Cyclical dynamics and co-movement of business, credit, and investment cycles: empirical evidence from India

The business cycle estimation and policy framework to monitor and control fluctuations in GDP is a well-known exercise carried out by various agencies internationally. However, the study of the financial cycle and its impact on the economy attracted the policymaker's attention in the aftermath of the 2008 Subprime crisis. Financial cycles analyze the state of the economy from the expansionary to contractionary phase. The expansion phase is characterized by increased economic activity and high growth rates. However, contractionary phases experienced a slowdown in economic activity. As proposed by Borio (2012), "self-reinforcing interactions between perceptions of value and risk, attitude towards risk and financing constraints which translate into booms followed by busts." Financial cycles are related to the trends in real bank credit, credit to GDP ratio, real equity prices, real effective exchange rate, and real house prices. Fluctuations in the stock market, bond markets, and foreign exchange markets are influenced by various economic factors such as policy-making, government intervention, speculation, and expectation. Hyman Minsky, in his "financial instability hypothesis" outlined three stages of instability. During the period of Hedge, the credit demand remains moderate due to the recession losses. Recovery leads to increased credit demand, asset price booms, and economic growth, creating an increased debt burden in the speculative stage. Finally, during the Ponzi stage, the increased debt burden led to financial stress, asset liquidation, and declining asset prices which resulted in a recession. Credit rise stimulates the demand for houses, causing asset price growth. As a result, increased mortgage value reduces the demand, reversing the process. These self-reinforcing interactions cause disruptions. (Borio and Disyatat, 2011) discussed a 'policy drift', where a prolonged period of low-interest rates encourages borrowings and subsequent demand for money, which results in increased prices of houses, shares, and other assets causing financial stress and obligations. Behra and Sharma (2019) explored the interactions between financial cycles and non-performing assets to examine uncertainties. There was a rise in non-performing assets. As of March 31, 2018, the total volume of gross NPA in the economy stands at Rs. 10.35 lakhs Crore. About 85% of these NPAs are from loans and advances of public sector banking. NPA of banks has expanded from 2.3% of total loans in 2008 to 9.3% in 2017, with it standing at 8.2% in March 2020. According to ICRA, gross and net NPAs were expected to increase to 10.1-10.6% and 3.1-3.2%, respectively, by March 2021. Simultaneously there was a decline in GDP at (2011-12) prices from 8.17% to 7.17% in 2017, then 6.12% in 2018 and 4.5% in Q2 (2019-20) from 5% in Q1. The GDP for the entire financial year (2020-21) contracted by -8%, causing a historic downfall in the Indian Economy attributed to the nationwide lockdown in response to the widespread coronavirus (Paul, 2018). This paper attempts to incorporate the Digital transformation index (DTI) to capture the technology's role in the credit creation process. It is important to highlight that the banking industry in India has changed tremendously in operations and service delivery mechanisms since 1991. The advancement of loan and credit dispersal has been significantly influenced by technological advancement and the wireless revolution. Kolodiziev et al. (2021) established that digital transformation has enhanced banking competitive capacity. Additionally, this paper aimed to determine the cyclical components of the GDP, Credit, and Investment cycle and employed SVAR analysis to examine the dynamic interplay among them. The structure of the paper is as follows: The section "Literature review" provides comprehensive literature on the nexus among these variables in India. Section "Data and variable construction" presented the data and the variables utilized. The section "Economic methodology" describes the econometric methodology. Section "Empirical analysis and results" presents the findings obtained from the estimation, followed by sections "Robustness" and "Discussions and policy implications", respectively, and concluded in the section "Conclusion".

