GARCH Research Articles

Currency, Property and Stock Market Volatility : An Empirical Analysis of China

This passage continues the earlier investigation regarding the correlation among the currency market, the property market, and the equity market. Additionally, it examines the transfer of fluctuation impact among the three markets. The application of the VAR-MGARCH-BEKK time series model and the GARCH model aims to assess volatility correlations between property prices, currency supply, and the stock index. Furthermore, it seeks to test the proposed hypothesis concerning the impact of their respective undulations on the growth rate of the stock index. The study found that, in line with the previous research conclusions, there is no volatility effect in the three markets under the long-term trend; that is, there is no volatility spillover effect between the three markets, but there is a nonlinear ARCH effect, and the GARCH regression results show that the joint volatility of the stock index price and the other two prices significantly affects the volatility of the stock market itself.

Forecasting cryptocurrency returns using classical statistical and deep learning techniques

The emergence of cryptocurrencies has generated enthusiasm and concern in the modern global economy. However, their high volatility, erratic price fluctuations, and tendency to exhibit price bubbles have made investors cautious about investing in them. Consequently, it is essential to develop methods and models to forecast cryptocurrency returns to benefit investors, traders, and the scientific community. Despite the considerable volume of research on Bitcoin price forecasting, other cryptocurrencies have received little attention in academic literature. Additionally, the current body of literature on predicting cryptocurrency prices or returns emphasizes the use of in-sample methodologies. However, this method is susceptible to overfitting. To address these gaps in the literature, this study employs autoregressive moving average (ARMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential generalized autoregressive conditional heteroskedasticity (EGARCH), and long short-term memory (LSTM) deep learning neural networks to forecast returns for the ten most actively traded digital currencies: Bitcoin, Ethereum, Ripple, Chainlink, Litecoin, Cardano, Ethereum Classic, Bitcoin Cash, Tether, and Binance Coin. To assess the accuracy of the two models, this study utilizes an out-of-sample method with data gathered sequentially from November 9, 2017, to September 18, 2022. The results indicate that all models exhibit high accuracy, as evidenced by their low root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE) values. Meanwhile, the hybrid EGARCH-LSTM or GARCH-LSTM models demonstrate slightly better accuracy compared with the other models. The findings are valuable for investors, traders, and researchers involved in cryptocurrency forecasting.

Is there a time-varying nexus between stock market liquidity and informational efficiency? – A cross-regional evidence

PurposeThis study aims to measure the degree of volatility comovement between stock market liquidity and informational efficiency across Asia, Europe, North-South America, Africa, and the Pacific Ocean over three decades. In particular, the authors analyze the extent of the time-varying nexus between different aspects of stock market liquidity and multifractal scaling properties of the stock return series across various regions and diversified market conditions. This study further investigates several factors altering the degree of dynamic conditional correlations (DCCs) between the efficiency and liquidity of the domestic stock markets.Design/methodology/approachThe study measures five aspects of stock market liquidity – tightness, depth, breadth, immediacy, and adjusted immediacy. The authors evaluate the multifractal scaling properties of the stock return series to measure the level of stock market efficiency across the regions and diversified market conditions. The study uses the dynamic conditional correlation-multivariate generalized autoregressive conditional heteroscedasticity framework to quantify the degree of volatility comovement between liquidity and efficiency over the period.FindingsThe study finds the presence of stronger volatility comovement between inefficiency and illiquidity due to the price impact characteristics of the stock markets irrespective of different regions and diversified market conditions. The extent of time-variation increased following the shock periods, indicating the significant role of the financial crisis in increasing the volatility comovement between inefficiency and illiquidity. The highest degree of time-varying correlation is observed in the developed stock markets of Northwestern and Northern Europe compared to the regional and emerging counterparts. On the other hand, weak DCCs are observed in the emerging stock markets of Europe.Originality/valueThe output of the present study assists investors in identifying diversification opportunities across the regions. Additionally, the study has significant implications for market regulators, aiding in predicting future troughs and peaks. The prediction, in turn, helps formulate capital market development plans during dynamic economic situations.

