trading days starting from January 2014 to June 2018. The ARCH-GARCH analysis result indicates that futures price volatility and monetary variables are impacting the volatility of spot price. Arabica spot price volatility is impacted by volatility of Arabica futures price, inflation and exchange rate while Robusta spot price is impacted by Robusta futures price volatility and exchange rate. This is confirming that futures market plays dominant role in spot price discovery. Local futures and spot prices are also found to be significantly influenced by volatility of offshore futures prices which indicates that emerging country futures market is actually influenced by offshore futures market which the price itself used as price reference.
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Dissertations / Theses on the topic "Price volatility"

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Acree, E. Bryan. "Volatility spillovers in international equity markets." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/30969.

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Sharma, Namit. "Forecasting Oil Price Volatility." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36815.

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This study compares different methods of forecasting price volatility in the crude oil futures market using daily data for the period November 1986 through March 1997. It compares the forward-looking implied volatility measure with two backward-looking time-series measures based on past returns - a simple historical volatility estimator and a set of estimators based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) class of models.

Tests for the relative information content of implied volatilities vis-à-vis GARCH time series models are conducted within-sample by estimating nested conditional variance equations with returns information and implied volatilities as explanatory variables. Likelihood ratio tests indicate that both implied volatilities and past returns contribute volatility information. The study also checks for and confirms that the conditional Generalized Error Distribution (GED) better describes fat-tailed returns in the crude oil market as compared to the conditional normal distribution.

Out-of-sample forecasts of volatility using the GARCH GED model, implied volatility, and historical volatility are compared with realized volatility over two-week and four-week horizons to determine forecast accuracy. Forecasts are also evaluated for predictive power by regressing realized volatility on the forecasts. GARCH forecasts, though superior to historical volatility, do not perform as well as implied volatility over the two-week horizon. In the four-week case, historical volatility outperforms both of the other measures. Tests of relative information content show that for both forecast horizons, a combination of implied volatility and historical volatility leaves little information to be added by the GARCH model.
Master of Arts

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Planting, Ronald James. "Petroleum futures trading and price volatility." Thesis, Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/91138.

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This study investigates the effects of futures trading on petroleum price variability. Though a number of critics from various quarters claim futures markets have made petroleum prices more volatile, economic reasoning does not support this viewpoint. A review of theoretical studies and empirical investigations of other commodities shows general support for the hypothesis that futures markets do not destabilize prices and may, in fact, add to price stability. In this study, regression analysis is used to explain the price variability of heating oil and gasoline in terms of factors that may affect this variability, including the existence of futures markets. Though the empirical tests performed are biased towards finding destabilizing effects of futures markets, no statistically significant increase in price volatility is found, and in the case of gasoline, indications of stabilizing effects are found. Thus, neither the results of other studies of futures markets nor examination of petroleum futures trading support the critics' contention that futures trading has destabilized petroleum prices.
M.A.
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Yang, Yue, and Viorica Gonta. "The relationship between volatility of price multiples and volatility of stock prices : A study of the Swedish market from 2003 to 2012." Thesis, Umeå universitet, Handelshögskolan vid Umeå universitet (USBE), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-72769.

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The purpose of our study was to examine the relationship between the volatility of price multiples and the volatility of stock prices in the Swedish market from 2003 to 2012. Our focus was on the price-to-earnings ratio and the price-to-book ratio. Some previous studies showed a link between the price multiples and the volatility of stock prices, this made us question whether there should be a link between the volatility of the price multiples and the volatility of the stock prices. The importance of this subject is accentuated by the financial crisis, as we provide investors with information regarding the movements of price multiples and stock prices. Moreover, we test if the volatility of the price multiples can be used to create a prediction model for the volatility of stock prices. Also we fill the gap in the previous researches as there is no previous literature about this topic. We conducted a quantitative research using statistical tests, such as the correlation test and the linear regression test. For our data sample we chose the Sweden Datastream index. We first calculated the volatility using the GARCH model and then continued with our statistical tests. The results of our tests showed that there is a relationship between the volatility of the price multiples and the volatility of the stock prices in the Swedish market in the past ten years. Our findings show that the correlation coefficients vary across industries and over time in both strength and direction. The second part of our tests is concerned with the linear regression tests, mainly calculating the coefficient of determination. Our results show that the volatility of the price multiples do explain changes in the volatility of stock prices. Thus, the volatility of the P/E ratio and the volatility of the P/B ratio can be used in creating a prediction model for the volatility of stock prices. Nevertheless, we also find that this model is best suited when the economic situation is unstable (i.e. crisis, bad economic outlook) as both the correlation coefficient and the coefficient of determination had the highest values in the last five years, with the peak in 2008.
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Nasir, Samia. "Volatility- An investigation of the relationship between price- and yield volatility." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-51054.