Schumpeter (1911) focused on the importance of financial development in economic growth, especially in the R&D department and allowing new entries. Many studies such as McKinnon (1973), Goldsmith (1969), Gregorio (1999), Levine (1997), Arteta et al. (2001), Edison et al. (2002) have supported this view. Another aspect of the finance-growth nexus was the examination of cyclical fluctuations in macroeconomic variables. Several papers have focussed on the financial cycle in developed countries by researchers like Borio (2012), Claessens et al. (2011a, 2011b), Tobias Adrian and Shin (2010), and others. While business cycles have been highly discussed in the literature, financial cycles are still a work in progress and have recently gained attention. Business cycles are associated with financial cycles, and their theories helped us understand financial cycles. The dynamic interactions between business cycles and financial cycles are important for the estimation of recessions and recoveries. Claessens et al. (2011a, 2011b) explored the interactions and found a strong relationship between them in 44 countries, shaping recessions and recoveries. The findings also revealed the synchronization of output cycles with credit and house prices, while equity price cycles show less similarity. Rünstler (2016) explored the US and five major European countries, revealing that financial cycles are longer than business cycles. Yong and Zhang (2016) emphasized financial cycles have a significant role in business cycles in the USA, UK, China, and Japan. Financial cycle shocks drive major fluctuations in macroeconomic variables, particularly during times of financial instability. Jawadi et al. (2022) revealed that information from business cycles is useful in forecasting financial cycles, particularly during expansion in the USA from 1987 to 2016. Sanvi and Matheron (2005) explored the lack of a strong link between stock prices and real activity except in the USA, highlighting the importance of the financial cycle and business cycle in strengthening the effectiveness of monetary policy. Various methodologies have been employed to study the financial cycles, including the traditional method of Turning point analysis by Arthur and Mitchell (1946), Bry and Boschan (1971), and Harding and Pagan (2002), which identified longer and deeper financial cycles than business cycles. These were medium-term cycles, as explained in the literature. Financial variables have different frequencies and durations, necessitating the utilization of statistical filter methods such as the unobserved component model as employed earlier by Aikaman et al. (2010). Galati et al. (2016) observed that financial cycles, overall tend to be longer and have greater amplitudes than business cycles. Two commonly used methods for estimating cyclical behavior are the Hodrick-Prescott Filter and the bandpass filter. The HP filter, introduced by Hodrick and Prescott (1981), separates the series into trend and cycle components. Conversely, the BP filter, proposed by Christiano and Fitzerald (2003), functions as a two-sided moving average filter, effectively smoothing out fluctuations and underlying cycles and trends. In India, studies about financial cycles such as Behra and Sharma (2019) have identified the existence of the financial cycle by examining their main characteristics using three methods: turning point analysis, spectral analysis, and bandpass filters, along with the quarterly data on credit, equity prices, house prices, and real exchange rates. Aravalath (2020) with the Wavelet-based causality test, revealed that financial shocks lead to business cycles and that financial cycles are larger than business cycles during the period Q1:1991 to Q4: 2019. These findings coincide with those of Kumar et al. (2020). Additionally, Paramanik et al. (2021) indicated a strong interdependency between real and financial markets between the years 2003 and 2020, emphasizing the importance of economic uncertainty. Conversely, Saini et al.(2021) found that the business cycle leads to the credit cycle in India at both aggregate and sectoral levels. Furthermore, the duration of the business cycle was approximately 4 years, whereas the credit cycle duration was 3 years. The significance of global financial cycles has been investigated in various studies. Cerutti et al. (2017) explored the importance of global financial cycles in determining capital flows in 85 countries from Q1 1990 to Q4 2015. Through the utilization of panel regressions, national capital equations, and event studies, they revealed that global financial cycles are not important in understanding capital flows. however, their impact can be seen in other variables like credit and house prices. Similarly, Silvia and Hélène (2021) revealed that changes in monetary policy bring about developments in global financial variables, resulting in contractions in asset price, a decline in credit, a wider spread, and a downturn in capital flow globally. Consequently, the results indicate that US monetary policy serves as the key driver of global financial cycles. This research aimed to address a gap in the existing literature by examining the impact of DTI cycles on business and credit cycles. To the author's knowledge, no prior studies have integrated these variables, particularly in India. Given the significant role played by the public and private sectors in India's growing digitization, characterized by deep internet and telecom penetration, the number of mobile subscribers is taken as a proxy for DTI. Olczyk and Kuc-Czarnecka (2022) found a strong relationship between GDP and digital transformation. Additionally, this paper incorporated the analysis of money supply and investment cycles, exploring the impact on the business cycle. Any time series data comprises four components, with one of the most important being the cyclical component evident in time series data, such as GDP, credit, and investment. According to Hawtrey's monetary theory of trade cycle, business cycles are caused by the expansion and contraction of bank credit. During the period of credit expansion, prices rise, profit increases, and aggregate output grows, constituting a boom period. Conversely, when bank credit falls, prices fall, profit decreases, and total production declines. Moreover, Hayek (1943) suggested that business cycles arise from disequilibrium between actual and desired investments. The theories of Samuelson and hicks on trade cycles emphasized the interactions between the multiplier and accelerator principles. Thus, it is well documented in the literature that economies move in a wave-like manner. Accordingly, hypotheses were formulated in this study. H₁: Cyclical components are present in GDP, credit, and investment H2: Credit cycles (NFGBC) have a causal relationship with GDP cycles H3: Financial cycles have a causal relationship with GDP cycles H₁: DTI cycles have a causal relationship with GDP cycles HA: DTI cycles have a causal relationship with credit cycles

