Autoregressive Conditional Heteroscedasticity Model Research Articles

Price dynamics in Indian wholesale wheat markets: Insights and implications

Wheat is an integral part of food as well as nutrition security holding a significant share in the consumption basket. Hence, any extreme deviations in the staple food price will affect the prospects of the economy. In the context, we analysed the price dynamics and price volatility in Indian wholesale wheat markets using compound annual growth rate, instability indices, and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model. The study sourced monthly data (July 2002 to June 2019) from the AGMARKNET portal for 15 states. Research findings indicated that the wholesale price, as well as divergence between maximum and minimum price, was highest in the case of Kerala implying the non-production region. Monthly price indices (highest as well as lowest in Haryana respectively during December: 109.90 and July: 91.48) exhibited a clear-cut seasonality attributed to the wheat arrivals post-crop harvest. Barring Maharashtra, the growth and variation in seasonal price indices were positive and relatively low respectively across markets. Corroborating the price trend, Kerala exhibited the highest volatility as evident by the GARCH model estimates, followed by Gujarat, Haryana and Chhattisgarh. Inter alia, volatility in wheat prices is directly linked to regional production. The study advocates for effective dissemination of market information such as price forecast, demand and supply to counter the price volatility as well as decision making for profitable agri-business.

The effect of COVID-19 pandemic on global stock markets: Return, volatility, and bad state probability dynamics.

This study investigates the impact of COVID‐19 pandemic on stock returns, conditional volatility, conditional skewness and bad state probability. This study utilizes an asymmetric exponential generalized autoregressive conditional heteroscedasticity model to capture the asymmetric effect of positive and negative shocks (news) on conditional volatility. Using a sample consisting of international stock market indices in Brazil, China, Italy, India, Germany, Russia, Spain, United Kingdom, and United States, over the period from January 1, 2013 to December 31, 2020, we find unprecedented increases in conditional volatilities and bad state probabilities across all the markets. However, this impact is not symmetric across markets. Furthermore, we find that the negative affect of deaths is more pronounced, compared to the positive impact of recovered cases.

Housing Price Volatility: What's the Difference between Investment and Owner‐Occupancy?*

This research examines housing price volatility and its determinants in Auckland, New Zealand. It differs from the existing literature by dividing residential sales into four groups: leveraged investment (LI), leveraged owner‐occupancy (LO), unleveraged investment (UI) and unleveraged owner‐occupancy (UO). The housing price volatility of these groups is estimated using autoregressive conditional heteroscedasticity (ARCH) models. This study builds four vector autoregression (VAR) models to conduct Granger causality tests and impulse response analyses. It is found that the four volatility series respond differently to shocks. A decrease in housing prices is a significant determinant across all four groups of transactions.

EFFECT OF VOLATILITY IN THE NAIRA - DOLLAR EXCHANGE RATE ON THE VOLUME OF IMPORTS TO, AND EXPORTS FROM NIGERIA

We analyzed the effect of volatility in the Naira-Dollar exchange rate on the volume of imports to and exports from Nigeria between 1990 and 2019. Data for all variables, except volatility, were sourced from the Central Bank of Nigeria (CBN), the National Bureau of Statistics (NBS), and the International Financial Statistics. The volatility of the naira exchange rate was calculated from the model. The study employed both the autoregressive distributed lagged (ARDL) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) models to test for the short and long-run relationships between changes in the Naira-Dollar exchange rate and the volume of imports and exports. Volatility in the Naira-Dollar exchange rate was found to be related to the volume of imports to and exports from Nigeria in the long run with no short-run effects because of the pass-through effect to domestic inflation. The study recommends that government should sustain efforts at promoting exports and reducing imports to improve the trade balance. The study brings into perspective another way of looking at the long-run relationship between the Naira-Dollar exchange rate and Nigeria's trade balance.Keywords: Foreign exchange rate, foreign exchange market, Imports, Exports, The balance of trade, Nigeria.JEL Classifications: F14, F31, 024.DOI: https://doi.org/10.32479/ijefi.11799

