Date of Submission


Date of Award


Institute Name (Publisher)

Indian Statistical Institute

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

Subject Name

Quantitative Economics


Economic Research Unit (ERU-Kolkata)


Sarkar, Nityananda (ERU-Kolkata; ISI)

Abstract (Summary of the Work)

The first chapter of this thesis begins with a brief review of the existing literature on empirical studies on stock returns, especially those in the context of the relationship between risk and return, at both univariate and multivariate levels. In the next section, studies on the relationship between stock return and monetary policy are reviewed. The motivation of this work is discussed in Section 1.4. Finally, the format of the thesis is given in Section 1.5.A Brief Review of the Literature on Risk-Return Relationship-Both Univariate and Multivariate Cases. In this section, we first present a brief review of this literature based on univariate analysis of stock returns. This is followed by the same considering the multivariate set-up where returns on several stock markets are modelled together. Thereafter, we briefly mention about some studies where returns are modelled in terms of some exogenous instruments of monetary policy. Univariate analysis of returns. In the literature on financial economics, one of the most important relationships studied is the one between risk and return. In fact, investors are assumed to evaluate the performance of their investments in terms of two summary statistics that represent the expected gains of a portfolio and its expected risk as determined from asset volatility. Since the seminal paper by Markowitz (1959), the capital asset pricing model (CAPM) has become an important tool in finance for assessment of cost of capital, portfolio performance, portfolio diversification, valuing instruments, and choosing portfolio strategy. Building on Markowitz’s work, Sharp (1964) and Black (1972) developed some other versions of CAPM that can be empirically tested. It is well-known that the CAPM assumes the risk to be constant. However, this assumption of constant risk was found to be very restrictive, particularly in the context of financial time series. In fact, it has been recognized as early as in 1960’s by Mandelbrot (1963), and Fama (1965), that uncertainty in speculative prices, as measured by variances and covariances, changes through time. But, it was not until the introduction of what is now known as Modern Financial Econometrics that applied researchers in financial and monetary economies started explicitly modelling variation over time in second-order moment. To that end, in his seminal paper in 1982, Engle introduced the autoregressive conditional heteroscedastic (ARCH) model which allows the conditional variance to change overtime as a function of past errors keeping the unconditional variance constant. It has been observed that this model captures many empirically observed temporal behaviours like the thick tail distribution and volatility clustering of many economic and financial variables (see, Bollerslev et al., (1992), Bera and Higgins (1993), Bollerslev et al., (1994), Shephard (1996) and Gourieroux (1997), for excellent surveys on ARCH/GARCH models and its various generalizations). Subsequently, this model was generalized by Bollerslev (1986), and this is called the generalized autoregressive conditional heteroscedasticity (GARCH) model. One important point to be noted while studying the risk-return relationship is that, as the degree of uncertainty in asset returns varies over time, the compensation required by risk-averse investors for holding these assets must not only be time-varying but also be such that investors are rewarded for taking additional risk by ensuring a higher expected return.


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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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