Indian Equity Market Research Articles

Dynamic Linkages Between U.S and Indian Equity Markets: An Empirical Study

In this study, we investigate the dynamic linkages between US and Indian equity markets by examining the daily Total Return Index values of NIFTY 50 and NASDAQ Composite over a decade ending October 2024. While the indices are not found to be cointegrated, suggesting potential long-term diversification benefits, they exhibit significant short-term correlations. Through various statistical analyses including regression, correlation, and variance tests, we find strong positive associations between the markets, with NASDAQ Composite movements explaining approximately 89% of variations in NIFTY 50 Total Return Index (TRI). The study reveals distinct volatility patterns, with the Indian market showing higher price fluctuations but stronger absolute growth compared to its US counterpart. Despite the strong correlation, both markets maintain unique characteristics influenced by their respective economic fundamentals. The findings demonstrate the evolving nature of market integration between developed and emerging economies, offering valuable insights for international portfolio diversification strategies and risk management in an increasingly interconnected global financial system. Keywords: Market Integration, Volatility Spillover, Portfolio Diversification, Stock Market Returns, Emerging Markets, Cross-market Correlation

Time-varying aggregate tail risk and cross-section of stock returns: Indian evidence

This article examines whether the time-varying aggregate tail risk is priced in the cross-section of average returns in the Indian equity market. The results show that the tail risk beta sorted portfolio returns increase monotonically with an increase in their tail risk sensitivity. More importantly, the Indian stocks provide economically and statistically significant risk premium for the tail beta risk factor. The findings remain robust after controlling for the Fama–French and momentum risk factors, and other individual characteristics governing tail behaviour of stocks. The study also provides evidence that institutional ownership and firm size affect tail risk premium.

Assessing the Impact of Federal Reserve Policies on Equity Market Valuations: An Instrumental Variables Approach

This study investigates the impact of Central Bank interventions on the pricing dynamics of select stock markets. The research utilizes the instrumental variables three-stage least square (3SLS) model approach. It analyses the effects of variations in the Federal Reserve’s balance sheet size across three distinct intervention scenarios: the 2008–2013 Great Recession, the 2020–2021 COVID-19 pandemic periods, and an overarching analysis spanning these timelines. Our methodology includes estimations of the Seemingly Unrelated Regression Equations (SURE), and the results are robust under the two-step Generalized Method of Moments (GMM). Our findings indicate that changes in the size of the Fed’s balance sheet correlate significantly with the pricing of principal U.S. equity market indices. This correlation reflects a time-dependent effect emanating from the Fed’s balance sheet expansion, marking a growing divergence between the adaptability of pricing mechanisms in equity and debt markets. Notably, the Federal Reserve’s interventions during the COVID-19 crisis are associated with an increase of approximately 0.0403 basis points per billion in treasury yields. This research makes a significant contribution to the understanding of financial asset pricing, particularly by elucidating the extent to which interventions in government debt securities engender price distortions in certain equity markets.

Assessing the Impact of the ECB’s Unconventional Monetary Policy on the European Stock Markets

This study assesses the effects of the European Central Bank’s (ECB) unconventional monetary policy (UMP) on the prices of selected European stock market indices during the European sovereign debt (2010–2012) and the COVID-19 pandemic (2020–2022) crises interventions. This research employs the instrumental variables (IV) two-stage least squares (2SLS) model approach to evaluate the effects of changes in the size of the ECB’s balance sheet on the pricing of key equity market indices in Europe. The results of this study suggest that the ECB’s asset value expansion had the opposite statistically significant effects on the European stock market indices’ prices between the interventions. That is, an increase in the ECB’s balance sheet size was associated with a decrease in the prices of the indices during the sovereign debt crisis and with a rise during the COVID-19 pandemic. This research pinpoints the price sensitivity of each of the European equity indices to the ECB’s UMP and determines the different outcomes of the ECB’s quantitative easing policy between the interventions.