Beef Cattle Price and Production Patterns in Relation to Drought in New Mexico

Understanding the fluctuations in monthly and annual cattle prices plays a key role in supporting the sustainability of New Mexico’s (NM’s), United States (US), beef cattle industry under variable environmental conditions. The goal of this study was to provide an improved understanding of NM’s beef cattle production systems in terms of prices and production patterns and related drought impacts. The main objectives were to evaluate monthly and annual prices patterns for heifers and steers (cattle) and calves, the relationships between annual cattle prices and inventory and drought, and the effects of drought on ranch net return. Drought events were assessed using the Self-Calibrated Palmer Drought Severity Index (SC-PDSI). The generalized autoregressive conditional heteroscedasticity models and their exponential version were used to investigate the effects of drought and cattle supply on cattle prices, and the effects of drought on ranch net return. Spectral analysis and timeseries decomposition were used to identify the cycles of the annual price and numbers of cattle and calf. Coherence analysis was used to examine the relationships between inventory of cattle classes and drought. The results indicated that prices of cattle and calf usually drop in October through January and peak in April. The inventory of replacement heifers and steers were negatively related to cattle prices, while the inventory of calves was positively related to calf prices. Cattle and calf prices showed negative linear relationships with droughts. Annual cattle and calf prices showed 6- and 10-year cycles, while their inventory showed 6- and 8- year cycles, respectively. Our finding suggested that a rancher can still earn some net return when drought falls within the “Abnormally Dry” category of the US Drought Monitor. However, a rancher with a large herd or ranch size can endure drought more than a rancher with a medium herd or ranch size and reach the breakeven point. Specifically, the net return ($/head) is expected to increase (or decrease) by $62.29, $60.51, and $64.07 per head if the SC-PDSI increase (or decrease) by one unit in all large and medium ranch sizes, respectively. The effects of drought on ranch net return that we identified need further improvements using additional data. Due to NM’s location and the diversity of its rangeland, understanding the response of cattle prices to drought and beef cattle supply based on these findings can be used to help NM’s ranchers and those in other similar regions make informed ranch management decisions. These findings can also support the development of improved understanding of beef cattle production systems regionally.

Integer-valued autoregressive processes with prespecified marginal and innovation distributions: a novel perspective

Integer-valued autoregressive (INAR) processes are generally defined by specifying the thinning operator and either the innovations or the marginal distributions. The major limitations of such processes include difficulties in deriving the marginal properties and justifying the choice of the thinning operator. To overcome these drawbacks, we propose a novel approach for building an INAR model that offers the flexibility to prespecify both marginal and innovation distributions. Thus, the thinning operator is no longer subjectively selected but is rather a direct consequence of the marginal and innovation distributions specified by the modeler. Novel INAR processes are introduced following this perspective; these processes include a model with geometric marginal and innovation distributions (Geo-INAR) and models with bounded innovations. We explore the Geo-INAR model, which is a natural alternative to the classical Poisson INAR model. The Geo-INAR process has interesting stochastic properties, such as MA() representation, time reversibility, and closed forms for the -order transition probabilities, which enables a natural framework to perform coherent forecasting. To demonstrate the real-world application of the Geo-INAR model, we analyze a count time series of criminal records in sex offenses using the proposed methodology and compare it with existing INAR and integer-valued generalized autoregressive conditional heteroscedastic models.

Study on Air Pollution Behavior of VOCs with Photochemical Monitoring Stations Using EGARCH Model in Southern Taiwan