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This report investigates the relationship between the yield volatility and the price volatility in the Swedish market. The method given in our report can be used to analyze any market with appropriate data set. We have used a time-series data of interest rate yield curves from Swedish government bonds. The curves are bootstrapped from the bills and bonds. The linear interpolation on these curves results in the nodes i.e. 1Y, 2Y,..., 10Y. We also need prices for instruments. A good choice is to use the synthetic government bonds namely SE GVB 2Y, SE GVB 5Y, and SE GVB 10Y. They are issued every day with maturity 2, 5, and 10 years. We also use the time-series of these bonds. These bonds have a yearly coupon of 6%. We can get zero-coupon values of these bonds by stripping their coupons using the interest rate yield curves. We have time-series data of zero-coupon prices with maturities 2, 5, and 10 years and time-series data of interest rates with the same tenors. We can use our data to calculate their respective volatilities to investigate how they are related to each other.
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Stråle, Johansson Nathalie, and Malin Tjernström. "The Price Volatility of Bitcoin : A search for the drivers affecting the price volatility of this digital currency." Thesis, Umeå universitet, Företagsekonomi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-98397.

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Created in 2009, the digital currency of bitcoin is a relatively new phenomenon. During this short period of time, it has however displayed a strong development of both price and trade volume. This has led to increased media attention, but also regulators and researchers have developed an interest. At this moment, the amount of available research is however limited. With a focus on the price volatility of bitcoin and an aim of finding drivers of this volatility, this study is taking a unique position. The research has its basis in the philosophical position of positivism and objectivism. This has shaped the research question as well as the construction of the study. The result is a describing and explaining research with a deductive research approach, a quantitative research method and an archival research strategy. This has in turn stimulated an extensive literature review and information search. Areas of discussion are microstructure theory, the efficient market hypothesis, behavioural finance and informational structures. Due to the limited amount of previous bitcoin research within the area of price volatility, the study has drawn extensively on research performed on more classical assets such as stocks. Nevertheless, when available, bitcoin research has been used as a foundation/reference and an inspiration. Reviews of academic literature and economic theories, as well as public news helped to identify the variables for the empirical study. These variables are; information demand, trade volume, world market index, trend and six specified events, occurring during the chosen sample period and included in the study as dummy variables. The variables are all analysed and included in a GARCH (1,1) model, modified following a similar research by Vlastakis & Markellos (2012) on stocks. This GARCH (1,1) model is then fitted to the bitcoin volatility registered for the sample period and is able thereby able to generate data of if and how the variables affect the bitcoin volatility. The test result suggests that five of the ten variables are significant on a 5 %-level. More specifically it suggests that information demand is a significant variable with a positive influence on the bitcoin volatility, something that corresponds to the literature on information demand and price volatility. This also relates to the events found significant, as they generated bitcoin related information. The significant events of the Cypriot crisis and the failure of the bitcoin exchange MtGox are thus specific examples of how information affects price volatility. Another significant variable is trade volume, which also displays a positive influence on the volatility. The last significant variable turned out to be a constructed positive trend, suggesting that increasing acceptance of bitcoin decreases its volatility.
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Santana, Verônica de Fátima. "IFRS adoption, stock price synchronicity and volatility." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/12/12136/tde-30032015-143815/.

