On the Nonlinear Causality Between Inflation and Inflation Uncertainty in the G3 Countries

This study examines the causal nexus between inflation and inflation uncertainty. In this regard, conventional Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and Stochastic Volatility (SV) models are used to measure inflation uncertainty and Bai and Perron (Econometrica 66:47–78, 1998; J Appl Econom 18:1–22, 2003) test is used to identify structural breaks in inflation. The empirical evidence derived from the monthly data for the period from June 1961 to April 2011 suggests that the measure of inflation uncertainty obtained from SV model is more reliable than the measure obtained from GARCH model and also the causal nexus between inflation and inflation uncertainty seems to be significantly conditional upon the measure of uncertainty used. The structural break test identifies four episodes of inflation during the sample period, and the causality between inflation and its variability varies across different episodes. The inflation and its variance seem to be independent of each other during the first two regimes that cover the period from 1960 to 1980 and on the contrary, during the later period largely bidirectional causality is observed. Further, inflation seems to exert positive impact on inflation uncertainty, whereas inflation uncertainty has negative impact on inflation.

On inflation and inflation uncertainty in the G7 countries

On the inflation-uncertainty hypothesis in Jordan, Philippines and Turkey: A long memory approach

The aim of this study is to determine the number of transactions among the currencies, which will eventually become a part of our lives, cannot be physically held, can move quickly, and emerge as a new shopping and investment tool in the changing world order, as of the year (2023) when this study was conducted. The study focuses on the analysis of the variables that affect the most popular currency, Bitcoin. Although the analysis of variables that influence Bitcoin was determined as the primary aim of the study, the study also attempted to reach a general conclusion about the variables affected by the cryptocurrencies. Since there is no other cryptocurrency that is traded as much as Bitcoin, Bitcoin is thought to be a good model for the analysis of cryptocurrencies. The method used in the study was autoregressive conditional heteroskedastic (ARCH) models. It is believed that the most suitable models for the Bitcoin variable, whose value changes every second, are ARCH and its derivatives. Other models selected from the ARCH models were also added to the analysis as a method. The models used in the study can be listed as follows: linear ARC, generalized ARC (GARCH), exponential GARCH and threshold GARCH. A statistical model called autoregressive conditional heteroscedasticity (ARCH) is used to study the volatility of time series. Through the provision of a volatility model that more closely mimics actual markets, ARCH modeling is utilized in the financial sector to quantify risk. According to ARCH modeling, periods of high volatility are followed by even higher volatility, and periods of low volatility are followed by even lower volatility. In this study, 5 different variables were selected using literature to analyze the variables affecting Bitcoin returns using ARCH models. The dependent variable in the study is the price of Bitcoin. The remaining variables were included in the models as independent variables. These variables are actually variables that are accepted and selected as the best among a set of variables. In other words, 15 variables were first added to the study using the literature. After this, a correlation analysis was carried out. As a result of the correlation analysis, the variables with the highest correlation with the price of Bitcoin, which is the dependent variable, and the lowest correlation with each other were retained in the model. These variables are Bitcoin Price, Crude Oil Spot Price, Euro-Dollar Parity, Gold Spot Price and NASDAQ Composite Index. The study period is between 2020 and 2023 and it was studied using daily data. Days with no data were removed from the daily period from 2020 to 2023 and loss of information was prevented. After removing missing observations, this study examined the remaining 837 observations. During the research, while running the models created using different methods, it was found that the model that gives the best result is the GARCH model. In other words, when modeling the variables affecting bitcoin (cryptocurrency from the perspective of the population), it was seen that the GARCH model gave the best results when comparing linear ARCH, generalized ARCH (GARCH), exponential GARCH, and threshold GARCH of the ARCH model. Comparing the output of the GARCH model with other ARCH models not included in this study can be a recommendation for the future study