This study adopted the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model to examine the 10 ozone precursors of the highest concentrations among the 54 that were assessed over a number of years at the four photochemical assessment monitoring stations (PAMSs) in the Kaohsiung–Pingtung Area in Taiwan. First, the 10 ozone precursors, which were all volatile organic compounds (VOCs), were analyzed using the factor analyses in multiple statistical analyses that had the most significant impact on the area’s ozone formation: mobile pollution factor, which included 1,2,4-Trimethylbenzene (C9H12), toluene (C7H8), and Isopropyl benzene (C9H12). Then, taking into consideration that the number sequences might be affected by standardized residuals, this study applied the vector autoregressive moving average-EGARCH (VARMA-EGARCH) model to analyze the correlation between the three VOCs under different polluting activities. The VARMA-EGARCH model in this research included dummy variables representing changing points of variance structures in the variance formula to predict the conditional variance. This process proved able to effectively estimate the relevant coefficients of the three VOCs’ dynamic conditions that changed with time. The model also helped to prevent errors from occurring when estimating the conditional variance. Based on the testing results, this study determined the VARMA(2,1)-EGARCH(1,0) as the most suitable model for exploring the correlation between the three VOCs and meteorological phenomena, as well as the interplay between them in regard to interaction and formation. With the most representative of the three, toluene (TU), as the dependent variable and 1,2,4-Trimethylbenzene (TB) and Isopropyl benzene (IB) as the independent variables, this study found it impossible to calculate the TU concentration with TB and IB concentrations in the same period; estimations of TB and IB concentrations with a period of lag time were required because TU was mainly contributed by automobiles and motorcycles in Kaohsiung. TB and IB resulted from other stationary pollution sources in the region besides cars and motorbikes. When TU was evenly distributed and stayed longer in the atmosphere, the TB and IB concentrations were lower, so distribution conditions and concentrations could not be used to effectively estimate the concentration of toluene. This study had to wait until the next period, or when stationary pollution sources started producing TB and IB of higher concentrations during the daytime, in order to estimate the TU concentrations in a better photochemical situation.

COVID-19 and financial market response in China: Micro evidence and possible mechanisms.

This paper uses event study based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model to study the impact of the COVID-19 outbreak on China’s financial market. It finds that the pandemic had an overall significant and negative impact on the stock prices of firms listed on SSE, SZSE and ChiNext. However, such impact appeared to be heterogeneous across industries, affecting listed firms in industries such as pharmaceutical and telecommunications positively, but those in services industries such as accommodation, catering, and commercial services negatively. Apparently, a crisis for some had been an opportunity for others. In addition, this paper seeks to understand the micro mechanism behind the heterogeneity of pandemic shock from the perspective of firms’ financial position. It finds that listed firms with higher debt level were hit harder, whereas those with more net cash flow had displayed higher resilience against the blow of the pandemic. However, the opposite pattern is found among those listed on ChiNext and in industries severely devastated by the pandemic. These findings have policy implications in terms of preventing systemic financial risks and facilitating recovery during pandemic-induced economic downturns. It also helps investor adjust investment strategies, hedge against risks, and secure gains when the market conditions in general are unfavorable.

Measuring multi‐volatility states of financial markets based on multifractal clustering model

AbstractMeasuring multi‐volatility states is an important component of financial risk management. In this paper, taking 17 years' high‐frequency data of the Shanghai Stock Exchange Composite Index (SSEC) as an example, we set out to propose a novel model of multifractal clustering model (MCM), combining multifractal algorithm with k‐mean clustering algorithm, to measure the multi‐volatility states of financial markets. The empirical results present that the financial markets are multifractal and the multifractal parameter Sα measured by multifractal algorithm correlates well with the performance of financial markets. Meanwhile, the multi‐volatility states recognized by k‐mean clustering algorithm based on Sα have obvious statistical significance. More importantly, the experiment results show that by the loss functions and Diebold–Mariano (DM) test, generalized autoregressive conditional heteroscedasticity (GARCH) model with the multi‐volatility state measured by MCM is superior to others measured by Markov switching (MRS) model and hidden Markov model (HMM) as well as that without the multi‐volatility states, which indicates that MCM has best performance of measuring the multi‐volatility states of financial markets. The robustness of MCM is also evaluated by Shenzhen Securities Component Index (SZSE) instance set, SSEC instance sets with different time intervals, and different volatility models. The results still prove that MCM has strong robustness on measuring the multi‐volatility states of financial markets.