Volatility in the Equity Stock Market in India from 2019 to 2024: An Empirical Analysis

This research investigates the volatility and fluctuations in the Indian equity stock market from 2019 to 2024, a period characterized by unprecedented global and domestic challenges. The Indian stock market, like many emerging markets, experienced heightened volatility due to a confluence of events, including the COVID-19 pandemic, geopolitical tensions, economic reforms, and shifts in global financial conditions. The study employs advanced econometric models, including GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and event study analyses, to capture the time-varying nature of market volatility and to identify the key factors driving these fluctuations. Our findings reveal that the COVID-19 pandemic triggered the most significant spikes in volatility, particularly during the early months of 2020, as uncertainty regarding the pandemic's impact on the economy led to sharp market corrections and investor panic. Additionally, the study highlights the influence of macroeconomic variables such as GDP growth, inflation, and monetary policy changes on market behavior. The paper also explores the role of investor sentiment and market liquidity in exacerbating volatility during crisis periods. The results provide valuable insights into the dynamics of the Indian equity market, offering implications for investors seeking to manage risk and for policymakers aiming to enhance market stability. The study underscores the need for robust risk management strategies and proactive regulatory measures to mitigate the adverse effects of such volatility, especially in an increasingly interconnected global financial environment. Keywords: Equity Stock Market, Volatility, India, COVID-19, Economic Reforms, 2019-2024

Dynamic Returns Connectedness: Portfolio Hedging Implications During the COVID‐19 Pandemic and the Russia–Ukraine War

ABSTRACTWe apply a Time‐Varying Parameter Vector Auto Regressive (TVP‐VAR) connectedness approach on global assets to investigate time‐varying dynamic connectedness, portfolio performance, and hedge effectiveness during COVID‐19 and the Russia–Ukraine war. With increased connectedness and the changing role of energy and soft commodities during these two events, we find the minimum correlation (connectedness) portfolio performing better during COVID‐19 and the Russia–Ukraine war and that cumulative returns of portfolios are higher during COVID‐19. Additionally, we find varying (stable) hedge effectiveness of equity market indices and soft commodities (cryptocurrencies). This paper provides specific insights to investors about using optimal portfolios and hedging during pandemics and military conflicts.

Economic policy uncertainty and Indian equity sectors: a quantile regression approach

PurposeThis paper aims to investigate the effect of economic policy uncertainty (EPU) shocks on Indian equity market sectors. The effect of domestic (Indian) and foreign (USA) EPU shocks is examined on ten major Bombay Stock Exchange sectors.Design/methodology/approachThe study uses data covering the period from September 2005 to July 2023 and uses the methodology of quantile regression to investigate the heterogenous response of stock market sectors under diverse market conditions explained through the analysis of conditional quantiles distribution.FindingsThe results demonstrate that domestic and foreign EPU shocks negatively affect most of the sectors in bearish market conditions. Industrials, commodities, utilities, consumer discretionary and financial services are the most affected sectors by domestic EPU. However, the information technology sector is found to be immune to domestic EPU shocks but negatively affected by foreign EPU shocks. On the other hand, energy, financial services and fast-moving consumer goods sectors are found to be immune to foreign EPU shocks but are negatively affected by domestic EPU shocks.Practical implicationsUnderstanding the heterogeneous response of different sectors to EPU shocks could help investors and portfolio managers identify portfolio diversification opportunities.Originality/valueThis study makes an inaugural attempt to examine the responses of Indian stock market sectors to domestic and foreign EPU shocks using the approach of quantile regression and unveils the previously unexamined diverse reactions of Indian stock market sectors to EPU shocks originating from both India and USA.

Testing the Adaptive Market Hypothesis and Time-Varying Efficiency in the Indian Equity Market

The study examines the adaptive market hypothesis (AMH) as an evolutionary principle of the alternative efficient market hypothesis in the Indian stock market (Sensex and Nifty50) on the daily return from April 2014 to May 2020. Based on AMH, investors behave, learn, and adapt to market conditions. This distinction of dynamic market conditions is divided into bull and bear market classifications. We apply three variations of the variance ratio test and the returns have been whitened using the Autoregressive model with generalized autoregressive conditional heteroskedasticity (AR-GARCH) approach to examine the nonlinear predictability test. Further, we evaluate a fixed-length subsample window framework to detect the time-varying predictability and examine whether market conditions affect stock return predictability and market condition. The study confirms inefficient market behaviour during crises, fear, panic, macroeconomic events and each market adapts differently to certain market conditions. Furthermore, the return series exhibits significant periods of efficiency and inefficiency consistent with the adaptive market hypothesis. The evidence of our findings sheds light on efficient market behaviour in dynamic market conditions and market environments for researchers, investors in investment strategies and policy intervention in risk containment measures and control market manipulation.

