计量经济分析 = Econometric analysis

We present a simple overlapping generations model of an asset market in which irrational noise traders with erroneous stochastic beliefs both affect prices and earn higher expected returns. The unpredictability of noise traders' beliefs creates a risk in the price of the asset that deters rational arbitrageurs from aggressively betting against them. As a result, prices can diverge significantly from fundamental values even in the absence of fundamental risk. Moreover, bearing a disproportionate amount of risk that they themselves create enables noise traders to earn a higher expected return than rational investors do. The model sheds light on a number of financial anomalies, including the excess volatility of asset prices, the mean reversion of stock returns, the underpricing of closed-end mutual funds, and the Mehra-Prescott equity premium puzzle.

https://ms.mcmaster.ca/~grasselli/DeLongShleiferSummersWaldmann90.pdf

Noise Trader Risk in Financial Markets

ARCH modeling in finance: A review of the theory and empirical evidence

A voluminous literature has emerged for modeling the temporal dependencies in financial market volatility using ARCH and stochastic volatility models. While most of these studies have documented highly significant in-sample parameter estimates and pronounced intertemporal volatility persistence, traditional ex-post forecast evaluation criteria suggest that the models provide seemingly poor volatility forecasts. Contrary to this contention, we show that volatility models produce strikingly accurate interdaily forecasts for the latent volatility factor that would be of interest in most financial applications. New methods for improved ex-post interdaily volatility measurements based on high-frequency intradaily data are also discussed.

https://faculty.washington.edu/ezivot/econ589//////AndersenBollerslevIER1998.pdf

Answering the skeptics: yes, standard volatility models do provide accurate forecasts*

We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. In addition, we find that stocks with high idiosyncratic volatility relative to the Fama and French (1993) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.

The Cross-Section of Volatility and Expected Returns

Implied volatility is widely believed to be informationally superior to historical volatility, because it is the "markets" forecast of future volatility. But for S&P 100 index options, the most actively traded contract in the United States, we find implied volatility. In aggregate and across subsamples separated by maturity and strike price, implied volatility has virtually no correlation with future volatility, and it does not incorporate the information contained in recent observed volatility. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

The Informational Content of Implied Volatility

This paper presents empirical evidence that trading in options contributes to both transactional and informational efficiency of the stock market by reducing the effect of constraints on short sales. The significantly higher average level of short interest exhibited by optionable stocks supports the argument that options facilitate short selling. We also find significant effects on option prices, related to the short interest in the underlying stock. We then present evidence that options also increase information efficiency. Earlier work, that is replicated and extended here, has suggested that short sale constraints cause stock prices to underweight negative information. Options appear to reduce that effect.

Options, Short Sales, and Market Completeness

In a world of heterogeneous investors, a competitive financial market has two major functions. First, it is the mechanism by which ownership of the existing supply of risky assets is distributed among investors. This is the role which is analyzed in detail by the standard Capital Asset Pricing Model (CAPM) and its many extensions. The second important market function is to aggregate the diverse information held by different investors into a single price. Most versions of the CAPM eliminate the information aggregation function by assuming that investors hold homogeneous beliefs with respect to the probability distribution of asset returns. Exceptions which deal with some aspects of the problem are Lintner [7], Grossman [4], Grossman and Stiglitz [5], and Figlewski [1, 3 ].

The Informational Effects of Restrictions on Short Sales: Some Empirical Evidence

Standard approaches to designing a futures hedge often suffer from two major problems. First, they focus only on minimizing risk, so no account is taken of the impact on expected return. Second , in estima ting the hedge ratio, no allowance is made for time variation in the distribution of cash and futures price changes. This paper describes a technique for estimating the optimal futures hedge that corrects these problems and illustrates its use in hedging Treasury bonds with T-bond futures. Copyright 1988 by MIT Press.

Estimation of the Optimal Futures Hedge

IN EARLY 1982, TRADING BEGAN at three different exchanges in futures contracts based on stock indexes. Stock index futures were an immediate success, and quickly led to a proliferation of new futures and options markets tied to various indexes. One reason for this success was that index futures greatly extended the range of investment and risk management strategies available to investors by offering them, for the first time, the possibility of unbundling the market and nonmarket components of risk and return in their portfolios. Many portfolio management and other hedging applications in investment banking and security trading have been described elsewhere1 ranging from use by a passive fund manager to reduce risk over a long time horizon to use by an underwriter to hedge the market risk exposure in a stock offering for one or two days. In considering the potential applications of index futures, it is clear that in nearly every case a cross-hedge is involved. That is, the stock position that is being hedged is different from the underlying portfolio for the index contract.2 This means that return and risk for an index futures hedge will depend upon the behavior of the "basis," i.e., the difference between the futures price and the cash price. Hedging a position in stock will necessarily expose it to some measure of basis risk-risk that the change in the futures price over time will not track exactly the value of the cash position. Basis risk can arise from a number of different sources, and is a more significant problem for stock index contracts than for other financial futures, like Treasury bills and bonds.3 The most apparent cause of basis risk is the nonmarket component of return on the cash stock position. Since the index contract is tied to the behavior of an underlying stock market index, nonmarket risk cannot be hedged. This is the essential problem of a cross-hedge. However, basis risk can be present even when the hedge involves a position in the index portfolio itself and there is no nonmarket risk. For one thing, returns to the index portfolio include dividends, while the index, and the index future, only track the capital value of the portfolio. Any risk associated with dividends on the portfolio will become basis risk in a hedged position. Still, dividends are fairly low and also quite stable, so this may not be a terribly important shortcoming.

https://www.jstor.org/stable/pdfplus/2327924.pdf

Stephen Figlewski

Papers

The Informational Content of Implied Volatility

Options, Short Sales, and Market Completeness

The Informational Effects of Restrictions on Short Sales: Some Empirical Evidence

Estimation of the Optimal Futures Hedge

Hedging Performance and Basis Risk in Stock Index Futures