Risk-free Rate Of Return Research Articles

Residual Income Risk, Intrinsic Values, and Share Prices

Empirical accounting research provides surprisingly little evidence on whether accounting earnings numbers capture cross-sectional differences in risk that are associated with cross-sectional differences in share prices. We address two questions regarding the risk-relevance of accounting numbers: (1) Are accounting-related risk measures (i.e., the systematic risk and total volatility in a firm's time-series of residual return on equity) associated with the market's assessment and pricing of equity risk? (2) If so, then are these accounting-related risk measures incrementally associated with the market's assessment and pricing of equity risk beyond other observable factors, such as those in the Fama and French (1992) three-factor model? We develop an accounting-fundamentals-based measure of the market's pricing of risk—the difference between actual share price and a residual income valuation model estimate of share value using risk-free rates of return. Our results show that both systematic risk and total volatility in residual return on equity partially explain this pricing differential, and that the explanatory power of total volatility is incremental to the Fama and French (1992) factors—market beta, firm size, and the market-to-book ratio.

An introduction to security returns

The equity premium - the difference between the return achievable from investment in the equity market (RM ) and the risk-free rate of return (RF )- plays an important part in corporate finance. The expression equity premium (sometimes referred to as the equity risk premium) is used to denote the ex ante expectation of investors. The term excess return refers to the ex post achievement of stock returns over and above the risk-free return. If we compare US and UK returns, we find that total returns, real returns and the value of (RM - RF ) are all marginally higher for the UK. Summarized evidence appears in Table 1 and Table 6. Such greater returns may be due to an increased risk premium related to increasing unexpected inflation. Particularly important in estimating the equity risk premium is whether excess returns are measured using a geometric or an arithmetic mean return. To a significant extent, this question revolves around mean reversion in stock returns. Evidence of mean reversion is substantial, although it cannot be proved unequivocally. Given the weight of evidence of mean reversion, there may be a strong case for the use of a geometric mean with an equity premium of between 3% and 5% - or even less.

A simple model of incomplete insurance the case of permanent shocks

This paper constructs a simple model of incomplete insurance by introducing permanent idiosyncratic shocks into an Ak type endogenous growth model. As in the exchange economy of Constantinides and Duffie (1996), we define a no-trading equilibrium using this model. Exploiting a simple closed-form solution, the paper examines several important implications including the effect of precautionary savings caused by uninsured shocks on consumption growth and asset pricing (in particular on risk-free rates of return), the relation with representative agent models, and the cross-sectional consumption distribution.

Principles for modelling financial markets

The paper introduces an approach focused towards the modelling of dynamics of financial markets. It is based on the three principles of market clearing, exclusion of instantaneous arbitrage and minimization of increase of arbitrage information. The last principle is equivalent to the minimization of the difference between the risk neutral and the real world probability measures. The application of these principles allows us to identify various market parameters, e.g. the risk-free rate of return. The approach is demonstrated on a simple financial market model, for which the dynamics of a virtual risk-free rate of return can be explicitly computed.

Expected and realised returns for Singaporean IPOs: Initial and long-run analysis

We analyse initial and long-run returns for all Singapore IPOs made between 1 July 1973 and 31 December 1992. Initial returns are found to be around 30 percent. However, after rationing and application costs are taken into account initial returns are insignificantly different from the risk-free rate of return, a result consistent with the predictions of the model of Rock (1986). Initial returns are positively related to the level of oversubscription and retained ownership, although the economic significance of the latter is weak. In contrast to many other international studies of IPOs, the long-run average returns for Singaporean IPOs are insignificantly different from an efficient market expectation. While long-run returns for individual companies show considerable variation, these returns are not predictable using information available at the IPO date. We further investigate the large oversubscription rates which are a peculiar feature of the Singapore IPO market, and argue that they are consistent with demand expansion by informed investors. Our estimates of the minimum price that a rational issuer would set in an IPO are below the actual issue price for all issues, given what we regard as reasonable limits on uninformed demand.

On the Mean-Variance Tradeoff in Option Replication with Transactions Costs

This paper analyzes the tradeoff between cost and risk of discretely rebalanced option hedges in the presence of transactions costs. I present closed form solutions for expected hedging error, transactions costs, and variance of the cash flow from a time-based hedging strategy similar to that analyzed by Leland (1985). Furthermore, I characterize the cost and risk of a move-based hedging strategy without resorting to Monte Carlo simulations. All results are sufficiently general to accommodate the use of a transactions costs adjusted hedging volatility and an asset rate of return that differs from the risk-free rate of return.

Tests of the risk premium on foreign currency futures impiled by the intertemporal asset pricing theory

In earlier literature on futures exchange rates, some evidence against the martingale hypothesis is not accompanied by an explicit alternative hypothesis derived from the asset pricing theory. This paper utilizes the asset pricing theory to show that the rate of growth of futures price is a function of the rate of growth of the per capita consumption, the logarithm of the gross risk-free rate of return and the time varying risk premium. The form of risk premium implied by the asset pricing theory is examined and its statistical significance is tested with the British Pound and German Deutschemark futures. Consequently, the specification of the test equation implied by the asset pricing theory is tested against ad hoc test equations of the martingale hypothesis. For both currency futures, there is strong evidence against the ad hoc test equations of the martingale hypothesis.