Geopolitical risks and tourism industry interactions: Evidence from tokens and equity markets

Geopolitical risk can have an impact on investment behavior and rates of return. We explore the time-varying relation between the daily geopolitical risks index (GPRD) and tourism market. We use tourism tokens (TTI) and tourism equity market indices (WHRL) as proxies of the tourism industry. Results from DCC-GARCH analysis depict weak co-movements between GPRD-TTI and GPRD-WHRL pairs. However, concerning time-varying Granger causality, the GPRD-WHRL pair exhibited longer periods of a significant relationship along with bidirectional causality during the Russia-Ukraine war, whereas the COVID-19 pandemic’s effect was weak in both pairs. Spillovers identified through Q-VAR analysis revealed more pronounced effects of GPRD on each variable during both market events, albeit with a more apparent and stronger influence on WHRL. These findings suggest that the cryptocurrency market may not consistently and effectively incorporate geopolitical risk factors, whereas equity markets demonstrate a greater capacity to capture and respond to such risk factors.

Dynamic connectedness, portfolio performance, and hedging effectiveness of the hydrogen economy, renewable energy, equity, and commodity markets: Insights from the COVID-19 pandemic and the Russia-Ukraine war

This study employs a Time-Varying Parameter Vector Auto Regressive (TVP-VAR) connectedness approach to investigate the dynamic interconnections, portfolio performance, and hedging effectiveness across hydrogen economy, renewable energy markets, equities, and energy commodities amidst the backdrop of the COVID-19 pandemic and the Russia-Ukraine conflict. Our findings reveal a consistent level of average connectedness during both black swan events, with dynamic connectedness reaching its peak during the COVID-19 crisis. While some assets (hydrogen economy index, renewable, solar and clean technology index) persistently function as net transmitters, the others (geothermal index, crude oil, natural gas, biofuel, and gold) consistently act as net receivers. We also find that the role of equity indices (wind energy, fuel cell, FTSE_100, and S&P 500) as net transmitters or receivers changes over time. The network plot results show that the hydrogen economy typically functions as shock transmitters for equity market indices and commodities. Portfolio analysis underscores the superior risk-adjusted performance of the minimum variance (connectedness) portfolio during the COVID-19 crisis in comparison to the Russia-Ukraine conflict. Furthermore, our examination of effective hedge and portfolio strategies demonstrates that managed collateralized portfolio weights of hydrogen economy and renewable energy assets (equities) in the minimum variance portfolio increase (decrease) from 7% to 12% (53%–41%) during the COVID-19 crisis, and subsequently decline (rise) further to 6% (53%) amidst the Russia-Ukraine conflict. These findings suggest a preference for hydrogen economy and renewable energy assets as hedging instruments in health-related financial crises over crises precipitated by military conflicts.

Forecasting conditional volatility based on hybrid GARCH-type models with long memory, regime switching, leverage effect and heavy-tail: Further evidence from equity market

The properties of clustering, long memory, switching regime, leverage effect and heavy tail in volatility dynamic behavior are induced by important stylized facts in financial markets. There is still a controversy whether incorporating these properties could improve the modelling and forecasting performance of volatility. We construct hybrid volatility models via three perspectives including short memory, long memory and Markov switching GARCH with leverage effect and heavy tail, and empirically compare their performance of in-sample estimation, out-of-sample forecast and risk measurement based on trading data of Chinese equity market index. The out-of-sample forecast results indicate that the FIEGARCH model with innovation distribution of GED outperforms the competing models, and the backtesting results of VaR and ES confirm that this model performs well in the application of risk measurement.

Macroeconomic momentum and cross-sectional equity market indices

Momentum is a well-known and studied artefact of financial markets. In this paper, we investigate whether momentum in a country’s macroeconomic variables is related to the future performance of equities in that country. We find that the past economic trends of a country’s fundamentals are positively associated with the equity market index returns. Based on that, an economic momentum portfolio of buying (selling) equity index in countries with relatively strong (weak) economic past trends exhibits an annualised Sharpe ratio of 0.87. The economic momentum portfolio outperforms benchmarks regarding rewards to variability and maximum drawdown and yields an annualised alpha of 3.72%, leaving 95% of the returns unexplained by the benchmarks.

