Is there a price bubble in the exchange rates of the developing countries? The case of BRICS and Turkey

This paper attempts to examine the relationship between exchange rates and IT stock prices. The data for the study was taken from the information of daily closing observations of the NSE CNX IT Index and the nominal Indian Rupee per US dollar exchange rates. The study was based on exchange rate of Indian rupee and US Dollar. Statistical tests were applied to study the behavior and dynamics of exchange rates. The results of the study indicate that, both CNX IT Nifty returns and Exchange Rates are not normally distributed. Also it was found that, time series; Exchange rate and CNX IT returns are stationary at the level form itself. A negative correlation is observed between CNX IT returns and Exchange Rates. The direction of influence between two series is verified by the Granger causality test and the results of the test states that Exchange rates, clearly, Granger cause the IT stocks whereas IT stocks prices cannot be said to direct the Exchange rates.

The Generalized Supremum Augmented Dickey-Fuller (GSADF) technique is performed to resolve whether the Indonesian Rupiah/US exchange rate has experienced multiple explosive bubbles. The GSADF uncovers that the Indonesian Rupiah/US exchange rate deviates from the fundamental values by six times from January 1985 to September 2023, periodically indicating the presence of numerous explosive behaviors. Once the full-sample period separates into the managed-floating regime and the free-floating regime, the GSADF still detects multiple bubbles. Of particular curiosity on uncertainty trinity, this study underlines that global geopolitical risk negatively drives explosive actions in the ratio of exchange rates for non-traded and traded goods. The global economic policy uncertainty negatively affects speculative bubbles in the exchange rate and the ratio of exchange rates for non-traded. The country's geopolitical risks negatively strike only speculative bubbles in the exchange rate. Further, we find heterogeneity in our results by examining different exchange rate systems. The robustness checks further firmly ascertain across baseline empirical findings.

Financial bubbles are described as the deviation of the market values of financial assets from their core values. They are one of the main indicators for the financial crises by negatively affecting the markets due to the constant overvaluation of the assets. Therefore, investigating the presence of financial bubbles and determining the reasons are of great importance for investors, market regulators, and portfolio managers in terms of investment risk and price stability. In the study, the presence of bubbles in the foreign exchange market is investigated by considering the most traded currencies such as the US Dollar, Euro, Japanese Yen, British Pound, and Chinese Yuan. For this purpose, the daily price data for the period between 03.01.2005-20.11.2019 for the US Dollar/Turkish Lira, Euro/Turkish Lira, British Pound/Turkish Lira, and Chinese Yuan/Turkish Lira, and the daily price data belonging to 28.08.2013-20.11.2019 period for Japanese Yen/Turkish Lira are used. Supremum Augmented Dickey-Fuller (SADF) and Generalized Supremum Augmented Dickey-Fuller (GSADF) tests are used to determine the bubbles in aforementioned exchange rates. The results indicate findings regarding the formation of financial bubbles in the foreign exchange market in Turkey.