Data and variables: Annual data from 1980–2021 (mobile subscribers from 2000–2021) were used. Key variables include GDP (proxy for business cycle), non-food gross bank credit (NFGBC; proxy for credit cycle), domestic credit to private sector, gross fixed capital formation (GFCF; proxy for investment cycle), broad money (M3), BSE Sensex volatility, Brent crude oil prices (global financial cycle proxy), CPI, and mobile subscriptions per 100 people (proxy for digital transformation index, DTI). Data sources: World Bank, Reserve Bank of India, and BSE India. Cycle extraction: Cyclical components were extracted using the Hodrick–Prescott (HP) filter. For annual data, λ = 100 was employed (Backus and Patrick, 1992). The HP filter separates each series into trend and cycle components, balancing goodness of fit and smoothness via the smoothing parameter λ. Econometric approach: The study examined synchronization and co-movement through visual inspection of cycles, correlations, and simple OLS regressions between GDP cycles and each financial/investment indicator's cycles. Granger causality tests were conducted to detect directional relationships among cycles (notably GDP, NFGBC, private credit, GFCF, BSE, M3, Brent, and mobile subscribers). To analyze dynamic relationships and long-run interactions among GDP, NFGBC, and GFCF cycles, a Structural VAR (SVAR) model with Blanchard–Quah long-run restrictions was estimated. Optimal lag length (AIC, FPE, HQ, LR, SC) indicated two lags. Long-run pattern matrix coefficients, impulse response functions (IRFs), and forecast error variance decompositions (FEVD) were computed. Robustness diagnostics included SVAR residual serial correlation LM tests, Jarque–Bera normality tests, and heteroskedasticity tests with cross terms.

- Clear divergence between domestic and global financial cycles: Brent crude oil cycles showed weak synchronization with India's GDP cycle (correlation about 0.083; regression coefficient insignificant; Brent Granger-causes GDP only at 10% level). - Business and credit cycles are closely linked: GDP and NFGBC cycles are moderately positively correlated (~0.505). OLS suggests GDP cycle movements associate with about 25% of the variation in the credit cyclical component (R^2 ≈ 0.255; significant coefficients). Granger tests show bi-directional causality between GDP and NFGBC (p ≈ 0.0095 for GDP→NFGBC; p ≈ 0.0754 for NFGBC→GDP), indicating interdependence. - Private sector credit cycles show weak/inverse association with GDP cycles: correlation ≈ -0.204; regressions insignificant (R^2 ≈ 0.042), and no Granger causality. - Investment cycles (GFCF) exhibit pronounced fluctuations but weak contemporaneous linkage to GDP cycles: correlation ≈ 0.099; regressions insignificant (R^2 ≈ 0.010); no Granger causality. - Stock market cycles (BSE) have negligible linkage with GDP cycles: correlation ≈ 0.052; regressions insignificant (R^2 ≈ 0.003); no Granger causality. - Money supply (M3) moderately co-moves with GDP cycles: correlation ≈ 0.339; OLS indicates M3 explains about 11.5% of GDP cycle variation (significant at ≈ 5%); Granger causality not established at 5%. - Digital transformation proxy (mobile subscriptions) shows: weak link with GDP cycles (correlation ≈ 0.327; regression R^2 ≈ 0.107; coefficient not significant) but a notable, significant relationship with credit cycles (NFGBC): correlation ≈ 0.503; regression coefficient significant with R^2 ≈ 0.253; Granger causality from MS to NFGBC at ≈ 10% level (p ≈ 0.0537). - Cycle characteristics: GDP cycles recorded 8 peaks and 9 troughs; longest peak-to-peak spans ~9 years (1999–2007; 2010–2018). NFGBC had 6 peaks and 7 troughs; a 12-year cycle (1995–2006). GFCF had 10 peaks and 9 troughs; extended cycles 2011–2018 (~8 years). Other variables exhibit documented turning points aligned with major events (1991 BOP crisis, 2007–09 GFC, 2016 demonetization, COVID-19). - SVAR long-run pattern (GDP equation): GDP(-1) positive and highly significant (0.01861); GDP(-2) insignificant; GFCF(-1)=0.05133 (p=0.0076), GFCF(-2)=0.07203 (p<0.001) positive and significant; NFGBC(-1)=0.09682 and NFGBC(-2)=0.06417 both positive and highly significant (p<0.001). - SVAR IRFs: A GDP shock initially exerts negative effects on GFCF and NFGBC in the short term (e.g., second period impacts: ΔGFCF ≈ -0.0012; ΔNFGBC ≈ -0.0027), with mixed longer-run dynamics; GFCF shocks yield a slight positive effect on GDP (second period ≈ +0.0088) but negative on itself and NFGBC in the long run; NFGBC shocks are generally positive for GDP in the short run but show negative longer-run effects on GFCF and NFGBC. - Variance decomposition: GDP cycle forecast variance is largely explained by NFGBC in early periods (≈79% at period 1) with GDP’s own contribution rising over time (≈31% by period 5; ≈30.8% by period 10) and NFGBC declining to ≈63% by period 10; GFCF contribution to GDP variance remains small but positive. GFCF variance is predominantly explained by itself (≈86–94%). NFGBC variance is initially dominated by GDP (≈71% at period 1), decreasing toward ≈37% by period 10, with GFCF explaining ≈27–30% in later periods. - Robustness: SVAR residuals show no autocorrelation up to 3 lags; heteroskedasticity tests indicate homoskedasticity at 5%; normality is broadly acceptable except GDP component violating due to COVID-19 shock.