Central bank's communication and markets' reactions: Polish evidence

PurposeThis study contributes to the pool of knowledge about the impact of monetary policy communication of central banks on financial instruments' prices and assets' value in emerging markets.Design/methodology/approachEmpirical analysis is executed using the National Bank of Poland (NBP) announcements about its monetary policy covering the data from the broad financial market in its three main segments: stock market, foreign exchange market and bonds market. The reactions are measured relative to the changes in the NBP announcements and also with respect to investors' expectations. Autoregressive conditional heteroscedasticity (ARCH) models with dummy variables are used as the main methodological tool.FindingsBonds market and foreign exchange market are the most sensitive market segments, while interest rate and money supply are the most influential types of announcements. The changes of the revealed new macroeconomic figures had more impact on assets' prices movements than the deviations from their expectations. Moreover, greater diversity of the Monetary Policy Council (MPC) members' opinions on the voted motions, captured in the MPC voting reports, is associated with more cases of statistically significant NBP communication events.Practical implicationsThe findings have direct relevance for fund managers, portfolio analysts, investors and also for financial market regulators.Originality/valueThe results provide novel evidence about how the emerging financial market responds to monetary policy announcements. They help understand the nature of the impact of public information on financial assets' valuation and on movements of their prices, analysed comprehensively in three market segments, in the emerging market environment.

Determination of VaR on BBRI Stocks and BMRI Stocks Using the ARIMA-GARCH Model

Stocks are investment instruments that are much in demand by investors as a basis in financial storage. Return and risk are the most important things in investing. Return is a complete summary of investment and the return series is easier to handle than the price series. The movement of risk of loss is obtained from stock investments with profits. One way to calculate risk is value-at-risk. The movement of stocks is used to form a time series so that the calculation of risk can use time series. The purpose of this study was to find out the Value-at-Risk value of BBRI and BMRI stock using the ARIMA-GARCH model. The data used in this study was the daily closing price for 3 years. The time series method used is the Autoregressive Integrated Moving Average (ARIMA)-Generalized Autoregressive Conditional Heteroscedastic (GARCH) model. The stage of analysis is to determine the prediction of stock price movements using the ARIMA model used for the mean model and the GARCH model is used for volatility models. The average value and variants obtained from the model are used to calculate value-at-risk in BBRI and BMRI stock. The results obtained are the ARIMA(3,0,3)-GARCH(1,1) and ARIMA(2,0,2)-GARCH(1,1) model so with a significance level of 5% obtained Value-at-Risk of 0.04058 to BBRI stock and 0.10167 to BMRI stock.

Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks

Stocks are common investments that are in great demand by investors. Stocks are also an investment instrument that provides returns but tends to be riskier. The return time series is easier to handle than the price time series. In investment activities, there are the most important components, namely volatility and risk. All financial evaluations require accurate volatility predictions. Volatility is identical to the conditional standard deviation of stock price returns. The most frequently used risk calculation is Value-at-Risk (VaR). Mathematical models can be used to predict future stock prices, the model that will be used is the Glosten Jagannathan Runkle-generalized autoregressive conditional heteroscedastic (GJR-GARCH) model. The purpose of this study was to determine the value of the risk obtained by using the time series model. GJR-GARCH is a development of GARCH by including the leverage effect. The effect of leverage is related to the concept of asymmetry. Asymmetry generally arises because of the difference between price changes and value volatility. The method used in this study is a literature and experimental study through secondary data simulations in the form of daily data from BCA shares and BNI shares. Data processing by looking at the heteroscedasticity of the data, then continued by using the GARCH model and seeing whether there is an asymmetry in the data. If there is an asymmetric effect on the processed data, then it is continued by using the GJR-GARCH model. The results obtained on the two stocks can be explained that the analyzed stock has a stock return volatility value for the leverage effect because the GJR-GARCH coefficient value is > 0. So, the risk value obtained by using VaR measurements on BCA stocks is 0.047247 and on BNI stocks. is 0.037355. Therefore, the ARMA-GJR-GARCH model is good for determining the value of stock risk using VaR.

Price Discovery and Volatility Spillover for Indian Energy Futures Market in the Pre- and Post-Crisis Periods

The present paper examined the price discovery and volatility spillovers in pre and post-crisis (global financial crisis and European sovereign debt crisis) periods of spot and futures energy markets in India from January 1, 2007 – December 31, 2018, with the help of closing price series listed on the Multi Commodity Exchange Limited (MCX) for both spot and futures crude oil and natural gas markets. The data were examined using Johansen cointegration test, vector error correction (VEC) model, autoregressive distributed lag (ARDL) model, and Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroscedasticity (GJR-GARCH) model to measure the price discovery and volatility spillovers. For price discovery, most of the sample cases had a long-run equilibrium relationship between their spot and futures prices, and the futures (spot) market led the spot (futures) market in the long-run in most sample periods (post-ESDC period). In case of volatility spillover, most of the results concluded the dominance of the futures market over the spot market except crude oil in the post-ESDC period. All these factors made the futures market more efficient and cost-competitive in terms of price discovery. So, it can be concluded that the market participants may depend on the futures market’s price changes for their investment and trading decisions. The results of during and post-crisis periods may be helpful for the current investors for modification of their optimum portfolio. Investors and policy makers may draw meaningful conclusions and become prepared for the next crisis period.