Exchange rate movements have a great impact on the political and Economic stability of a country. Understanding the dynamic behavior of exchange is extremely important for decision makers such as legislators, investors and market participations in foreign exchange markets. The efficiency of the foreign exchange market is related to the long memory property of the exchange rate dynamics. The question of whether exchange rate markets are efficient or not, is directly related to the long memory in the exchange rate changes. Therefore, detecting long memory in an exchange rate dynamic is important to understand whether exchange rate markets of an economy are efficient or not. Long memory suggests very strong market inefficiency. However, South Asian exchange rates have not received much attention about long memory in the finance literature. This study intends to fill this gap in the finance literature by examining the long memory properties of the South Asian foreign exchange rates against to U.S. dollar, namely Sri Lankan Rupee (LKR/USD), Indian Rupee (INR/USD), Pakistan Rupee (PKR/USD), Bangladesh Taka (BDT/USD), Bhutanese Ngultrum (BTN/USD), Nepalese Rupee (NPR/USD), Maldivian Rufiyaa (MVR/USD) and the Afghan Afghani (AFN/USD). The study covers the period from January 1, 2007 to December 31, 2017 consisting 2870 daily observations per country. The growth rates of daily exchange rate are measured by the return series defined as log difference of exchange rate. These data were collected from the Central bank of Sri Lanka. To examine the random walk nature (issue of unit roots) of empirical exchange rate behavior, standard unit root tests; the ADF test (Dickey & Fuller, 1979, 1981) and the PP test (Phillips & Perron, 1988), and the KPSS test (Kwiatkowski-Phillips-Schmidt-Shin (1992)) are implemented on all exchange rate series. The ADF and PP tests are used to test the null hypothesis of the series are non-stationary I(1), against the alternative that they are stationary. The null hypothesis of KPSS test is: series is stationary against series are nonstationary. To achieve the main objective of the study, a battery of non-parametric (Rescaled Range Statistics (R/S)), Semi-parametric (Geweke and Porter-Hudak (GPH) and Local Whittle Estimator (LWE)), parametric (Fractionally Integrated Autoregressive Moving Average (ARFIMA)) tests are employed based on econophysics models. Tests show that exchange rate return series have long memory except Maldivian Rufiyaa (MVR/USD), Bangladesh Taka (BDT/USD) and Afghan Afghani (AFN/USD) and they are fractionally integrated. The fractional difference parameters are significant at 5 % level. Findings showed that south Asian foreign exchange rates except Afghanistan, Bangladesh and Maldives possess long memory. The findings of the study have policy implications for traders and investors in implementing trading strategies. The results indicate that an exchange rate market which has long memory is not efficient.

PurposeRosewood, as the most internationally traded endangered species, is subject to a series of restrictive trade policies globally. China has historically been the largest importer of rosewood in the world. The fluctuation of China’s rosewood import prices will have a profound impact on the global rosewood trade pattern. This study, therefore, assessed the impact of restrictive trade policies on China’s rosewood import prices to explore the fluctuation rule of rosewood trade prices under restrictive policies.Design/methodology/approachThe study built a partial equilibrium framework about the formation mechanism of rosewood import price bubbles under supply constraints. On this basis, with China’s daily import prices of major rosewood species, the generalized supremum augmented Dickey–Fuller (GSADF) and backward supremum augmented Dickey–Fuller (BSADF) tests were applied to explore the effect of restrictive trade policies on China’s rosewood import prices.FindingsThe empirical analysis revealed that there were multiple price bubbles for five of the seven rosewood species. The largest bubbles were always created before and after the deployment of supply constraints. The empirical results for the counterfactual examples implied that price bubbles would not have occurred if restrictive rosewood trade policies had not been implemented. The above findings indicated that these measures tended to trigger significant price bubbles in China’s rosewood imports.Originality/valueThe effect of restrictive rosewood trade policies on rosewood trade prices had not yet been explored in previous research studies. This study empirically analyzed the effect of restrictive trade policies on China’s rosewood import prices using econometric models.

Purpose– The authors make assessment on RMB valuation and to contribute to the fierce debate on this important issue, which is perceived to have a great effect on the improvement or deterioration in trade balance. A triangular analysis approach is put forward and empirical assessment is made. The paper aims to discuss these issues.Design/methodology/approach– A triangular analysis approach based on no arbitrage conditions for three currencies, and causality and influence analysis.Findings– First, it has been found that the movements in the RMB dollar exchange rate do influence the dollar euro exchange rate and the former do have a causality effect on the latter, in both the long run and the short term. Second, it is implied that the RMB is overvaluedvis-à-visthe US dollar, as the analysis suggests that an overvalued eurovis-à-visthe US dollar would imply a kind of overvaluation of the RMBvis-à-visthe US dollar, and by any conventional measures the euro has appeared to be overvaluedvis-à-visthe US dollar, especially in the months before the last financial crisis.Practical implications– First, the peg of the RMB to the US dollar that undervalues the RMBvis-à-visthe US dollar will not help promote China's overall trade balance or export even if undervaluation of currencies can ever help improve nations' terms of trade. Second, no stability in RMB exchange rates can be claimed by pegging the RMB to the US dollar, as the exchange rate of the RMBvis-à-viscurrencies other than the US dollar would be as volatile as that between the US dollar and the euro and other convertible currencies.Originality/value– A new triangular analysis approach in international finance research. First, there is an advantage to adopt this seemingly simple analytical framework: it is highly reliable; no triangular arbitrage conditions have to be met even under exchange controls, whilst PPP may not hold even with flexible exchange rate regimes. Second, it does away with the thinking confined to small open economies that has dominated academic research for so long and is totally inapplicable to the RMB case.