Findings corroborate prior evidence of tight interlinkages between business and credit cycles: credit availability and economic activity are intertwined, and regulating credit conditions could help mitigate business cycle fluctuations. Investment cycles showed weak links with GDP contemporaneously, suggesting complex determinants of investment beyond the variables explored. The significant correlation and predictive content of mobile subscriber cycles for credit cycles indicate that technological penetration and digital transformation affect credit creation and delivery mechanisms. Policymakers and financial institutions should monitor credit conditions and technological trends jointly to promote financial stability and to better anticipate cyclical turning points. The observed disconnect between domestic stock market cycles and GDP implies equity market fluctuations may be driven by factors other than real activity, limiting their utility as contemporaneous indicators for GDP. Limited influence of global financial cycles (proxied by Brent) on India’s GDP cycle underscores the dominance of domestic financial-credit dynamics over external price shocks in shaping cyclical GDP fluctuations.

This paper examines the interconnectedness of the business, credit, and investment cycles and the influence of domestic and global financial cycles on India’s business cycle using annual data (1980–2021). HP-filtered cycles reveal pronounced cyclical components in GDP, banking credit, and investment. Business and credit cycles exhibit close co-movement and bi-directional Granger causality. Investment cycles (GFCF) show strong internal volatility but weak links with GDP, while stock market cycles display negligible synchronization with GDP. Money supply changes partly explain GDP cycle movements. Global financial cycles, proxied by Brent oil prices, show weak synchronization with India’s GDP cycle. Incorporating mobile subscribers as a DTI proxy shows limited relation with GDP cycles but a significant association with credit cycles, explaining about 25% of NFGBC variation. SVAR results confirm long-run positive effects of past credit and investment cycles on GDP cycles, and variance decomposition attributes a major share of GDP cycle variance to NFGBC shocks. Robustness checks support the stability of findings. Future work could use broader ICT-based DTI measures and frequency-domain techniques (e.g., wavelets) to deepen understanding of technology–finance–real economy interactions.

- The proxy for digital transformation (DTI) is the number of mobile subscribers, a simple measure that may not capture the broader ICT landscape; authors note future work should employ richer ICT indicators. - Mobile subscriber data are available only from 2000–2021, shortening the time span for DTI-related analyses relative to other variables. - SVAR residual normality is violated for the GDP component due to the COVID-19 period, which may affect inference under normality assumptions; robustness checks indicate overall model stability. - Use of annual data and HP filter (λ=100) involves standard assumptions about trend–cycle separation and may affect identified cyclical properties.