THE HOLIDAY EFFECTS ON AMUSEMENT AND THEME PARK COMPANIES’ STOCK PRICES

This study offers further evidence of the holiday effect on excess stock returns and volatility, and additional insights into its impact on the amusement park and attractions industry in the United States. The generalized auto-regressive conditional heteroscedasticity (GARCH) model and dummy variables are adopted to investigate pre- and post-holiday excess returns and volatility. Empirical results suggest the following: (1) both excess returns and excess volatility happen more frequently in pre-holidays than post-holidays; (2) the frequency of excess volatility is higher than excess returns for the holiday effect; and (3) nearly all significant excess volatilities are negative. We, therefore, conclude that the holiday effect exists in publicly traded amusement and theme parks.

Bayesian Learning in an Affine GARCH Model with Application to Portfolio Optimization

This paper develops a methodology to accommodate uncertainty in a GARCH model with the goal of improving portfolio decisions via Bayesian learning. Given the abundant evidence of uncertainty in estimating expected returns, we focus our analyses on the single parameter driving expected returns. After deriving an Uncertainty-Implied GARCH (UI-GARCH) model, we investigate how learning about uncertainty affects investments in a dynamic portfolio optimization problem. We consider an investor with constant relative risk aversion (CRRA) utility who wants to maximize her expected utility from terminal wealth under an Affine GARCH(1,1) model. The corresponding stock evolution, and therefore, the wealth process, is treated as a Bayesian information model that learns about the expected return with each period. We explore the one- and two-period cases, demonstrating a significant impact of uncertainty on optimal allocation and wealth-equivalent losses, particularly in the case of a small sample size or large standard errors in the parameter estimation. These analyses are conducted under well-documented parametric choices. The methodology can be adapted to other GARCH models and applications beyond portfolio optimization.

Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index

The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method assumes a homogeneous residual variance, but data with high volatility can cause violations of this assumption. Hence, it is interesting to compare the forecasting accuracy of symmetric and asymmetric Autoregressive Conditional Heteroskedasticity (ARCH) models in various data conditions. The research aimed to compare the accuracy of the symmetric ARCH/ Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and asymmetric TGARCH models in forecasting weekly Jakarta Composite Index (JCI) data on January 1st, 2018, to April 24th, 2023, by involving the influence of COVID-19 as a covariate variable and applying several validation scenario models to training and testing data. Based on the best-selected model, forecasting was carried out from May 1st, 2023, to July 3rd, 2023. The data used were weekly JCI opening data from January 1st, 2018, to April 24th, 2023, with the COVID-19 period as a covariate variable. The analysis results show that symmetric and asymmetric methods can handle violations of the heteroscedasticity assumption in the ARIMAX model. The best model produced based on four data validation scenarios is the asymmetric ARIMAX(3,1,3)-TGARCH(1,2) model with an average MAPE value of 3.158%. In this model, the COVID-19 variable significantly influences the JCI movement. Forecasting is done with forecasting results that are stable with confidence intervals that widen in each period.

South African Real Estate Investment Trusts Prefer Tuesdays

This study examines the day-of-the-week effect on the returns of different classifications of South African REITs. Ordinary least squares regression (OLS), generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) (2,1), and Kruskal–Wallis (KW) tests were performed on data obtained from the IRESS Expert database from 2013 to 2021. We found statistical differences in the day-of-the-week effects for SAREITs; the best day to invest in office REITs is Friday, for diversified REITs Thursday, and for industrial REITs Friday. Generally, Wednesday was found to be the least profitable day to invest in all REIT classifications because it had the least average daily return. Tuesdays were the most profitable days for all REIT classifications, with the highest average daily return. REITs traded the most on Fridays, while REITs traded the least on Mondays. Returns were the most volatile on Monday, while volume was the least volatile on Thursday. The KW test revealed a statistically significant difference between the median returns across days of the week. Based on the above, profitability is expressed on Tuesdays in South African REITs. By recognizing the day-of-the-week effect, investors can buy and sell South African REITs more effectively. This study, apart from being the first in the context of South African REITs, provides updated evidence of the contested calendar anomaly issues.

Decoding Stocks Patterns Using LSTM

Decoding stocks is extensively utilized in the financial sector by numerous organizations. It is volatile in nature, so it’s tough to predict the prices of stock. Numerous methodologies exist for tackling this task, including logistic regression, support vector machines (SVM), autoregressive conditional heteroskedasticity (ARCH) models, recurrent neural network (RNN), convolutional neural networks (CNN), backpropagation, Naïve Bayes, among others. Among these, Long Short-Term Memory (LSTM) stands out as particularly adept at handling time series data. The primary aim is to discern prevailing market trends and achieve accurate stock price forecasts. Leveraging LSTM and RNN , we strive for error free stock price predictions, with promising results.