Oil is an important energy resource. Fluctuation in international crude oil prices affects all aspects of the economy. The exchange rate is one of the important channels for the international crude oil price shock to pass to the real economy and financial markets. The impact of international crude oil price fluctuation on the exchange rate of oil-importing countries has attracted more and more attention. The US dollar is the main invoice and settlement currency of the international oil market. The change of the US dollar exchange rate will inevitably affect the international crude oil price. By combing the relevant classic literature and recent literature, the relationship between international crude oil prices and the exchange rate of oil-importing countries is discussed. Due to the different methods of use, the relationship between the two is not conclusive. This paper probes into the impact paths of national crude oil price fluctuation on the exchange rate of oil-importing countries, but the influence level depends on the relative degree of each country affected by oil prices, which cannot be generalized. The article summarizes the methods of studying the relationship between oil prices and exchange rates, most of which are studying the causal relationship between the two and their mutual influence, nonlinear structure, and volatility spillover effects. Through reviewing and summarizing the relevant literatures, this paper argues that exploring the inherent laws of oil price volatility buffer under different exchange rate regimes and studying the transmission mode and intensity of oil price fluctuations on exchange rate effects can be the focus of future research.

This paper applies recursive right-tailed unit root tests to detect bubble activity for Turkish Lira against financially most-traded five currencies (i.e., the US Dollar (USD/TRY), the British pound (GBP/TRY), the Euro (EUR/TRY), the Chinese Yuan (CNY/TRY) and the Russian Ruble (RUB/TRY)) over January 2, 2015 to February 12, 2021. It can be identified from the Supremum Augmented Dickey–Fuller (SADF) and the Generalized Supremum Augmented Dickey-Fuller (GSADF) tests statistics that there is a high degree of evidence of bubble activity which characterizes all five exchange rates both in the full-sample period and in the sub-periods, including the pre-COVID-19 era (January 2, 2015 to November 15, 2019) and the COVID-19 era (November 18, 2019 to February 12, 2021). The empirical results also indicate that positive bubbles are common for each selected exchange rate and the multiple bubbles were intensified during the COVID-19 period, referring that forex markets became relatively more inefficient compared to the pre-COVID-19 period.