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This research aimed to investigate whether and how the adoption of the International Financial Reporting Standards (IFRS) has affected the synchronicity of stock prices in the Brazilian capital market and how this was reflected in the behavior of idiosyncratic and systematic risk. In order to do so, it was first conducted a regression analysis associating the Transition (2008 and 2009) and the Post-Adoption (from 2010) period with a measure of stock price synchronicity, controlling for structural aspects that affect the functioning of stock markets as a whole and for aspects of individual firms that affect the process of incorporating information into their stock prices and their incentives to report transparent financial statements. Then, it was built series of volatility decomposed into two components, market-wide (capturing the systematic risk) and firm-specific (capturing the idiosyncratic risk), according to the methodology of Campbell et al. (2001), and performed an analysis based on tests for identifying trends on the series. The study predicted that if IFRS was able to increase the amount of firm-specific information incorporated into stock prices, it could (i) reduce synchronicity (J. Kim & Shi, 2012), and idiosyncratic volatility would have become more intense relatively to systematic volatility; or (ii) it could increase synchronicity (Beuselinck et al., 2010; Dasgupta et al., 2010), and idiosyncratic volatility would, then, have become less intense. The results confirmed that stock price synchronicity has decreased from the Post-Adoption period, in line with the view of J. Kim & Shi (2012), that the reducing effect can be more intense for less developed countries, which tend to be more synchronous (Morck et al, 2000) and because the improvement in the informational environment acts as a substitute to the weak institutional environment. These results indicate that stock prices became more informative (Durnev, Morck, & Yeung, 2004), making the market less obscure (K. Li et al., 2003) and better able to efficiently allocate resources (Wurgler, 2000; Habib, 2008). However, although a visual analysis of the volatility series suggests a slightly upward trend for the firm-level series, the statistical tests were not able to identify any significant trend, so, only the first part of the hypothesis could be confirmed. Nevertheless, despite of this limitation and the possible caveats with the models that were used, this research provides evidence that IFRS adoption brought positive changes to the functioning of the Brazilian capital market.
Esta pesquisa buscou investigar se, e de que forma, a adoção dos International Financial Reporting Standards (IFRS) afetou a sincronicidade dos preços das ações no mercado de capitais brasileiro e como isso se refletiu no comportamento dos riscos idiossincrático e sistemático. Para tanto, foi feita uma análise de regressão associando o período de Transição (2008 e 2009) e o de Pós-Adoção (a partir de 2010) com uma medida de sincronicidade dos preços das ações, controlando por aspectos estruturais que afetam o funcionamento do mercado de capitais e por aspectos individuais das firmas que afetam a incorporação de informações em seus preços e seus incentivos para reportar demonstrações financeiras transparentes. Em seguida, foram construídas séries de volatilidade decompostas em dois componentes: o mercado em geral (capturando o risco sistemático) e específica da firma (capturando o risco idiossincrático), segundo a metodologia de Campbell et al. (2001), e foi feita uma análise baseada em testes para identificar tendências nessas séries. O estudo previa que se a adoção das IFRS foi capaz de aumentar a quantidade de informação específica das firmas incorporada nos preços das ações, então ela poderia (i) diminuir a sincronicidade (J. Kim & Shi, 2012), e a volatilidade idiossincrática teria se tornado mais intensa em relação à volatilidade sistemática; ou (ii) ela poderia aumentar a sincronicidade (Beuselinck et al., 2010; Dasgupta et al., 2010), e a volatilidade idiossincrática teria, então, se tornado menos intensa. Os resultados confirmaram que a sincronicidade diminuiu a partir do período de Pós-Adoção, em consonância com a visão de J. Kim & Shi (2012), de que o efeito redutor pode ser mais intenso para países menos desenvolvidos, que tendem a ter mercados mais sincronizados (Morck et al, 2000) e porque a melhora no ambiente informacional funciona como uma substituta para o ambiente institucional fraco. Esse resultado indica que os preços das ações se tornaram mais informativos (Durnev, Morck, & Yeung, 2004), tornando o mercado menos obscuro (K. Li et al., 2003) e melhor capaz de alocar recursos eficientemente (Wurgler, 2000; Habib, 2008). No entanto, apesar de uma análise visual das séries de volatilidade mostrar uma leve tendência crescente para a série do nível da firma, os testes estatísticos não puderam identificar qualquer tendência significativa, então, somente a primeira parte da hipótese pôde ser confirmada. Contudo, apesar dessa limitação e das possíveis ressalvas quanto aos modelos que foram usados, esta pesquisa fornece evidências de que a adoção das IFRS trouxe mudanças positivas para o funcionamento do mercado de capitais brasileiro.
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Moabelo, Julith Tsebisi. "Analysing potato price volatility in South Africa." Thesis, University of Limpopo, 2019. http://hdl.handle.net/10386/3049.

