Boschan Algorithm Research Articles

Price Analysis of Coffee in India

The present study is proposed with the objective to analyze the trend, seasonal and cyclical price variations of coffee in India. Secondary data collected from Coffee Board over a period of 13 years (2007-2020). Compound Annual Growth Rate Analysis, Seasonal Index, Bry - Boschan algorithm used to identify the price trend, seasonal and cyclical price variations of coffee in India. The farm gate price of coffee in the selected markets Chikmagalur and Tamil Nadu was positive and significant. In Chikmagalur market, prices prevailed lean during March, April, August, September, October and November and higher in the months of December, January and February for Arabica. Robusta parchment and cherry prices prevailed lean during January, November and December and higher in March, June and July. In Tamil Nadu market, price was higher in the months of January, February and March and lean during September, October and November.

Impact of the Global Financial Crisis on the Business Cycle in the Visegrad Group

Objective: The aim of the research was to analyse morphological changes in the Visegrad Group business cycles before and after the global financial crisis of 2007/2008, and to provide comparison of their overall economic performance in terms of the real GDP growth, unemployment rate changes and price stability. Research Design & Methods: Visegrad Group’s business cycles were determined by employing Hodrick and Prescott filter to extract cyclical fluctuations, while Bry and Boschan algorithm was applied to identify the turning points of the cycles. Morphological features of business cycles were examined. The analysis was supplemented by examining unemployment rate and price stability. Findings: The financial crisis resulted in the drop of the GDP growth and a periodical increase in unemployment. Nevertheless, in the 20 analysed years the unemployment rate tends to attain lower values, while prices become more stable. The last two economic cycles resulted in post-crisis rebound of the GDP growth. At the same time, the performance of the countries (except for outperforming Poland) becomes more and more correlated. Implications & Recommendations: The Visegrad Group was affected by the crisis and it is plausible that any future disturbances in the world economy might affect it again. Further research aiming at deeper understanding of how a particular country was affected could help mitigate a negative impact of future crises. Contribution & Value Added: The article helps to understand how the crisis affected the V4 economies in terms of the real GDP growth, unemployment rate and price stability. It indicates how the economic aggregates behaved before and after the crash.

Dating business cycles in India

PurposeThis paper aims to present a chronology of Indian business cycles in the post-reform period. In India, earlier, macroeconomic shocks were about droughts and oil prices. Economic reforms have led to an interplay of a market economy, financial globalisation and decisions of private firms to undertake investment and hold inventory. This has changed the working of the business cycle and has raised concerns about business-cycle stabilisation. In the backdrop of these developments, the macroeconomics research agenda requires foundations of measurement about business-cycle phenomena. One element of this is the identification of dates of business-cycle turning points.Design/methodology/approachThis paper uses the growth-cycle approach to present the chronology of business cycles. The paper uses the Christiano–Fitzgerald (CF) filter to extract the cyclical component and shows the robustness of the findings to the contemporary methods of cycle extraction. It then applies the Bry–Boschan algorithm to identify the dates of peaks and troughs.FindingsThe paper finds three periods of recession. The first recession was from 1999-Q4 to 2003-Q1; the second recession was from 2007-Q2 to 2009-Q3; and the third recession ran from 2011-Q2 till 2012-Q4. These results are robust to the choice of filter and to the choice of the business-cycle indicator. These dates suggest that, on average, expansions in India are 12 quarters in length and recessions run for 9 quarters. The paper offers evidence of change in the nature of cycles.Originality/valueDates of business-cycle turning points are a critical input for academic and policy work in macroeconomics. The paper offers robust estimation of the business-cycle turning points in the post-reform period using contemporary techniques of cycle extraction. This work helps lay the foundations for downstream macroeconomics research by academicians and policymakers.

Bull and Bear Phases: An Empirical Perusal of Indian Stock Market NSE and BSE Stock Markets?

In general, any one known to stock market is acquainted with the phenomenon of bull and bear phases, but whether the traders or investors put air to these phases while making a decision to buy, sell, or stay invested. The present paper attempts to identify and analyze the two most popular market phases, i.e. bull and bear, for better investment decisions with the use of Bry and Boschan Algorithm and time series data. Further, it seeks to analyze the distributional characteristics of the variances in stock returns and search evidence of asymmetries, if any, in volatility under different market conditions which may help to shed light on the bull and bear phases of Indian equity market. The study arrange for evidence that in bull markets, stock prices run far ahead of earnings and for fairly long periods of time. The paper indicates 12 bull and bear phases in the Sensex and Nifty during the sample period of 19 years with the associated factors responsible for the shift of bull and bear market phases. The results provide considerable support for the view that markets choose to ignore adverse possibilities and react with zest to favorable possibilities and market declines can partly be explained by increases in risk.