Previous articleNext article FreeCommentMichael W. McCrackenMichael W. McCrackenFederal Reserve Bank of St. Louis Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreI. IntroductionThis paper makes me think of academic seminars. Not in the content per se or even its presentation, but rather in how I personally know when I think a seminar is “good” or “bad.” Since there exists no such thing as a perfect paper my definition of a good or bad seminar does not reflect whether the paper is perfect. For me, a good seminar is one where I like the paper enough to be willing to engage in an active discussion even if that means pointing out aspects of the paper I disagree with. In contrast, a bad seminar is one associated with a paper that is so bad that I don’t want to ask questions because that only prevents me from getting out of the seminar as soon as possible.This is a good paper. It has a question that I find intriguing and addresses the question in a reasonable fashion. And yet it is not perfect and there are many issues that can be criticized. These include focusing only on one quarter ahead forecasts, using rolling windows to estimate parameters despite very small sample sizes, the potential for data snooping over the many models and periods considered, using somewhat quirky and oddly timed OECD data, and so forth.Rather than spend time working my way through a list of referee-style suggestions that might improve the paper, in the remainder I’ll focus on what I think is a deeper issue in this paper and, more generally, the literature, on very standard empirical macroeconomic models of exchange rates. In particular I discuss some very pragmatic forecasting issues involving forecast origins and the relevant forecast horizons.II. Forecast Origins and HorizonsAs someone who works at a central bank I tend to think of forecasting in the context of (a) Federal Open Market Committee (FOMC) dates and (b) macroeconomic aggregates. The former implies a very specific set of forecast origins—dates on which a forecast must be produced. The latter implies a very specific set of forecast horizons–the difference between the FOMC date and the dates on which the macroeconomic variables are published by the Bureau of Economic Analysis (e.g., GDP), the Bureau of Labor Statistics (e.g., the unemployment rate), or even the Federal Reserve System (e.g., industrial production). Both are very convenient because they define the collection of data available at any given FOMC meeting (anything observed before that date) and it defines how far into the future we have to forecast (the day in which the data is released). In the notation of a forecasting model this tells me when my forecast origin t is and how far ahead into the future h my forecast horizon is. In contrast, in this paper these quantities are not very clearly motivated and hence the remainder of my discussion will focus on how that affects how we should view their results on the predictive content of macroeconomic models of exchange rate predictability.In this paper the authors consider a very standard forecasting exercise in which they investigate the predictability of bilateral US dollar/ euro exchange rate movements. To do so they consider a very standard collection of empirical models including the Monetary, Purchasing Power Parity, and interest rate differentials models, as well as variants of a Taylor rule based model developed in earlier work by Moltsdovoya and Papell (2009). Each of these models implies a set of predictors x that are observed at a quarterly frequency. In addition, following the literature, the y variable being predicted is measured as the log-difference of the exchange rate observed on the last business day of each quarter. For example, this implies that y2012:Q1 equals the natural log of the exchange rate measured on March 31, 2012, minus the same measured on December 31, 2011. In each case the predictive model is an OLS (ordinary least squares) estimated linear regression of the form in which measurements of macroeconomic aggregates obtained prior to the current quarter are used to predict the current quarter log-difference in the exchange rate.While standard, this modeling procedure is not obvious for someone who works in a very structured forecasting environment such as a central bank. For example, suppose that I observe my quarterly frequency xt value on December 31, 2011. Why is it that we forecast exchange rate movements at the one quarter horizon and not, say, at the one month horizon? In this framework we would define yt+1 as the log-difference in the exchange rate over the first month of quarter t + 1 and hence y2012:Q1 equals the natural log of the exchange rate measured on January 31, 2012, minus the same measured on December 31, 2011? The US dollar/euro exchange rate varies not only across the quarter but also does so monthly, weekly, daily, and even intra-daily. That’s not to say that the one quarter horizon isn’t potentially interesting, but rather there is nothing about the exchange rate market that implies that a one quarter ahead horizon is a natural forecast horizon given an information set of data available through the end of the previous quarter. It is perfectly possible that a quarterly frequency predictor xt would be useful for forecasting soon after its release date (a day, a week, or even a month) and yet at a one quarter ahead horizon a naive random walk forecast dominates. My fear is that the one quarter ahead horizon is chosen by default simply because the x variable is observed at a quarterly frequency.In the previous example I assumed that my quarterly frequency predictor xt was observed on the last day of the previous quarter, say December 31, 2011. If I am using it as a predictor of current quarter exchange rate movements, the earliest possible forecast origin is clearly December 31, 2011. But what if I am asked to provide a forecast of future current quarter exchange rate movements at an FOMC meeting dated January 31, 2012? I could still use it as a predictor but I would want to redefine my y variable. For example, suppose I define yt+1 = y2012:Q1 as the natural log of the exchange rate measured on March 31, 2012, minus the same measured on January 31, 2012. There is nothing stopping me from using the same regression framework from before to construct a forecast. In this hypothetical world, since xt is defined on the last day of the previous quarter, I could conduct this type of exercise for y variables defined over any subperiod of quarter t.In figure 1 we consider such an exercise for four distinct definitions of yt+1 when the Taylor rule fundamentals model uses the output gap for prediction.1 When defined relative to t + 1 = 2012:Q1, these take the values of the difference in the log-exchange rate between (a) March 31, 2012, and December 31, 2011 (the definition of yt+1 considered in the paper and elsewhere in the literature); (b) January 31, 2012, and December 31, 2011; (c) February 28, 2012, and January 31, 2012; and (d) March 31, 2012, and February 28, 2012. The figure consists of four lines. When , the line corresponds to the MSPE ratio path from table 1, panel A, of the paper (case (a)). The other lines are the MSPE ratio paths when the forecast origin and horizon are defined relative to cases (b), (c), and (d).Fig. 1. MSPE ratios Taylor rule fundamentals model with output gapView Large ImageDownload PowerPointWe immediately find there is considerable heterogeneity in the predictive content of this model across the quarter. Over the first month of the quarter (so that ) the model predicts quite poorly relative to the random walk benchmark, with MSPE ratios near 1.2. Over the second month of the quarter the model does a bit better with ratios near 1.1, but is still worse than the random walk model. Somewhat surprisingly, the model consistently outperforms the random walk model during the last month of the quarter with MSPE ratios generally below one with values ranging from 0.95 to 0.9. Integrating across these three lines we obtain the line that matches the numbers from table 1, panel A .The MSPE ratio paths lead to a somewhat odd conclusion: the model performs better, relative to the random walk model, the closer we get to the end of the quarter. This is despite the fact that the information content in the predictors is increasingly stale as we move from a