This paper estimates the volatility of the won-dollar exchange rate during the 2008-9 crisis. We find that the volatility increased in September 2008 and decreased in May 2009. The volatility rose gradually for one month and subdued in a similar manner, which implies that the volatility was not governed by any specific event or government policy. The overall changes in the volatility are similar to the movements of the CDS premium. We also find that the UK foreign exchange market experienced a similar pattern of volatility shifts and suffered smaller but longer volatility than the Korean one. The volatility shifts are estimated using a Markov switching GARCH model and a Bayesian method is suggested.Keywords: Bayesian Inference, Markov Switching GARCH Models, Exchange Rate Volatility, Credit CrisisJEL classification: C11, C22, F31(ProQuest: ... denotes formulae omitted.)1. INTRODUCTIONThe Korean foreign exchange market has experienced two crises during the last two decades, as shown in Figure 1. The exchange rate was managed by the government before the Asian financial crisis in 1997-8. The Korean currency, or the won, depreciated sharply during the crisis and showed a trend of appreciating during the next decade. The foreign exchange market suffered from another foreign capital flight during the global credit crisis in 2008-9. Contrary to the case of the 1997-8 crisis, the 2008-9 credit crisis stemmed from the developed economies. However, the credit crisis led many developing countries, including South Korea, to an economic or financial crisis. Many policies were implemented to stabilize the financial and foreign exchange markets in Korea. The Bank of Korea has swiftly lowered the policy rate from 5.25% to 2% for 4 months since October 2008. The government announced the Financial Market Stabilization Measures, which included government warrants of the foreign currency debt of the commercial banks and provision of dollar liquidity to them, on October 19, 2008. The Bank of Korea has also made a 30-billion US dollar swap arrangement with the Federal Reserve on October 30, 2008 and other deals with the People's Bank of China and the Bank of Japan on December 12, 2008. From October 2008 to February 2009, it provided around 30-billion dollar liquidity to financial institutions which had difficulties in overseas fund raising.This paper estimates the volatility of the won-dollar exchange rate during the 2008-9 crisis and investigates what determined the volatility. We find that the volatility increased in September 2008 and decreased in May 2009. The volatility rose gradually for one month and subdued in a similar manner, which suggests that the volatility was not governed by any specific event or government policy. The overall changes in the volatility are similar to the movements of the CDS premium. We also find that the dollar-pound exchange rate experienced a similar pattern of volatility shifts to that of the won-dollar exchange rate. But, the UK foreign exchange market suffered smaller but longer volatility than the Korean one.Since the Autoregressive Conditional Heteroskedastic (ARCH) model was suggested by Engle (1982), the conditional heteroskedastic models have been updated and developed to analyze the volatility of the financial markets. Researchers, including Bollerslev (1986), generalized the ARCH model to GARCH (generalized ARCH) and its variants, such as IGARCH, GARCH-M, and EGARCH. The models are known to describe well the many features of volatility, such as volatility clustering and the leverage effect.1 However, as Schwert (1990) and Engle and Mustaffa (1992) show, the GARCH models imply too much persistence in the conditional variance. To overcome this shortcoming, Cai (1994) and Hamilton and Susmel (1994) incorporate the Markov switching component into the ARCH model. Gray (1996) and Dueker (1997) generalize their model to Markov switching GARCH (hereafter, MS-GARCH) models. …

