This paper surveys research on corporate governance, with special attention to the importance of legal protection of investors and of ownership concentration in corporate governance systems around the world.

A Survey of Corporate Governance

This article surveys research on corporate governance, with special attention to the importance of legal protection of investors and of ownership concentration in corporate governance systems around the world. CORPORATE GOVERNANCE DEALS WITH the ways in which suppliers of finance to corporations assure themselves of getting a return on their investment. How do the suppliers of finance get managers to return some of the profits to them? How do they make sure that managers do not steal the capital they supply or invest it in bad projects? How do suppliers of finance control managers? At first glance, it is not entirely obvious why the suppliers of capital get anything back. After all, they part with their money, and have little to contribute to the enterprise afterward. The professional managers or entrepreneurs who run the firms might as well abscond with the money. Although they sometimes do, usually they do not. Most advanced market economies have solved the problem of corporate governance at least reasonably well, in that they have assured the flows of enormous amounts of capital to firms, and actual repatriation of profits to the providers of finance. But this does not imply that they have solved the corporate governance problem perfectly, or that the corporate governance mechanisms cannot be improved. In fact, the subject of corporate governance is of enormous practical impor

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http://homepage.ntu.edu.tw/~ntuperc/docs/publication/2002_25_Lin.pdf

Unit root tests in panel data: asymptotic and finite-sample properties

Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?

This paper introduces an ARCH model (exponential ARCH) that (1) allows correlation between returns and volatility innovations (an important feature of stock market volatility changes), (2) eliminates the need for inequality constraints on parameters, and (3) allows for a straightforward interpretation of the "persistence" of shocks to volatility. In the above respects, it is an improvement over the widely-used GARCH model. The model is applied to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987. Copyright 1991 by The Econometric Society.

Conditional heteroskedasticity in asset returns: a new approach

https://repository.upenn.edu/cgi/viewcontent.cgi?article=1056&context=fnce_papers

Expected stock returns and volatility

This paper analyzes the relation of stock volatility with real and nominal macroeconomic volatility, economic activity, financial leverage, and stock trading activity using monthly data from 1857 to 1987. An important fact, previously noted by Officer (1973), is that stock return variability was unusually high during the 1929-1939 Great Depression. While aggregate leverage is significantly correlated with volatility, it explains a relatively small part of the movements in stock volatility. The amplitude of the fluctuations in aggregate stock volatility is difficult to explain using simple models of stock valuation, especially during the Great Depression. ESTIMATES OF THE STANDARD deviation of monthly stock returns vary from two to twenty percent per month during the 1857-1987 period. Tests for whether differences this large could be attributable to estimation error strongly reject the hypothesis of constant variance. Large changes in the ex ante volatility of market returns have important negative effects on risk-averse investors. Moreover, changes in the level of market volatility can have important effects on capital investment, consumption, and other business cycle variables. This raises the question of why stock volatility changes so much over time. Many researchers have studied movements in aggregate stock market volatility. Officer (1973) relates these changes to the volatility of macroeconomic variables. Black (1976) and Christie (1982) argue that financial leverage partly explains this phenomenon. Recently, there have been many attempts to relate changes in stock market volatility to changes in expected returns to stocks, including Merton (1980), Pindyck (1984), Poterba and Summers (1986), French, Schwert, and Stambaugh (1987), Bollerslev, Engle, and Wooldridge (1988), and Abel (1988). Mascaro and Meltzer (1983) and Lauterbach (1989) find that macroeconomic volatility is related to interest rates. Shiller (1981a,b) argues that the level of stock market volatility is too high relative to the ex post variability of dividends. In present value models such as Shiller's, a change in the volatility of either future cash flows or discount rates

/pdf/why-does-stock-market-volatility-change-over-time-1tlvx2plat.pdf

Why Does Stock Market Volatility Change Over Time

Asset returns and inflation

Recent work by Said and Dickey (1984 ,1985) , Phillips (1987), and Phillips and Perron(1988) examines tests for unit roots in the autoregressive part of mixed autoregressive-integrated-moving average (ARIHA) models (tests for stationarity). Monte Carlo experiments show that these unit root tests have different finite sample distributions than the unit root tests developed by Fuller(1976) and Dickey and Fuller (1979, l981) for autoregressive processes. In particular, the tests developed by Philllps (1987) and Phillips and Perron (1988) seem more sensitive to model misspeciflcation than the high order autoregressive approximation suggested by Said and Diekey(1984).

https://papers.ssrn.com/sol3/Delivery.cfm/nber_t0073.pdf?abstractid=254517&mirid=1

Tests for Unit Roots: a Monte Carlo Investigation

This paper compares several statistical models for monthly stock return volatility. The focus is on U.S. data from 1834-19:5 because the post-1926 data have been analyzed in more detail by others. Also, the Great Depression had levels of stock volatility that are inconsistent with stationary models for conditional heteroskedasticity, We show the importance of nonlinearities in stock return behavior that are not captured by conventional ARCH or GARCH models. We also show the nonstationariry of stock volatility, even over the 1834-1925 period.

G. William Schwert

Papers

Expected stock returns and volatility

Why Does Stock Market Volatility Change Over Time

Asset returns and inflation

Tests for Unit Roots: a Monte Carlo Investigation

Alternative Models for Conditional Stock Volatility