forecast origin of the last day of the previous quarter to a forecast origin of the last day of the second month of the current quarter. If we take a deeper look at the raw MSPEs from the random walk and Taylor rule models (not shown) we find that both models contribute to this result: the random walk MSPE path associated with the first month of the quarter tends to be a bit lower than that from the third month of the quarter, while the Taylor rule model MSPE path associated with the first month of the quarter tends to be a bit higher than that from the third month of the quarter. Whether or not these paths are statistically distinct from one another is beyond the scope of the discussion, but the differences are interesting nevertheless.One potential explanation might arise from the derivation of the Taylor rule based models and in particular the timing of information flows within these models. As described in section II, subsection A, equation (1) of the text, the basic building block of this model is an equation of the form2 where it is the target for the short-term nominal interest rate, πt is the inflation rate, is the target level of inflation, gt is a measure of the output gap (or more generally some measure of economic slack in the economy), and R is the equilibrium level of the real interest rate. The equilibrium concepts and R are known constants chosen by the relevant monetary authority. Moreover, the preference parameters Φ and γ are also known to the monetary authority. The basic premise of this rule is that it provides a description of what the monetary authority should do when selecting the target for the short-term nominal interest rate i at time t based on the levels of π and g observed at time t.With this in mind consider the logic followed in developing the Taylor rule based predictive model for exchange rates. First we take the time t difference between the Taylor rule associated with the FOMC and that for the Governing Council of the ECB (GC hereafter) as the authors do for equation (4) of the text where asterisks denote observables for the euro area and the lack thereof denotes an observable for the United States. In addition we maintain that the policy parameters Φ and γ are common across the FOMC and GC and hence λ = 1 + Φ, while we aggregate R, R*, , and Φ into the constant term α. From here, with a bit of handwaving that links interest rate differentials to exchange rate movements, the authors obtain the predictive equation In the paper, t is linked one-to-one with quarters as defined by a calendar year where, as an example, January, February, and March together define the first quarter of a year. This is not entirely unreasonable and is the procedure followed throughout much of the literature including Mark (1995); Cheung, Chinn, and Pascual (2002); and Engel, Mark, and West (2008) in the context of other, non-Taylor rule based, quarterly frequency macroeconomic models of exchange rate determination. Moreover, with t defined relative to a sequence of quarters within a calendar year, setting h equal to 1 is not an unreasonable choice.And yet given the description of the Taylor rule from earlier, it’s not clear that is the correct way to view t. Recall that i is defined as the target for the short-term nominal interest rate. This rate typically only changes when the FOMC or the GC has its regularly scheduled meetings: eight times a year for the FOMC (twice per quarter; approximately the third and ninth week of each quarter), and twelve times a year for the GC (once per month and typically in the first two weeks of the month).3 This implies that irrelevant of the terms on the right-hand side of (4), the left-hand side will literally only change if either the FOMC or the GC changes its respective policy rate. Put differently, equation (4) implies that t is not so much indexed to calendar time as indexed to scheduled meetings of the FOMC or the GC.That is not to say that the right-hand side terms in (4) are irrelevant for exchange rate movements. Quite the contrary, these are very much the types of data the FOMC and GC looks at when making decisions about the short-term policy rate. The problem is that by transitioning from equation (4) to equation (5) you are changing a time index that is primarily associated with the timing of FOMC and GC meetings to one that is interpreted as being associated with (end of quarter) quarterly calendar dates.To see how this might affect the intra-quarter predictability of the Taylor rule based model, consider the following approximate time line of FOMC and GC meetings for the first quarter of 2012: January 12*, January 25, February 9*, March 8*, and March 13, where I’ve let an asterisk denote a GC meeting and the absence of an asterisk denotes an FOMC meeting. If the Taylor rule based predictive model is taken literally, exchange rate movements in 2012:Q1 due to changes in policy rates (it − it*) can only occur on or after these dates. These policy rates in turn will have changed only if the inflation rate or the output gap changed since the previous meeting. Since US RGDP for 2011:Q4 was released on January 28, 2012, euro area RGDP for 2011:Q4 was released on February 15, 2012, and the next GC and FOMC meetings do not occur until March, the only month within 2012:Q1 that the output gap component of the Taylor rule will be able to affect exchange rate movements is March–the third month of the quarter, in accordance with the MSPE ratio paths from figure 1.III. ConclusionAs I said in the introduction, I like this paper and a lot can be learned from it. Perhaps my favorite part is simply that the authors took the time to gather vintage data in order to conduct their forecasting exercises in something akin to a real-time environment—the kind of environment policymakers would have faced throughout the past decade and particularly during the Great Recession. Even so, there are many unanswered questions associated with the paper. And as I made clear in my discussion, the aspect of the paper that confuses me the most is the simple definition of the forecast origins and horizons implied by these quarterly frequency macroeconomic models of exchange rate predictability. And again, to be fair, this concern is not uniquely tied to this paper but it is exacerbated by the focus this paper puts on Taylor rule based models of exchange rate predictability—models that center around changes in the short-term policy rates set by both the FOMC and Governing Council of the ECB.EndnotesThe views expressed herein are solely those of the author and do not necessarily reflect the views of the Federal Reserve Bank of St. Louis or the Federal Reserve Board of Governors. For acknowledgments, sources of research support, and disclosure of the author’s material financial relationships, if any, please see http://www.nber.org/chapters/c12775.ack.1. The data was kindly provided by the authors.2. In the following I use g to denote an output gap rather than y, as is done in the text.I do so to distinguish it from the generic use of y as a dependent variable.3. The ECB Governing Council meets more like twice per month for a total of 24 times per year. However, the first meeting of the month is the one associated with decisions on the policy stance of the ECB.ReferencesCheung, Y., M. Chinn, and A. Pascual. 2002. “Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive?” Journal of International Money and Finance 24: 1150–75.First citation in articleGoogle ScholarEngel, Charles, Nelson C. Mark, and Kenneth D. West. 2008. “Exchange Rate Models Are Not As Bad As You Think.” In NBER Macroeconomics Annual 2007, edited by Daron Acemoglu, Kenneth Rogoff, and Michael Woodford, 381–441. Chicago: University of Chicago Press.First citation in articleGoogle ScholarMark, Nelson. 1995. “Exchange Rate and Fundamentals: Evidence on Long-Horizon Predictability.” American Economic Review 85: 201–18.First citation in articleGoogle ScholarMolodtsova, Tanya, and David H. Papell. 2009. “Exchange Rate Predictability with Taylor Rule Fundamentals.” Journal of International Economics 77: 167–80.First citation in articleGoogle Scholar Previous articleNext article DetailsFiguresReferencesCited by Volume 9, Number 12013 Article DOIhttps://doi.org/10.1086/669591 © 2013 by the National Bureau of Economic ResearchPDF download Crossref reports no articles citing this article.