رابطه میان تورم و نااطمینانی آن می تواند تحت تأثیر رژیم های تورمی مختلف قرار گیرد . تحقیقات انجام شده در ایران، نقش این رژیم ها در ارتباط پویای تورم و نااطمینانی را بررسی نکرده اند. به‌منظور پرکردن این خلأ در ادبیات اقتصاد ایران، این مقاله به مطالعه رابطه میان تورم و نااطمینانی آن با وجود انتقال رژیم و با توجه به رفتار نامتقارن الگو می پردازد. برای دستیابی به این هدف از تبدیل مارکوف در چارچوب یک الگوی تعمیم یافته گارچ نامتقارن استفاده می گردد. به این منظور دو معادله به ترتیب برای تورم و نااطمینانی آن، برای دوره (2013:07-1990:03) برآورد می گردد. معادله اول تحت دو رژیم فشار تورمی فزاینده وکاهنده و معادله دوم رفتار در دو وضعیت نوسانات تورمی زیاد و کم برآورد می شود. برآوردها نشان می دهد که اثر نااطمینانی تورم بر سطح تورم در رژیم فشار تورمی فزاینده، مثبت اما در رژیم فشار تورمی کاهنده، منفی است. همچنین در وضعیت نوسانات تورمی زیاد، افزایش تورم باعث ازدیاد نااطمینانی اما در وضعیت نوسانات تورمی کم، سطح تورم بر نااطمینانی تورم تأثیری ندارد. اثرات تکانه های مثبت قیمتی بر نااطمینانی بیش تر از تکانه های منفی می باشد و احتمال ماندگاری در هر وضعیت تورمی در ایران بالا است. با توجه به نتایج، به نظر می رسد که اتخاذ سیاست های تثبیت قیمت ها نه‌تنها در کاهش تورم بلکه در کاهش نااطمینانی تورم نیز نقش مهمی دارند؛ بنابراین، پیشنهاد می گردد که دولت و به‌ویژه بانک مرکزی از اتخاذ سیاست های اقتصادی که به نااطمینانی تورم دامن می زند، اجتناب نماید. ازجمله نتایج مهم دیگر این تحقیق که باید مورد توجه مسئولین پولی قرار گیرد، اهمیت تشخیص درست و به‌موقع نوع رژیم تورمی کشور برای اتخاذ سیاست مناسب است.

The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application.

This paper studies the impact of inflation on inflation uncertainty in a modelling framework where both the conditional mean and conditional variance of inflation are regime specific, and the GARCH model for inflation uncertainty is extended by including a lagged inflation term in each regime. Applying this model to the G7 countries with monthly data from 1970 till 2013, it is found that the impact of inflation on inflation uncertainty differs over the regimes in most of the G7 countries. The findings also provide strong empirical support to the well-known Friedman-Ball hypothesis of positive impact of inflation on inflation uncertainty, but only for the high-inflation regime.

A standard Generalized Autoregressive Conditional Heteroskedastic (q,v) model is employed to construct a measure of monthly inflation uncertainty in 12 emerging market economies, and the relationship between inflation and inflation uncertainty is examined using Granger‐causality tests. The results suggest that higher inflation rates increased inflation uncertainty in all the economies, providing strong support for the Friedman hypothesis. The evidence on the effect of inflation uncertainty on average monthly inflation is more mixed, with increased inflation uncertainty leading to lower average inflation in Colombia, Israel, Mexico, and Turkey, consistent with the Holland hypothesis, but to higher average inflation in Hungary, Indonesia, and Korea, consistent with the hypothesis of Cukierman and Meltzer.

The welfare costs of inflation and inflation uncertainty are well documented in the literature and empirical evidence on the link between the two is sparse in the case of Egypt. This paper investigates the causal relationship between inflation and inflation uncertainty in Egypt using monthly time series data during the period January 1974–April 2015. To endogenously control for any potential structural breaks in the inflation time series, Zivot and Andrews (2002) and Clemente–Montanes–Reyes (1998) unit root tests are used. The inflation–inflation uncertainty relation is modeled by the standard two-step approach as well as simultaneously using various versions of the GARCH-M model to control for any potential feedback effects. The analyses explicitly control for the effect of the Economic Reform and Structural Adjustment Program (ERSAP) undertaken by the Egyptian government in the early 1990s, which affected inflation rate and its associated volatility. Results show a high degree of inflation–volatility persistence in the response to inflationary shocks. Granger-causality test along with symmetric and asymmetric GARCH-M models indicate a statistically significant bi-directional positive relationship between inflation and inflation uncertainty, supporting both the Friedman–Ball and the Cukierman–Meltzer hypotheses. The findings are robust to the various estimation methods and model specifications. The findings of this paper support the view of adopting inflation-targeting policy in Egypt, after fulfilling its preconditions, to reduce the welfare cost of inflation and its related uncertainties. Monetary authorities in Egypt should enhance the credibility of monetary policy and attempt to reduce inflation uncertainty, which will help lower inflation rates.

