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Purpose – The purpose of this paper is to empirically estimate industry beta in Indian stock market with three alternative models and compare the accuracy of forecasting error to find the most suitable model for time-varying beta estimation. Design/methodology/approach – The paper applies the standard regression model, Kalman filter model, other statistical approaches and secondary material. Findings – The paper finds that the existence of dynamic beta in Indian market. The results also indicate systematic risk or beta of Indian industries is susceptible to the global economic effect. Finally, the Kalman filter generates the lower forecasting error compared to the other method for almost all the industries. Practical implications – The accurate estimation of beta which is a measure of systematic risk helps investors to make investment decision easier. The implication of this result is important for finance practitioners such as portfolio managers, investment advisors and security analysts. This study will help to determine the country risk with respect to the global index and analyze the global financial market integration effect on India. Originality/value – This paper reliably estimate industry portfolio beta for India. The time-varying beta is estimated using Kalman filter method which is rarely applied in Indian literature. This paper contributes by extending the knowledge of existing literature by introducing a new data set with Indian data which is not affected by the “data snooping” bias. This study will also help to determine the country risk with respect to the global index and analyze the global financial market integration effect on India.

This paper examines the effects of size, value and momentum on the cross-sectional relation between expected returns and risk in the Indian stock market. We find that the conditional Carhart four-factor model empirically describes the variation of crosssection of return better than the unconditional model. When size, book-to-market and momentum effects are controlled in the conditional model, the positive relation of market beta, book-to-market and momentum with expected returns remains economically and statistically significant. However, this evidence is found to be subject to characteristics of test portfolios. The expected returns are sensitive to changes in predictive macroeconomic variables. IntroductionThe Capital Asset Pricing Model (CAPM) developed by Sharpe and Lintner has become an important tool in finance for the assessment of cost of capital, valuing investments, portfolio performance measurement and diversification among others. However, recent empirical research in finance focused on firm-specific characteristic or anomalies that explain the cross-section of expected return better than the traditional asset pricing models. Fama and French (1993) propose a three-factor asset pricing model to include small-minus-big size (SMB) and high-minus-low book-to-market (HML) risk factors with the traditional CAPM to capture size and value effect, respectively. Motivated by the Fama-French three-factor model, Carhart (1997) further improved the model by including winners-minus-losers momentum (WML) factor to capture momentum effect with the three-factor model. In light of the recent developments and issues on asset pricing, there are many empirical researches on size, value and momentum effects in the developed stock market especially in the US. Despite the facts that emerging stock markets constitute a significant share of the world equity market, it has received less attention in this regard. There is a discrepancy in the domestic investor's participation rate, equity premium and risk in emerging markets against developed market like the US (Bekaert and Harvey 2013; Giannetti and Koskinen 2010). As the capital market of these markets has become more integrated with regional and world markets, it is necessary to provide academics and practitioners a better understanding on the cross-sectional behaviour of stock returns in these markets. This study is an attempt to address this issue in the Indian context. Indian markets have experienced a phenomenal growth in the last two decades. The consequence

The concept of time scales is essential for modeling financial decisions. This paper investigates time-frequency relationships across time scales between stock market returns, crude oil prices and exchange rates by applying wavelet analysis technique over the period 1999 to 2021. We find evidence of several strong co-movements between oil price and stock market and between oil price and foreign exchange rate in India. Each of these associations is linked with some important macroeconomic events. This implies economic shocks in developed market have a spillover effect on Indian market. The phase relationships indicate stock returns are in phase with oil prices and exchange rates are in out of phase with oil prices. We find that the impact of volatility at lower scale has a short term effect on the variables. Further, the wavelet coherency at high scale has slower changes with long term effect on the relationship between the variables of our interest. These results are useful for investors aiming specific time horizon of their investment and preferences, for portfolio managers and in risk assessment. Understanding the leading and lagging relationships will also help in business cycle based investing by detecting the subsequent business cycle fluctuations and forecasting the trend.

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