Risk Modeling and Connectedness Across Global and Industrial US Fintech Stock Market: Evidence from the COVID‑19 Crisis

The main purpose of this paper is to test the performance of GARCH models in estimating and forecasting VaR (value at risk) of the US Fintech stock market from July 20, 2016, to December 31, 2021. In addition, this study examines the impact of COVID‑19 on the risk spillover between the adequate VaR series of the US global KFTX index and the five Fintech industries. Specifically, we compare different VaR estimates (862 in‑sample daily returns) and predictions (550 out‑of‑sample daily returns) of several GARCH model specifications under a normal and Student‑t distribution with 1% and 5% significance. The Backtesting results indicate that I‑GARCH with Student‑t distribution is a good model for estimating and forecasting VaR of the US Fintech stock market before and during COVID-19. Moreover, the total connectedness results suggest that global and each Fintech industry increases significantly under turbulent market conditions. Given these considerations, this paper provides policymakers and regulators with a better understanding of risk in the Fintech industry without inhibiting innovation.

РОЗРАХУНОК АРКОВОЇ ДОРОЖНЬОЇ СПОРУДИ З ГОФРОВАНОГО МЕТАЛУ З УРАХУВАННЯМ ФОРМИ ГОФРУ

Introduction. The issue of calculation of an arched road structure made of corrugated metal on the basis of numerical modeling using the finite element method (FEM) using the actual cross-sectional parameters of metal corrugated structures (MCS) is considered. Problem Statement. In past years, for the calculations and tests of road constructions from MCS, a simple rectangular cross-section, equivalent in terms of bending stiffness to a corrugated profile, was considered. This was adopted to simplify calculations and research. Such an approach is also used in modern norms for the design of buildings with MCS. But this is not quite a correct simplification. Purpose. To show the possibility to avoid unnecessary convention and simplification of the calculation scheme and to obtain more correct and more informative results of the calculation of structures with MCS. Research methods and results. The purpose and tasks are realized by calculation-theoretical analysis and numerical modeling according to FEM using a specific example of calculating a road structure (small bridge) from the MCS. The calculation model is made according to the actual shape of the corrugations, without switching to the conventional simple rectangular cross-section of the arch elements, which are equivalent in terms of bending stiffness. The main design condition for strength in this work is the condition that the design load does not exceed the bearing capacity of the arch, as a component of the «building-soil» system.The bearing capacity of the structure in this work is considered as the sum of the arch's own bearing capacity (outside the soil) and the addition, which is provided by the elastic support of the embankment soil in response to the movement of the arch toward the soil. To determine the addition of bearing capacity due to soil resistance, the arch model is adopted as a «mechanism» with full hinges in the places of the greatest stresses and deformations of the arch. The geometric stability of the "mechanism" is ensured by additional rods that simulate the soil support by moving the arch. The forces in these rods must be corresponding to the equivalent resistance of the soil. The bearing capacity supplement is defined as the load on the arch, at which the forces in the additional rods are equal to the equivalent soil resistance of the embankment by the permissible movement of the arch toward the soil.

A study of the dynamic relationship between the RMB exchange rate, foreign exchange reserves and monetary policy

In today's globalized economic environment, the relationship between China's RMB exchange rate, foreign exchange reserves and monetary policy has become more and more complex, and is crucial to the stability and sustainable development of the economy. The interactions among these three have a direct impact on China's macroeconomic situation, so it has become a very urgent task to study this relationship in depth. This study combines theoretical and empirical research to deeply analyze the dynamic relationship among these three, aiming to provide practical guidance for the formulation of China's monetary policy.This paper selects the time series data of RMB exchange rate, foreign exchange reserves and money supply from 2003 to 2023 to study the relationship between RMB exchange rate, foreign exchange reserves and monetary policy. This paper will deeply explore the impact of the government's market intervention on the RMB exchange rate under different exchange rate regimes, especially under the fixed exchange rate regime and the floating exchange rate regime. This paper will delve into the transmission mechanism of monetary policy, especially the flexibility and adaptability of monetary policy under the volatility of global financial markets and changes in economic cycles. In terms of empirical research, this paper will use the VAR model and the GARCH model for time series analysis to verify the applicability of the theoretical mechanism in practical situations and provide lessons for the formulation of future monetary policy.