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Thesis ( M.Sc.(Agricultural Economics)) --University of Limpopo, 2019.
Potato is perceived as an excellent crop in the fight against hunger and poverty. The recent high potato price in South Africa has pushed the vegetable out of reach of the poorest of the poor. The study attempts to analyse potato price volatility in South Africa and furthermore assess how various factors were responsible for the recent potato price volatility. Quarterly data for potato price, number of hectares planted, rainfall and temperature levels from 2006q1 to 2017q4 was collected from various sources and were used for analysis. The total observation of 48. The volatility in the series was determined by performing ARCH/GARCH model. GARCH model indicates an evidence of GARCH effect in the series, meaning that GARCH model influences potato price volatility in South Africa. The Johansen cointegration used both trace and eigenvalue to test the existence of a long run relationship between potato price and various variables. The cointegration results were positive indicating that there exists long run relationship amongst variables. The study further used Johansen cointegration as well as standard error to determine the number of cointegrating variables in the long run. The results indicated that the number of hectares planted and rainfall level have significant relationship with potato price. Wald tests was used to check whether the past values of number of hectares planted and rainfall level influenced the current value of potato price. The Walt test results concluded that there is no evidence of short run causality running from number of hectares planted and rainfall level to potato price. In the study, ECM model was used to forecast the potato price fluctuation in South Africa. The study recommends that farmers need to engage in contract market so as to minimize the risk of potato price volatility. The Department of Agriculture should forecast agricultural commodities price volatility and make information accessible to the farmers so that they are able to adopt strategies that will assist them to overcome crisis.
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Ndiaye, Moctar. "Maize price volatility in Burkina Faso : Measurement, Causes and Consequences." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTD042.

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La volatilité des prix alimentaires est devenue un sujet de préoccupation constante dans les pays en développement suite à la flambée des prix des produits alimentaires en 2007/08 et 2010/11. Cette thèse s’intéresse à la caractérisation de la volatilité des prix au Burkina Faso. La volatilité des prix est définie comme la part imprévisible des variations de prix. Les objectifs de cette thèse sont en particulier i) d’évaluer les caractéristiques de la volatilité des prix du maïs au Burkina Faso, ii) d’analyser ses déterminants et iii) ses impacts sur le comportement des producteurs. Pour répondre à ces questions complémentaires, nous avons combiné des données originales et riches de prix céréaliers sur plusieurs marchés et des données sur l’activité agricole de près de 2000 producteurs sur l’ensemble du territoire Burkinabé. Plusieurs résultats émergent dans cette thèse. Premièrement, ces données ont permis d’isoler le secteur clé du maïs pour ensuite présenter de manière détaillée les données sur les prix du maïs et sur l’activité agricole des ménages utilisés dans la suite de la thèse (chapitre 1). Deuxièmement, l’analyse des séries de prix du maïs sur chaque marché propose le processus ARCH comme modèle de séries chronologiques qui explique le mieux les caractéristiques de la volatilité des prix sur la majorité des marchés. Sur ces marchés les baisses et les hausses de prix ont une contribution similaire sur la volatilité des prix, et seuls les chocs de court terme l’affectent. Les autres marchés sont caractérisés par une persistance de la volatilité avec un effet différencié des variations de prix qui s’expliquent par les caractéristiques géographiques (chapitre 2). Troisièmement, l'analyse des séries de prix en panel révèle que la volatilité des prix du maïs est élevée sur les marchés les plus enclavés (chapitre 3). Quatrièmement, l’analyse des séries de prix du maïs combinés aux données sur l’activité agricole des ménages indiquent qu’une hausse des prix du maïs accroît l'utilisation des engrais chimiques. Toutefois, les variations de prix imprévisibles diminuent le niveau d'utilisation de ces engrais ; tandis que les variations des prix prévisibles n’ont aucun effet significatif sur leur utilisation (chapitre 4). La principale originalité de cette thèse réside dans le traitement des questions relatives à la volatilité des prix à l’échelle des marchés locaux et à un niveau microéconomique avec des données de ménage, alors que cette problématique est généralement perçue sous un angle macroéconomique à l’échelle internationale
Food price volatility is an ongoing concern in developing countries since the food price spikes in 2007/08 and 2010/11. This dissertation focuses on the patterns of food price volatility in Burkina Faso. Price volatility is defined as the unpredictable component of price variations. The aim of this dissertation is to contribute to a better understanding of three complementary issues i) the nature of maize price volatility in Burkina Faso, ii) its determinants and iii) its impacts on agricultural producers’ behavior. We combine an original database of grain prices on 28 local markets in the last 15 years and a panel database of almost 2,000 farm households’ production choices throughout the. Our results can be summarized as follows. First, these data allowed isolating the key sector of maize and then presenting detailed data on maize price series and the agricultural activity of households used in the remainder of this thesis (chapter 1). Second, the analysis of maize price series in each market suggests that ARCH model as the dominant time-series model to describe price volatility patterns in most markets in Burkina Faso. In these markets, price drops and peaks have a similar contribution to price volatility, and only recent episodes of price variations increase current volatility. Other markets are characterized by long term volatility episodes with a differential effect of price variations due to the geographical position (Chapter 2).Third, the analysis with panel method of maize price series shows that maize price volatility is greater in remote markets (Chapter 3). Fourth, by combining price series on local cereal markets and a panel data set on farm households’ production choices, we find that higher maize prices increase the quantity of chemical fertilizer use. However, unpredictable maize price variations decrease the level of fertilizer use; while predictable maize prices have no significant effect on fertilizer use (Chapter 4). The novelty of this thesis lies in the analysis of price volatility on local markets and at a micro level with household data, whereas this issue is usually perceived at the macroeconomic scale
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Venter, Rudolf Gerrit. "Pricing options under stochastic volatility." Diss., Pretoria : [s.n.], 2003. http://upetd.up.ac.za/thesis/available/etd09052005-120952.