Pricing of the Eurodollar bank rate

The Eurodollar bank rate has been widely viewed as one of the chief barometers of credit market conditions in the Eurocurrency markets, yet not enough is known about how this rate is determined. For example, most of the short-to-medium term loans made by banks in the Eurocurrency market are floating rate loans linked to the London Interbank Offered Rate (LIBOR). The Eurocurrency interbank market “encompasses over 1,000 banks from 50 different countries, with a total market size of $3.3 trillion . . . transactions in U.S. dollars are the most prominent.. [13, p. 171. The objective of this paper is to attempt to further our understanding of the pricing of the Eurodollar bank rate through an analysis of recent Eurodollar bank rate movements. Empirical studies on determination of the Eurodollar bank rate [l, 7,11,12], generally have viewed the Eurodollar market as a major financial intermediary that channels short-term funds among the money markets of industrial countries. In these studies, the dollar-denominated risk-free rate of return (U.S. Treasury bill rate) is taken to be the principal determinant of the Eurodollar bank rate.’ Moreover, these studies anticipate that the maximum adjustment in the Eurodollar bank rate will be on a one-to-one basis to changes in the U.S. Treasury bill rate. Using both quarterly and monthly data for 1957-64, Hendershott estimated a stock-flow adjustment equation of the “Koyck-Nerlove” type. He concluded that, “in response to a one percentage point rise in the mean U.S. bill rate, . . . the eventual increase in the Eurodollar rate will be . . . 1.07 percentage points. This is not significantly greater than the maximum anticipated value of unity” [7, p. 4621. Using a multiple regression approach and monthly data for the period 1961-71, Argy and Hodjera [l] showed that the maximum value of the estimated coefficient on the U.S. Treasury bill rate is 0.62. Mills [12], using a similar approach and weekly data for 1966-70, showed that the maximum value is 0.61. In this paper the basic relationship between the Eurodollar bank rate and a dollar-denominated risk-free rate of return (the U.S. Treasury bill rate) is examined.

Some implications of traded options on the pricing of the underlying stock

In a large asset market where the equilibrium APT holds and stock options are priced by the Black-Scholes model, the expected return of the stock must equal the risk-free rate. This conclusion is due to the fact that a zero-beta stock-option hedge will not have a risk-free rate of return unless the expected return of the stock equals the risk-free rate. If one accepts the equilibrium APT and the Black-Scholes option pricing model as good approximations, the result provides a plausible explanation for some recent anomalous empirical findings.

On the Use of Period-Average Stumpage Prices to Estimate Forest Asset Pricing Models

Several recent papers (Thomson 1987; Zinkhan 1988; Zinkhan and Mitchell 1988; Cubbage, Harris, and Redmond 1988; Binkley and Washburn 1988a, 1988b), including one in this journal (Redmond and Cubbage 1988), use either Sharpe's (1963) single-index market model (with returns to the overall market portfolio serving as the index) or the Capital Asset Pricing Model (CAPM) (Sharpe 1964; Lintner 1965) to measure the risk and evaluate the performance of timberland investments.' The CAPM specifies an equilibrium relationship between asset i's expected rate of return (ERi), the risk-free rate of return (Rf), and the expected risk premium on the market portfolio of all assets in the economy (the expected market rate of return, ERm, minus the risk-free rate, Rf):

Put‐Call Parity and Market Efficiency

OPTIONS HAVE CONSTITUTED THE most dynamic segment of the securities markets since the inception of the Chicago Board Options Exchange in April 1973, which was followed shortly thereafter by four other registered option exchanges. Until June 1977, only call options were traded on these registered option exchanges. At that time, each of the exchanges was permitted to commence trading in put options on five underlying securities. The initial success of the put options promises to provide the next major thrust to the registered options markets when they can be traded on all of the underlying securities listed on the exchanges. The advent of put trading on the registered exchanges is important because a deterministic relationship should exist between put and call prices, irrespective of investor demands, if both options are written on the same underlying security and have the same exercise (striking) price and expiration date.' This relationship exists because the put, call, and the underlying stock form an interrelated securities complex, in which any two of the three instruments can be combined in such a manner to yield the profit and loss opportunities of the third instrument. Because of conversion possibilities, theoretical put-call parity models can be developed to determine a put (call) price given a corresponding call (put) price and other relevant information. If the actual put or call price should deviate substantially from the parity price, an opportunity exists for investors to set up a riskless arbitrage position and earn more than the risk-free rate of return. The original put-call parity model was developed by Stoll [10] and later extended and modified by Merton [5]. These models were used by Stoll [10] and and Gould and Galai [3] to empirically investigate parity among over-the-counter (OTC) put and call options. While these studies basically supported the put-call parity theory, some inefficiencies in the relationship were also found to exist. Today's options market is vastly different from the OTC market, however. Major changes made by the registered options exchanges, including the standardization of contractual terms such as the exercise price and expiration date, and the creation of a central clearing corporation, have transformed the options market place from a thin, negotiated market to a competitive, auction market.2 Another

E-V and E-S Capital Asset Pricing Models: Some Empirical Tests

The purpose of this study was to test empirically the risk and return relationships for a mean-variance (E-V) and a mean-semivariance (E-S) capital asset pricing model (CAPM). To date, virtually all empirical work has focused on the Sharpe-Lintner [28,17] E-V model. In the E-V model, the risk of an efficient portfolio is measured by the standard deviation of return, σp. For individual securities, the appropriate measure of risk is the covariance of return on the security and the market portfolio. The E-V model states that the expected return of any security or portfolio equals the risk-free rate of return plus a risk premium that is η times the difference between the expected return on the market portfolio and the risk-free rate of return, i.e.,where the tildes denote random variables, and = expected rate of return on security i,Rf = risk-free rate of return, = expected rate of return on the market portfolio, and.