This study investigates the presence and temporal dynamics of speculative bubbles in Türkiye’s citrus markets. The analysis is based on seasonal price data covering the 2000/2001 to 2023/2024 marketing years. This study employs the Supremum Augmented Dickey–Fuller (SADF) and Generalized SADF (GSADF) tests to determine whether these surges are attributable to fundamental market forces or speculative behavior. The results indicate the presence of speculative bubble episodes in citrus markets, particularly after 2020. Orange and mandarin prices exhibited persistent speculative patterns over multiple seasons, whereas lemon prices displayed shorter and more delayed bubble episodes. These findings suggest that speculative bubbles are not solely driven by supply and demand dynamics but are also significantly influenced by market uncertainty, asymmetric information, and financialization. The findings underscore the need for early warning systems, enhanced market transparency, and flexible policy interventions to mitigate the impact of speculative bubbles.

Abstract. This study examines the relationships between inflation, interest rates, and US dollar exchange rates from 2013 to 2023. The main objective is to evaluate the value of the US dollar (USD) and understand how key economic indicators interact. Using advanced quantitative analysis, we identify patterns that define these variables. A macro model incorporating the natural logarithms of the USD Index (USDX), Federal Funds Rate (FFR), and Consumer Price Index (CPI) addresses time-related and growth factors. By tracking inflation, interest rates, and dollar fluctuations, we gain insights into the factors influencing the US economy. The findings highlight the slow adjustment of inflation towards long-term stability and reveal significant causal relationships among the variables. As global financial conditions evolve, this study offers relevant insights for policymakers, economists, and market participants on navigating the dynamics of inflation, interest rates, and exchange rates in today's economic landscape. Keywords: Inflation; exchange rate; the United States; fed; fed funds rate