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Books on the topic "Price volatility"

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Hardouvelis, Gikas A. Price volatility and futures margins. London: Centre for Economic Policy Research, 1995.

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Diba, Behzad. Bubbles and stock price volatility. [Philadelphia, Pa.]: Federal Reserve Bank of Philadelphia, 1989.

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Worrell, DeLisle. Price volatility and financial instability. [Washington, D.C.]: International Monetary Fund, Monetary and Exchange Affairs Department and IMF Institute, 2001.

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Martinovich, Petro. Minerals price increases and volatility. Hauppauge, N.Y: Nova Science Publishers, 2009.

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Petro, Martinovich, ed. Minerals price increases and volatility. Hauppauge, N.Y: Nova Science Publishers, 2009.

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Martinovich, Petro. Minerals price increases and volatility. Hauppauge, N.Y: Nova Science Publishers, 2009.

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Fund, International Monetary, ed. Discretionary trading and asset price volatility. Washington, D.C: International Monetary Fund, 1995.

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Roesser, Randy. Natural gas price volatility: Staff report. [Sacramento, Calif.]: California Energy Commission, 2009.

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Adrian, Tobias. Inference, arbitrage, and asset price volatility. [New York, N.Y.]: Federal Reserve Bank of New York, 2004.

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Bernanke, Ben. Monetary policy and asset price volatility. Cambridge, MA: National Bureau of Economic Research, 2000.

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Book chapters on the topic "Price volatility"

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Ozenbas, Deniz, Michael S. Pagano, and Robert A. Schwartz. "Accentuated Intraday Stock Price Volatility." In Volatility, 111–26. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1474-3_8.

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Gilbert, Christopher L., and C. Wyn Morgan. "Food Price Volatility." In Methods to Analyse Agricultural Commodity Price Volatility, 45–61. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-7634-5_4.

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Taylor, Stephen J. "Stock Price Volatility." In The New Palgrave Dictionary of Economics, 1–4. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2162-1.

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Taylor, Stephen J. "Stock Price Volatility." In The New Palgrave Dictionary of Economics, 13126–29. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2162.

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Swinnen, Johan. "Food Price Volatility." In The Political Economy of Agricultural and Food Policies, 137–49. New York: Palgrave Macmillan US, 2018. http://dx.doi.org/10.1057/978-1-137-50102-8_8.

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Bhattacharyya, Subhes C. "Impact of Price Volatility." In Energy Economics, 443–67. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7468-4_15.