Global uncertainty and the spillover of tail risk between green and Islamic markets: A time-frequency domain approach with portfolio implications

This study first calculates the left tail risks in the renewable energy, green, sustainable, Islamic equity and Islamic bond markets using CAViaR models. Then, applying TVP-VAR based connectedness method it estimates various measures of dynamic connectedness and spillover of the tail risks at short, medium, and long term. Furthermore, through wavelet coherence analysis it examines how various global uncertainties impact such connectedness in time and frequency domain. Lastly, it estimates hedging effectiveness, optimal portfolio weights and Sharpe Ratios to provide practical implications for the market participants. The results show that the tail risk connectedness and spillover are mainly driven by the short-term dynamics emphasizing the importance of short-lived noises in the transmission of down-side risks. Usually, the connectedness of the tail risks is higher during the period of COVID-19 and Russia-Ukraine war. Though the tail risks of Islamic bond and developing Islamic equity markets are mostly isolated in the full sample showing hedging potential of these markets, the network analysis shows their increased connectedness with other markets during the period of COVID-19 and Russia-Ukraine war. In general, mature market indices such as Dow Jones Sustainable World and MSCI global environment indices, Dow Jones Islamic World, US and UK market indices are consistently net contributors/transmitters of tail risk shocks while Islamic bond and developing Islamic equity market indices are net receivers of shocks when they are connected to the network. Two uncertainty indices, that follow volatility related to gold and US dollar, demonstrate hedging potential and predictive power on the connectedness. Dow Jones Islamic World market index usually carries significant weights in optimal portfolio allocations while Islamic bonds from GCC countries have higher Sharpe Ratios indicating their promise as instruments for risk-adjusted profit.

Financial cycle comovement with monetary and macroprudential policy and global factors: Evidence from India

This paper assesses the interaction of the financial cycle with domestic and global factors. We assess the comovement of monetary and macroprudential policy stance towards financial cycle phases in India and analyze the external influence of two major global economies, the US and China, from 1990 to 2021. To measure the financial cycle, we use credit and equity prices derived from the low-frequency decomposition of wavelet analysis. The analysis suggests that the amplitude of the downturn phase is much smaller than the upswing phase, suggesting the downturn phase of the financial cycle is considerably milder in India. Results indicate that the procyclicality of monetary policy and macroprudential regulations dominates throughout the upward phase of the financial cycle. Further, the interest rate spectrum, short, intermediate, and extended-term rates are discovered to move together. Also, the monetary and macroprudential policies complemented one another throughout the sample period, indicating the effectiveness of these policies in dampening financial imbalances. Based on wavelet coherency analysis, the results of global factors indicate that India shares a long and stable credit relationship with the US. This credit relationship has weakened with China in post-global financial crisis. In comparison, the Indian equity market displays a strong relationship with the US and the Chinese equity markets only during the financial crisis and remains insulated during normal periods. These results thus suggest that in an emerging market economy like India, credit growth in the US creates significant long-term effects, whereas only during crisis periods do the equity markets of the US and China create financial instability in India.

Forecasting of P/E Ratio for the Indian Equity Market Stock Index NIFTY 50 Using Neural Networks

The ratio of present price of an index to its earnings is known as its price to earnings ratio denoted by P/E ratio. A high P/E means that an index’s price is high relative to earnings and overvalued. Its low value means that price is low relative to earnings and undervalued. A potential investor prefers an index with low P/E ratio. Therefore, the movement of the P/E ratio plays a crucial role in understanding the behaviour of the stock market. In this paper the modelling of the P/E ratio for the Indian equity market stock index NIFTY 50 using NNAR, MLP and ELM neural networks models and the traditional ARIMA model with Box-Jenkin’s method is carried out. It is found that MLP and NNAR neural networks models performed better than that of ARIMA model.

A wavelet analysis of investing in cryptocurrencies in the Indian stock market

PurposeThis study is designed to examine the dynamic interrelationships between four cryptocurrencies (Bitcoin, Ethereum, Dogecoin and Cardano) and the Indian equity market. Additionally, the study seeks to investigate the potential safe haven, hedge and diversification uses of these digital currencies within the Indian equity market.Design/methodology/approachThis study employs the wavelet approach to examine the time-varying volatility of the studied assets and the lead-lag relationship between stocks and cryptocurrencies. The authors execute the entire analysis using daily data from 1st October 2017 to 30th September 2023.FindingsThe result of the study shows that financial distress due to the pandemic and the Russian invasion of Ukraine have a negative effect on the Indian equities and cryptocurrency markets, escalating their price volatility. Also, the connectedness between the returns of stock and digital currency exhibits a strong positive relationship during periods of financial distress. Additionally, cryptocurrencies serve as a tool of diversification or hedging in the Indian equities markets during normal financial circumstances, but they do not serve as a diversifier or safe haven during periods of financial turmoil.Originality/valueThis study contributes to understanding the relationship between the Indian equity market and four cryptocurrencies using wavelet techniques in the time and frequency domains, considering both normal and crisis times. This can offer valuable insights into the potential of cryptocurrencies inside the Indian equities markets, mainly with respect to varying financial conditions and investment horizons.