Purpose This study aims to identify and analyze speculative bubbles in the Tunisian stock market from 2004 to 2023 and examine the evolution of return volatility during these periods. Design/methodology/approach The research uses the Supremum Augmented Dickey-Fuller (SADF) and Generalized Supremum Augmented Dickey-Fuller (GSADF) tests, alongside Monte Carlo and bootstrap simulations (Sieve-bootstrap and Wild-bootstrap), to detect speculative bubbles. The Markov-Switching Generalized Autoregressive Conditional Heteroskedasticity model is used to analyze volatility regimes. Findings The study identifies multiple speculative bubbles with varying timing, duration and response to external events. The GSADF test proves more effective than the SADF test for detecting longer, more frequent bubbles. Despite methodological differences, strong correlations among bootstrap techniques improve bubble identification. Bubble periods align with a high-volatility regime (regime 2), emphasizing volatility’s role in bubble formation. Research limitations/implications This study enhances the understanding of speculative bubble formation in emerging markets, highlighting the importance of considering national financial market specifics in bubble analysis. Practical implications The findings offer valuable insights for investors, regulators and policymakers, helping inform decisions and improve financial regulation to foster market stability. Social implications By identifying speculative bubbles, the research helps mitigate economic uncertainty, protects savings and supports financial stability, aiding policymakers in curbing excessive speculation and promoting sustainable economic growth. Originality/value This research contributes to the understanding of speculative bubbles in the underexplored Tunisian stock market, using innovative methodologies for a comprehensive analysis of bubbles and volatility dynamics.

This article presents empirical test results of Malaysian foreign exchange market microstructure assessment of exchange rate dynamics. We apply vector autoregressive (VAR) model to estimate the influential role of currency order flow in the determination of the currency exchange rate for the Malaysian ringgit (MYR) against the US dollar (USD). We investigate whether currency order flow captures the movements of exchange rate of MYR against USD, and how the long-term and short-term components impact the relative estimation of MYR in the international market. We, construct a measure of order flow in the Malaysian foreign exchange market to reflect the pressure of currency excess demand. Our focus is on the cumulative currency order flow and the exchange rate relationship of MYR and USD. A hybrid model of order flow and exchange rate dynamics proposed by Evans and Lyons (2002a) is applied to the Malaysian foreign exchange market (MYR/USD) to analyse a dataset of every 15-minute currency order flow and exchange rate movements from January 2010 to December 2015. Our dataset has unique features in terms of the quality of the data, extensive period and precise high frequency. Our results show that currency order flow explains an important portion of the movements in the MYR–USD exchange rate.

The study examines the possibility of a stock market price bubble in four sub-sector stock indices (financial, industrial, service, and technology) and the BIST 100 composite index in Turkey by using the monthly data spans from 2000 to 2023. To capture the irrational prosperity in Turkish stock markets, we employ the supremum augmented Dickey-Fuller (SADF) and the generalized supremum augmented Dickey-Fuller (GSADF) methodologies. Our primary focus is on the construction of price bubbles, particularly in the aftermath of the COVID-19 pandemic. Presently, there are ongoing and intense discussions regarding the potential emergence of such bubbles. The conclusion implies the presence of speculative bubbles in all sector-specific stock market indexes, with the exception of the technology index, subsequent to the complete removal of COVID-19 restrictions by the Turkish government. Moreover, the results show that exuberant investor behavior also occurred in some sub-periods other than the post-COVID-19 period for all stock market indices.

The Interest Rate Parity Theorem: A Reinterpretation