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Da Costa Lewis, Nigel. "Modeling Energy Price Volatility." In Energy Risk Modeling, 196–212. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230523784_12.

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Alexander, David R., and Emmanuel E. Haven. "Demand Heterogeneity and Price Volatility." In Mathematical Finance, 40–48. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8291-0_3.

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Diba, Behzad T. "Bubbles and Stock-Price Volatility." In The Stock Market: Bubbles, Volatility, and Chaos, 9–29. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-015-7881-3_2.

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Hoffman, Linwood A. "Using Futures Prices to Forecast US Corn Prices: Model Performance with Increased Price Volatility." In Methods to Analyse Agricultural Commodity Price Volatility, 107–32. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-7634-5_7.

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Conference papers on the topic "Price volatility"

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Algan, Neşe, Erhan İşcan, Duygu Serin Oktay, and Duygu Kara. "Impact of Energy Price Volatility on Macroeconomic Performance." In International Conference on Eurasian Economies. Eurasian Economists Association, 2017. http://dx.doi.org/10.36880/c08.01892.

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Last two decades witnessed increasingly volatile international markets with the many financial crises. Concurrently, volatility in energy prices and energy markets cause various adverse impacts on both national and world economies. Especially this volatility affected emerging markets and increased the fragility of the emerging economies. Because of the adverse impacts of this volatility, understanding the price behavior and impact of volatility of energy prices on economy became crucial for every economic agent in the economy including policy makers in the governments, consumers, and producers. The relationship between energy prices and macroeconomic performance has been studied widely as a consequence its long term macroeconomic impacts to world economies. Differently, the aim of this study is analyzing the effect of energy price volatility on macroeconomic indicators of Turkey. For that purpose, we employed a GARCH model to investigate effect of energy price volatility on macroeconomic performance for Turkey from 2002 to 2016. We use various energy prices and macroeconomic indicators data for the period from January 2002 to December 2016, obtained from the IFS and CBRT-EDDS. By applying GARCH methodology to various energy prices and macroeconomic indicators, we contribute to the understanding of price volatility in energy markets, and suggest policies that would be of use to policy makers in the governments, consumers, and producers.
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Staugaitis, Algirdas Justinas. "Financial speculation impact on agricultural commodity price volatility: TGARCH approach." In 21st International Scientific Conference "Economic Science for Rural Development 2020". Latvia University of Life Sciences and Technologies. Faculty of Economics and Social Development, 2020. http://dx.doi.org/10.22616/esrd.2020.53.014.

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Motivated by agricultural commodity price fluctuations and spikes in the last decade, we investigate whether financial speculation destabilizes the price of agricultural commodities. The aim of this research is to assess the impact of financial speculation on agricultural commodity price volatility. In our study we use weekly returns on wheat, soybean and corn futures from Chicago Mercantile of Exchange. To measure this impact, we apply autoregressive conditional heteroskedasticity (ARCH) technique. We also propose a model with seasonal dummy variables to measure if financial speculation impact on price volatility differs among seasons. The results of our research indicate that financial speculation as an exogenous factor has either no effect or reduces the volatility of the underlying futures prices. Therefore, we conclude that the increase of non-commercial market participants does not make the agricultural commodity prices more volatile or this link is at least questionable.
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Huang, Zhong-hua, Ci-fang Wu, and Xue-jun Du. "Analyzing housing price volatility in Shanghai." In 2008 International Conference on Management Science and Engineering (ICMSE). IEEE, 2008. http://dx.doi.org/10.1109/icmse.2008.4669131.

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Li Xie, Hua Zheng, and Guo-ying Fan. "Price volatility analysis by Grey disaster theory." In 2008 3rd IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2008. http://dx.doi.org/10.1109/iciea.2008.4582963.

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Astaneh, Mostafa F., and Zhe Chen. "Price volatility in wind dominant electricity markets." In IEEE EUROCON 2013. IEEE, 2013. http://dx.doi.org/10.1109/eurocon.2013.6625070.

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Chai, Jian, Ju'e Guo, Shou-yang Wang, and Hong-quan Li. "Oil Price Volatility and Change Point Analysis." In 2009 International Joint Conference on Computational Sciences and Optimization, CSO. IEEE, 2009. http://dx.doi.org/10.1109/cso.2009.382.