Bull and Bear Phases: An Empirical Perusal of Indian Stock Market NSE and BSE Stock Markets?

In general, any one known to stock market is acquainted with the phenomenon of bull and bear phases, but whether the traders or investors put air to these phases while making a decision to buy, sell, or stay invested. The present paper attempts to identify and analyze the two most popular market phases, i.e. bull and bear, for better investment decisions with the use of Bry and Boschan Algorithm and time series data. Further, it seeks to analyze the distributional characteristics of the variances in stock returns and search evidence of asymmetries, if any, in volatility under different market conditions which may help to shed light on the bull and bear phases of Indian equity market. The study arrange for evidence that in bull markets, stock prices run far ahead of earnings and for fairly long periods of time. The paper indicates 12 bull and bear phases in the Sensex and Nifty during the sample period of 19 years with the associated factors responsible for the shift of bull and bear market phases. The results provide considerable support for the view that markets choose to ignore adverse possibilities and react with zest to favorable possibilities and market declines can partly be explained by increases in risk.

Long-run bulls and bears

A central challenge in asset pricing is the weak connection between stock returns and observable economic fundamentals. We provide evidence that this connection is stronger than previously thought. We use a modified version of the Bry–Boschan algorithm to identify long-run swings in the stock market. We call these swings long-run bull and bear episodes. We find that there is a high correlation between stock returns and fundamentals across bull and bear episodes. This correlation is much higher than the analogous time-series correlations. We show that several asset pricing models cannot simultaneously account for the low time-series and high episode correlations.

Measuring U.S. business cycles: A comparison of two methods and two indicators of economic activities

We examine two issues in business cycle research. We first compare the performance of Hamilton's Markov-Switching (MS) model and the Bry and Boschan algorithm in replicating the US business cycle features. A number of studies, especially Harding and Pagan, have demonstrated that Hamilton's nonlinear MS model do not replicate business cycle features better than simpler linear models. Second, we compare the ability of the U.S. real GDP and a relatively new coincident index of four economic indicators, published by the Federal Reserve Bank of Philadelphia, in replicating features of the U.S. business cycle. We find that Hamilton's MS model is not robust when compared to the Bry and Boschan algorithm with respect to different sample periods and to different measures of real income. Second, we also find that a constructed quarterly version of the coincident index is slightly preferred over the real GDP.

Identifying and characterising the business cycle: the case of Morocco

This paper analyses the key features of the Moroccan business cycles during the period 1980:q1–2012:q1. The chronologies in classical and growth cycles, expansion and contraction phases, and full cycles in real gross domestic product are identified. Using the modified Bry–Boschan algorithm, eight business cycles were found and the ninth is not yet achieved. Then, the main features in these cycles are analysed by applying the method of Harding and Pagan [2002. “Dissecting the Cycle: A Methodological Investigation.” Journal of Monetary Economics 49: 365–381]. This method has shown that the Moroccan economy characterised by the average cumulative gain is more important than the cumulative loss and by the domination of expansion phases.

CYKLIČNOST PRŮMYSLOVÉ VÝROBY V KONTEXTU ČASOVÉ A FREKVENČNÍ DOMÉNY

Presented paper is focused on evaluation of cyclical behaviour of industrial production of the Czech Republic in time and frequency domain. Analyses in both domains are done in the context of commonly used detrending techniques necessary for obtaining growth business cycle. In the case of time domain analysis, the length of existing cycles is calculated on the basis of dating method application, i.e. right and left version of naive rules as well as Bry–Boschan algorithm. In the case of frequency domain, estimate of the spectra using periodogram and autoregressive process with optimum lag are used. In the time domain evaluation of the unity of the results of detrending techniques from the turning points identification is done as well. All analysis are done on the data of industrial production in the Czech Republic in 1996/Q1–2008/Q4.

Financial fluctuations in the Tunisian repressed market context: a Markov-switching–GARCH approach

Small open economies are not immune to financial shocks. Fluctuations arising there interest more and more decision makers as they influence their policies’ effectiveness. A common belief is that opening the capital account is the primary source of financial instability. In this article we show that even if a capital account is not previously opened in Tunisia, the investor sentiment plays the role of the transmission channel of financial fluctuations. On monthly data (2000:01–2010:03) we filter financial business cycles via the Hodrick–Prescott procedure. Also we establish their turning points in Tunisian, Moroccan and French markets using the Bry–Boschan algorithm. Thus we build the investor sentiment index in Tunisia. Then we use it for the estimation of the financial volatility through a Markov switching–GARCH model. We show that business financial cycles in Tunisia are partially synchronized with those in France and the Tunisian investor’s sentiment is a significant explicative variable of the financial volatility. Therefore, we recommend a financial stabilization policy based on agent’s expectations for better macroeconomic effectiveness policies.