Measuring parametric and semiparametric downside risks of selected agricultural commodities

In this paper, we evaluate the downside risk of six major agricultural commodities – corn, wheat, soybeans, soybean meal, soybean oil and oats. For research purposes, we first use an optimal generalised autoregressive conditional heteroscedasticity (GARCH) model to create residuals, which we later use for measuring downside risks via parametric and semiparametric approaches. Modified value-at-risk (mVaR) and modified conditional value-at-risk (mCVaR) provide more accurate downside risk results than do ordinary value-at-risk (VaR) and conditional value-at-risk (CVaR). We report that soybean oil has the lowest mVaR and mCVaR because it has two very favourable features – skewness around zero and low kurtosis. The second-best commodity is soybeans. The worst-performing downside risk results are in wheat and oats, primarily because of their very high kurtosis values. On the basis of the results, we propose to investors and various agents involved with these agricultural assets that they reduce the risk of loss by combining these assets with other financial or commodity assets that have low risk.

A Chaos Analysis of the Dry Bulk Shipping Market

Finding low-dimensional chaos is a relevant issue as it could allow short-term reliable forecasting. However, the existence of chaos in shipping freight rates remains an open and outstanding matter as previous research used methodology that can produce misleading results. Using daily data, this paper aims to unveil the nonlinear dynamics of the Baltic Dry Index that has been proposed as a measure of the shipping rates for certain raw materials. We tested for the existence of nonlinearity and low-dimensional chaos. We have also examined the chaotic dynamics throughout three sub-sampling periods, which have been determined by the volatility pattern of the series. For this purpose, from a comprehensive view we apply several metric and topological techniques, including the most suitable methods for noisy time series analysis. The proposed methodology considers the characteristics of chaotic time series, such as nonlinearity, determinism, sensitivity to initial conditions, fractal dimension and recurrence. Although there is strong evidence of a nonlinear structure, a chaotic and, therefore, deterministic behavior cannot be assumed during the whole or the three periods considered. Our findings indicate that the generalized autoregressive conditional heteroscedastic (GARCH) model and exponential GARCH (EGARCH) model explain a significant part of the nonlinear structure that is found in the dry bulk shipping freight market.

Exchange Rate Volatility and Import Trade Flow Evidence From India-U.S. at Industry Level

The centre interest of the study is to explore the impact of exchange rate volatility on the India-U.S. trade flow of Import on 6 industries spanned from September 2002 to June 2019. We investigate the relationship at disaggregate level by industry-wise data with monthly frequency. We employ exponential generalized autoregressive conditional heteroscedasticity (E-GARCH) model to gauge volatility and thereafter ARDL bound testing approach to unveil the short and long-run association of real exchange rate volatility and import. The empirical analysis implies the existence of both short-run and long-run effect in 5 importing industries except manufactured (engineering) goods. While real exchange volatility appears to have statistically significant effect in short-run, but also estimated short-run lasts onto long-run effect in only three industries. The results confirm the information of import in time-series analysis. The finding of the study helps to undertake the view of invariability and considering the industry before policy making.

Pricing the Volatility Risk Premium with a Discrete Stochastic Volatility Model

Investors’ decisions on capital markets depend on their anticipation and preferences about risk, and volatility is one of the most common measures of risk. This paper proposes a method of estimating the market price of volatility risk by incorporating both conditional heteroscedasticity and nonlinear effects in market returns, while accounting for asymmetric shocks. We develop a model that allows dynamic risk premiums for the underlying asset and for the volatility of the asset under the physical measure. Specifically, a nonlinear in mean time series model combining the asymmetric autoregressive conditional heteroscedastic model with leverage (NGARCH) is adapted for modeling return dynamics. The local risk-neutral valuation relationship is used to model investors’ preferences of volatility risk. The transition probabilities governing the evolution of the price of the underlying asset are adjusted for investors’ attitude towards risk, presenting the asset returns as a function of the risk premium. Numerical studies on asset return data show the significance of market shocks and levels of asymmetry in pricing the volatility risk. Estimated premiums could be used in option pricing models, turning options markets into volatility trading markets, and in measuring reactions to market shocks.