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Kaizoji, Taisei. "A synergetic approach to speculative price volatility." In the 1999 ACM symposium. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/298151.298191.

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Chakraborty, Shantanu, Milos Cvetkovic, Kyri Baker, Remco Verzijlbergh, and Zofia Lukszo. "Consumer Hedging Against Price Volatility Under Uncertainty." In 2019 IEEE Milan PowerTech. IEEE, 2019. http://dx.doi.org/10.1109/ptc.2019.8810922.

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Shorokhov, S. "ON DEEP LEARNING FOR OPTION PRICING IN LOCAL VOLATILITY MODELS." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.17.84.001.

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We study neural network approximation of the solution to boundary value problem for Black-ScholesMerton partial differential equation for a European call option price, when model volatility is afunction of underlying asset price and time (local volatility model). Strike-price and expiry day of theoption are assumed to be fixed. An approximation to option price in local volatility model is obtainedvia deep learning with deep Galerkin method (DGM), making use of the neural network of specialarchitecture and stochastic gradient descent on a sequence of random time and underlying price points.Architecture of the neural network and the algorithm of its training for option pricing in local volatilitymodels are described in detail. Computational experiment with DGM neural network is performed toevaluate the quality of neural network approximation for hyperbolic sine local volatility model withknown exact closed form option price. The quality of the neural network approximation is estimatedwith mean absolute error, mean squared error and coefficient of determination. The computationalexperiment demonstrates that DGM neural network approximation converges to a European calloption price of the local volatility model with acceptable accuracy.
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Haugom, Erik, Sjur Westgaard, Per Bjarte Solibakke, and Gudbrand Lien. "Modelling day ahead Nord Pool forward price volatility: Realized volatility versus GARCH models." In 2010 7th International Conference on the European Energy Market (EEM 2010). IEEE, 2010. http://dx.doi.org/10.1109/eem.2010.5558687.

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Reports on the topic "Price volatility"

1

Traore, Fousseini, and Insa Diop. Measuring food price volatility. Washington, DC: International Food Policy Research Institute, 2021. http://dx.doi.org/10.2499/p15738coll2.134399.

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Díaz-Bonilla, Eugenio, and Juan Francisco Ron. Food Security, Price Volatility and Trade:. Geneva, Switzerland: International Centre for Trade and Sustainable Development, 2010. http://dx.doi.org/10.7215/ag_ip_20101129.

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West, Kenneth. Dividend Innovations and Stock Price Volatility. Cambridge, MA: National Bureau of Economic Research, February 1986. http://dx.doi.org/10.3386/w1833.

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Bernanke, Ben, and Mark Gertler. Monetary Policy and Asset Price Volatility. Cambridge, MA: National Bureau of Economic Research, February 2000. http://dx.doi.org/10.3386/w7559.

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Hummels, David, and Georg Schaur. Hedging Price Volatility Using Fast Transport. Cambridge, MA: National Bureau of Economic Research, July 2009. http://dx.doi.org/10.3386/w15154.

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Hale, Galina, Assaf Razin, and Hui Tong. Institutional Weakness and Stock Price Volatility. Cambridge, MA: National Bureau of Economic Research, March 2006. http://dx.doi.org/10.3386/w12127.

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Hale, Galina, Assaf Razin, and Hui Tong. Credit Constraints and Stock Price Volatility. Cambridge, MA: National Bureau of Economic Research, May 2007. http://dx.doi.org/10.3386/w13089.

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Flood, Robert, and Robert Hodrick. Asset Price Volatility, Bubbles, and Process Switching. Cambridge, MA: National Bureau of Economic Research, March 1986. http://dx.doi.org/10.3386/w1867.

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Burger, John, Alessandro Rebucci, Francis Warnock, and Veronica Cacdac Warnock. External Capital Structures and Oil Price Volatility. Cambridge, MA: National Bureau of Economic Research, June 2010. http://dx.doi.org/10.3386/w16052.

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Banks, James, Richard Blundell, Zoé Oldfield, and James Smith. House Price Volatility and the Housing Ladder. Cambridge, MA: National Bureau of Economic Research, June 2015. http://dx.doi.org/10.3386/w21255.

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