The impact of the COVID 19 pandemic on stock market volatility: evidence from a selection of developed and emerging stock markets

This study examines the impact of the COVID 19 pandemic on the stock markets of China, India, Pakistan, the UK and the US using Generalised Autoregressive Conditional Heteroscedasticity (GARCH) and Threshold GARCH models with COVID 19 as an exogenous dummy variable in the variance equation. The sample period of 2016–2021 is divided into two sub-periods: the pre-COVID 19 period and the COVID 19 period. The results of the study indicate that there was persistent volatility in these markets and that this volatility increased as a result of the pandemic. In addition, the Threshold GARCH results indicate that the asymmetric term was significant in all markets indicating that bad news, such as the pandemic, had a stronger impact on the conditional variance of the returns as compared to good news. In addition, the results further confirm that the US market had no significant impact on the volatility of the Chinese market during the pandemic. The results have important implications for (1) international investors regarding portfolio management and investment risk minimisation in situations like the COVID 19 pandemic; and (2) policy-makers in terms of how they respond to any future pandemic.

Forecasting tail risk of skewed financial returns having exponential‐polynomial tails

AbstractAggregated long and short trading risk positions of speculative assets over time are likely to be unequal. This may be because of irrational decisions of traders and investors as well as catastrophic events that lead to pronounce or salient market crashes. Returns of such assets are therefore more likely to have one polynomial tail and one exponential tail. The generalized hyperbolic (GH) skewed Student‐t distribution is known to handle such situations quite well. In this paper, we use generalized autoregressive conditional heteroscedasticity (GARCH) models to empirically show the superiority of the GH skewed Student‐t distribution in forecasting the extreme tail risks of cryptocurrency returns in the presence of substantial skewness in comparison with some competing distributions. Furthermore, we show the practical significance of the GH skewed Student‐t distribution‐based risk forecasts in computing daily capital requirements. Evidence from the study suggests that the GH skewed Student‐t distribution model tends to be superior in forecasting volatility and expected shortfall (ES) but not value‐at‐risk. In addition, the distribution yields higher value‐at‐risk (VaR) exceptions but surprisingly avoids the red zone of the Basel II accord penalty zones and produces lower but optimal daily capital requirements. Therefore, in the presence of substantially skewed returns having exponential‐polynomial tails, we recommend the use of the GH skewed Student‐t distribution for parametric GARCH models in forecasting extreme tail risk.

Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model

AbstractMost existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high‐frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5‐min high‐frequency data are used to construct realized volatility which is decomposed into continuous components and jump components with positive and negative directions. Then, this information is combined with the long short‐term memory model for the realized volatility prediction. The empirical analysis demonstrates that negative jumps resulting from negative news have a more significant impact on market volatility than positive jumps. Additionally, the long short‐term memory model, which incorporates positive and negative jump volatility, outperforms traditional econometric and machine learning models in predicting out‐of‐sample volatility. Furthermore, applying the prediction results to value at risk yields a better measurement effect than the generalized autoregressive conditional heteroskedasticity model.

Simmering tensions on the Russia–Ukraine border and natural gas futures prices: identifying the impact using new hybrid GARCH

Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models.

US macroeconomic determinants of Bitcoin

This study aims to determine the impact of macroeconomic variables on bitcoin prices in the United States. Bitcoin is one of the cryptocurrencies that has the highest price and the most users in the United States in recent years. This study uses monthly data on inflation, interest rates, USD/EUR rates, gold prices, and bitcoin prices. To achieve the objectives of this study, Dynamic Conditional Correlation (DCC) and Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) were used. The results showed that there is a negative and significant relationship between the variables of inflation, interest rates, and USD/EUR rates affecting the price of Bitcoin in that period. Conversely, there is a positive and significant relationship between the price of gold and the price of Bitcoin in the United States during that period. An in-depth understanding of how macroeconomic factors such as inflation, interest rates and the USD/EUR rates affect Bitcoin price is key to making smart investment decisions in an increasingly complex crypto market. The findings of this analysis confirm that the significant relationship between macroeconomic variables and Bitcoin price provides deeper insights for investors to anticipate market movements and design adaptive investment strategies.