Forecasting Covid-19: SARMA-ARCH approach.

Forecasting the number of Covid-19 cases is a crucial tool in public health policy. In this paper, we construct seasonal autoregressive moving average and autoregressive conditional heteroscedasticity models to forecast the spread of the infection in the UAE. While most of the existing literature is dedicated to forecasting the total number of infections, we endeavor to forecast the number of new infections which is a significantly more challenging task due to the greater volatility. Our models are based on a careful analysis of correlation plots and residual analysis. In addition, we employ highly accurate population data that leads to more reliable outcomes. The results reveal a high degree of accuracy of the proposed forecasting methods. The constructed models can be used by health officials to better anticipate and plan for new cases of Covid-19.

Do Green Bonds Act as a Hedge or a Safe Haven against Economic Policy Uncertainty? Evidence from the USA and China

Economic policy uncertainty and particularly COVID-19 has stimulated the need to investigate alternative avenues for policy risk management. In this context, this study examines the dynamic association among economic policy uncertainty, green bonds, clean energy stocks, and global rare earth elements. A dynamic conditional correlation-multivariate generalized autoregressive conditional heteroscedasticity (DCC-MGARCH) model was used to gauge the time-varying co-movements among these indices. The analysis finds that green bonds act more as a hedge than a safe haven against economic policy uncertainty (EPU). In the case of diversification, green bonds work as diversifiers with clean energy stocks and rare earth elements during COVID-19 and in the whole sample period. Additionally, clean energy stocks and rare earth elements show safe haven properties against EPUs. This study contributes to the hedging and safe haven literature with some new insight considering the role of green bonds and clean energy stocks. Additionally, the outcomes of the research contribute toward the literature of portfolio diversification theory. These findings pave the way for not only US investors to hedge long-term economic policy risk by investing in green bonds, but also for China and the UK, as these financial assets (green bonds, clean energy stocks, and rare earth metals) and EPU are long-term financial and economic variables.

Co-Movements between Eu Ets and the Energy Markets: A Var-Dcc-Garch Approach

This paper analyzes the co-movements of prices of fossil fuels, energy stock markets and EU allowances. This analysis is conducted in order to identify the spillover effect of volatility and correlation among these financial markets, and to provide a scientific basis that shows the interest of incorporating sustainable assets in the design of minimum risk strategies of investment. To achieve this goal, we have used a Vector Autoregressive-Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (VAR-DCC-GARCH) model that also incorporates a stock index of industrial companies as a leading indicator of the level of economic activity. In addition, the paper conducts an impulse response analysis to determine how unexpected shocks to prices are propagated along time, and, in particular, how they affect prices of the others, both in mean, variance and correlation. Therefore, the results of this one- and two-dimensional analysis allow for the study of short and long run dynamics of the relationship among those prices, thus, providing greater meaning and information for investors, which has implications for building their portfolios. The analyzed period was from January 2010 to February 2021, so that the data include half of phase II, full phase III and the onset of phase IV of the EU ETS, as well as the COVID-19 outbreak in the European context. We also analyzed whether the EUA price impulses the demand of clean energy stocks, which has important implications for the objective of triggering the investment in clean energy. Our results show the transmission mechanism of all of those prices, which are relevant not only for investors but also for policymakers to construct an early-warning system, revealing the most important transmission channels. Moreover, from an investment viewpoint, we observe a decline in dirty energies and a rise in the clean energy market, which might be an indication of the progress towards the energy transition to renewables sources within a circular economy perspective. Therefore, this shows that the EU ETS is achieving its goals, and that clean energy companies, aligned with their role towards socially responsible initiatives, are also gaining acceptance in terms of investments, which would be beneficial for the environment.