Can Cryptocurrencies Provide Better Diversification Benefits? Evidence from the Indian Stock Market

Cryptocurrency has recently emerged as a financial asset among policymakers, investors and academics as a new alternative asset in the financial landscape. This research intends to empirically evaluate the safe haven, diversification and hedging potentials of digital currencies against the Indian equity market during different time frames. Four cryptocurrencies (Bitcoin, Ethereum, Binance Coin and Ripple) and Nifty 50 index data have been collected on a daily basis, from 26 July 2017 to 31 August 2023, for this purpose. Using wavelet-based methods, the study has discovered higher volatility in Nifty 50 and cryptocurrency prices during the crisis and stronger co-movement between pairs of equities and cryptocurrencies. Furthermore, the study finds that, under normal economic conditions, cryptocurrency can hedge the Indian stock market over short-term, medium-term and long-term investment horizons. However, investing in cryptocurrencies in the Indian stock portfolio for the short term does not give any safe haven or diversification advantages during times of economic crisis. Finally, we anticipate that the findings of our study will provide valuable insights into the potential usage of cryptocurrencies in the Indian stock market, both in stable and turbulent economic conditions. JEL Codes: G01, G41, N2, P34

Risk-Return Spectrum of Investment for Going Green: Evidence from Indian Equity Market

Risk-Return Spectrum of Investment for Going Green: Evidence from Indian Equity Market

Pandemic's Ripple Effect: Exploring Dynamic Connectedness of Indian Equity and Commodity Markets

This research investigates volatility spillovers between the Indian equity markets and commodity futures markets. The Nifty 50 index is chosen as a representative of the equity market, based on market capitalization, while Crude Oil, Copper, Zinc, Gold, and Silver are selected to represent the commodity market in India. The selection of each commodity is grounded in considerations of liquidity and physical market size. This research investigates how the volatility in the Indian stock market interacted with the volatility of five major commodity markets throughout the COVID-19 pandemic. In order to ascertain the direction of information flow, the study employs the Granger causality approach. The research investigates how volatility transmits from the Indian equity market to various commodity markets. Employing methodologies inspired by Diebold and Yilmaz (2012) for Time Varying Parameter and Vector Autoregression (TVP VAR) analysis, the study explores these connections. To further delve into the dynamic relationship between these markets, wavelet coherence analysis is also utilized. This methodologically robust approach goes beyond traditional correlation analysis by deconstructing the co-movements across different time scales, offering a more nuanced understanding of the complex relationships and potential transmission mechanisms at play during the unprecedented period of the COVID-19 pandemic. By analyzing the frequency-dependent interactions between these markets, the study aims to shed light on how economic shocks and market fluctuations in one sector might propagate and impact the other, providing valuable insights for investors, policymakers, and researchers alike.

Data Mining in Indian Equity Markets: building low Risk, Market beating Portfolios

Over the last five decades, business academics have identified over 300 determinants that potentially influence stock returns. However, we still do not know whether all return determinants are equally important, or whether there is a smaller set of determinants that has a disproportionately larger influence on stock returns. Can mining historical data help us find this smaller set of return determinants that has a disproportionately higher influence on stock returns? Using historical data from the Indian market, we build a large database of investments with more than 74,000 investments spread over a period of 132 months. From this database, using “association rule mining” method, we are able to mine a strong set of “association rules” that point to a smaller set of “return determinants” that are seen more frequently in investments that beat index returns. From a pool of thirty-seven return determinants, using “association rule mining”, we were able to find out a small set of key return determinants that are seen most frequently in investments that beat index returns in India. Portfolios created from these “association rules” have a portfolio risk lower than the market risk and provide index-beating returns. “Out-of-sample” portfolios created using these association rules have portfolio “Beta” less than one and provide returns that beat the market returns by a significant margin for all holding periods in the Indian market. Through this paper, we demonstrate how portfolio managers can mine “association rules” and build portfolios without any limits on the number of factors that can be included in the screening process.