Unveiling Interconnectedness and Volatility Transmission: A Novel GARCH Analysis of Leading Global Cryptocurrencies

The cryptocurrency industry has grown erratically and at an alarming pace during its brief life. It has received massive attention from the practitioners, academicians, and especially media since Bitcoin was introduced in 2009. The current paper investigates the volatility dynamics of five major cryptocurrencies, namely Bitcoin, Ethereum, Litecoin, DASH and Ripple’s XRP. It discloses the impact of volatility in the prices of other cryptocurrencies on the Bitcoin’s price volatility. The time series of all the cryptocurrencies prices for the period 2016 to 2022 are considered and preliminary analysis highlights that there exists conditional volatility in all the cryptocurrencies which allows us to use the family of GARCH model. The research findings reveal the significant effect of asymmetric past shocks on the current volatility of Bitcoins. Further, the volatility of remaining virtual currencies are also significantly affecting the Bitcoin’s current volatility. Therefore, the present study unveils the absence of diversification benefits in the market of virtual assets as significant effect of price volatility of other digital coins has been found on the price volatility of Bitcoins. The practical implication of our study is that the findings offer new information for investors and portfolio managers, who are attracted to invest in or hedging strategies in cryptocurrencies.

Threshold Network GARCH Model

Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its variations have been widely adopted in the study of financial volatilities, while the extension of GARCH‐type models to high‐dimensional data is always difficult because of over‐parameterization and computational complexity. In this article, we propose a multi‐variate GARCH‐type model that can simplify the parameterization by utilizing the network structure that can be appropriately specified for certain types of high‐dimensional data. The asymmetry in the dynamics of volatilities is also considered as our model adopts a threshold structure. To enable our model to handle data with extremely high dimension, we investigate the near‐epoch dependence (NED) of our model, and the asymptotic properties of our quasi‐maximum‐likelihood‐estimator (QMLE) are derived from the limit theorems for NED random fields. Simulations are conducted to test our theoretical results. At last we fit our model to log‐returns of four groups of stocks and the results indicate that bad news is not necessarily more influential on volatility if the network effects are considered.

Effect of fence tray matching care on excess adhesive and bracket placement accuracy for orthodontic bonding: an in vitro study

ObjectiveThis study aimed to evaluate the effect of fence tray matching care (FTMC) in bracket bonding by measuring excess adhesive, as well as linear and angular deviations, and by comparing it with the half-wrapped tray (HWT).Materials and methodsAn intraoral scanner was used to acquire data on the maxillary dental arch of a patient with periodontitis.Furthermore, 20 maxillary dental arch models were 3D printed. Using 3Shape, PlastyCAD software, and 3D printing technology, 10 FTMC (method I) and HWT (method II) were obtained. By preoperative preparation, intraoperative coordination, and postoperative measurement, the brackets were transferred from the trays to the 3D-printed maxillary dental arch models. Additionally, the bracket’s excess adhesive as well as linear and angular deviations were measured, and the differences between the two methods were analyzed.ResultsExcess adhesive was observed in both methods, with FTMC showing less adhesive (P< 0.001), with a statistical difference. Furthermore, HWT’s vertical, tip and torque, which was significantly greater than FTMC (P< 0.05), with no statistical difference among other respects. The study data of incisors, canines, and premolars, showed that the premolars had more adhesive residue and were more likely to have linear and angular deviations.ConclusionsThe FTMC had higher bracket bonding effect in comparison to HWT, and the adhesive residue, linear and angular deviations are smaller. The fence tray offers an intuitive view of the precise bonding of the bracket, and can remove excess adhesive to prevent white spot lesions via care, providing a different bonding method for clinical applications.

Time‐varying risk preference and equity risk premium forecasting: The role of the disposition effect

AbstractThis study examines whether the disposition effect can explain time‐varying risk preference and predict the equity risk premium. To do so, we propose an augmented general autoregressive conditional heteroskedasticity (GARCH)‐in‐Mean model unraveling the complex relationship between unrealized gains/losses, realized returns, and the equity risk premium. In our model, the risk aversion coefficient varies with the market state of unrealized gains/losses. Using data from the US stock markets, we show strong evidence that the disposition effect drives time‐varying risk preference: The risk aversion coefficient is significantly positive during periods of unrealized gains, but insignificant during periods of unrealized losses. These findings reconcile the conflicting results of the risk‐return trade‐off in existing literature. Moreover, our model shows significant predictability of the equity risk premium, both in‐sample and out‐of‐sample. Incorporating our model's predictions can yield substantial utility gains for a mean‐variance investor. Our results indicate that the disposition effect leads to time‐varying risk preference and thus induces equity risk premium predictability.