This paper explores bidirectional linkage between inflation and its uncertainty by observing monthly data of 11 Eastern European countries. The methodological approach comprises two steps. First, inflation uncertainty series have been created by choosing an optimal Generalized Autoregressive Conditional Heteroskedasticity- (GARCH) type model. Subsequently, inflation and inflation uncertainty have been observed together by two models examining whether Friedman's and Cukierman–Meltzer's hypotheses hold for selected Eastern Europe Countries (EEC). Due to the heterogeneous behaviour of some series of inflation and inflation uncertainty, the unconditional quantile regression estimation technique has been applied because of its robustness to the particular non-normal characteristics and outliers’ presence in the empirical data. According to the findings, both Friedman's and Cukierman–Meltzer's hypotheses have been confirmed primarily for the largest EEC with flexible exchange rate. In contrast, these theories are refuted in smaller, open economies with firm exchange rate regime.

Steel structures such as transmission towers may have cracks and other damages during the service period. When the structure vibrates, the cracks will open and close, which makes the structure have nonlinear characteristics of variant stiffness. In order to identify this type of nonlinear damage, a time-domain damage identification method based on the residual deviation distance (RDD) of the autoregressive conditional heteroskedasticity (ARCH) model is proposed. First, the basic theory of the ARCH model is introduced, and the methods of model order determination and parameter estimation are given. Then, the time-domain nonlinear damage characteristics are analyzed, and the second-order variance (SOV) index based on the ARCH model is introduced. Considering that both the ARCH model coefficient and the RDD contain nonlinear information, a conversion index is constructed using the Euclidean distance (ED) of the ARCH model coefficients before and after the damage. On this basis, an RDD conversion index is further proposed to improve the identification accuracy. Finally, a three-storey frame experiment and a complex transmission tower model experiment are used to verify the effectiveness of the proposed method. The experimental results show that the proposed RDD conversion index can better identify the location of structural damage, and the identification results of the RDD conversion index are obviously better than those of the SOV conversion index and the ED conversion index.

This study applies linear and nonlinear Granger causality tests to examine the dynamic relation between London Metal Exchange (LME) cash prices and three possible predictors. The analysis uses matched quarterly inventory, UK Treasury bill interest rates, futures prices and cash prices for the commodity lead traded on the LME. The effects of cointegration on both linear and nonlinear Granger causality tests is also examined. When cointegration is not modelled, evidence is found of both linear and nonlinear causality between cash prices and analysed predictor variables. However, after controlling for cointegration, evidence of significant nonlinear causality is no longer found. These results contribute to the empirical literature on commodity price forecasting by highlighting the relationship between cointegration and detectable linear and nonlinear causality. The importance of interest rate and inventory as well as futures price in forecasting cash prices is also illustrated. Failure to detect significant nonlinearity after controlling for cointegration may also go some way to explaining the reason for the disappointing forecasting performances of many nonlinear models in the general finance literature. It may be that the variables are correct, but the functional form is overly complex and a standard VAR or VECM may often apply.

For the limitations of vibration signals caused by non-stationary autoregressive (AR) model which can not effectively describe the signal characteristics, it is presented a fault diagnosis based on autoregressive conditional heteroskedasticity (ARCH) model. This method firstly uses ARCH model to fit various fault signals and regard the proceeds of the model parameters as the characteristics of fault diagnosis, using RBF neural network classification as fault diagnosis method. The experimental results verify the feasibility and effectiveness of the ARCH model, and at the same time it makes the comparison with the method based on same AR model and another modified method based on AR model. The results show that the method has significantly improvements in the diagnosis rate.

This study investigates cross-autocorrelation in portfolio returns which are formed on the basis of ownership concentration. The study randomly selected seventy-two firms that are listed at the Karachi Stock Exchange. Eight portfolios were formed based on ownership concentration, with each portfolio comprising of nine firms. Equally-weighted daily and weekly returns were calculated for these portfolios. Vector Auto-Regressive (VAR) and Auto-Regressive Conditional Heteroskedasticity (ARCH) models were employed to analyze the cross-autocorrelation among the portfolio returns. The results revealed that portfolios having higher concentration of ownership lead the returns of portfolio having lower concentration of ownership. The lead-lag relationship was found in daily returns for up to three days only. No evidence was found for lead-lag pattern in weakly returns