The Journal of Finance

The Time‐Varying Price of Financial Intermediation in the Mortgage Market

Published: 6/4/2024,  Volume: 79,  Issue: 4  |  DOI: 10.1111/jofi.13358  |  Cited by: 21

ANDREAS FUSTER, STEPHANIE H. LO, PAUL S. WILLEN

We introduce a new measure of the price charged by financial intermediaries for connecting mortgage borrowers with capital market investors. Based on administrative lender pricing data, we document that the price of intermediation reacts strongly to variation in demand, reflecting capacity constraints of mortgage originators. This positive comovement of price with quantity reduced the pass‐through of quantitative easing. We also find a notable upward trend in this price between 2008 and 2014, likely due to increased legal and regulatory burden in the mortgage market. The trend led to an implicit cost to borrowers of nearly $100 billion over this period.

A REFORMULATION OF THE THEORETICAL BASES FOR A PERMANENT FEDERAL EXCESS PROFITS TAX*

Published: 12/1953,  Volume: 8,  Issue: 4  |  DOI: 10.1111/j.1540-6261.1953.tb01193.x  |  Cited by: 0

Nelson H. C. Lo

Implementing Option Pricing Models When Asset Returns Are Predictable

Published: 3/1995,  Volume: 50,  Issue: 1  |  DOI: 10.1111/j.1540-6261.1995.tb05168.x  |  Cited by: 159

ANDREW W. LO, JIANG WANG

The predictability of an asset's returns will affect the prices of options on that asset, even though predictability is typically induced by the drift, which does not enter the option pricing formula. For discretely‐sampled data, predictability is linked to the parameters that do enter the option pricing formula. We construct an adjustment for predictability to the Black‐Scholes formula and show that this adjustment can be important even for small levels of predictability, especially for longer maturity options. We propose several continuous‐time linear diffusion processes that can capture broader forms of predictability, and provide numerical examples that illustrate their importance for pricing options.

Trading Volume: Implications of an Intertemporal Capital Asset Pricing Model

Published: 12/2006,  Volume: 61,  Issue: 6  |  DOI: 10.1111/j.1540-6261.2006.01005.x  |  Cited by: 88

ANDREW W. LO, JIANG WANG

We derive an intertemporal asset pricing model and explore its implications for trading volume and asset returns. We show that investors trade in only two portfolios: the market portfolio, and a hedging portfolio that is used to hedge the risk of changing market conditions. We empirically identify the hedging portfolio using weekly volume and returns data for U.S. stocks, and then test two of its properties implied by the theory: Its return should be an additional risk factor in explaining the cross section of asset returns, and should also be the best predictor of future market returns.

Nonparametric Estimation of State‐Price Densities Implicit in Financial Asset Prices

Published: 4/1998,  Volume: 53,  Issue: 2  |  DOI: 10.1111/0022-1082.215228  |  Cited by: 812

Yacine Aït‐Sahalia, Andrew W. Lo

Implicit in the prices of traded financial assets are Arrow–Debreu prices or, with continuous states, the state‐price density (SPD). We construct a nonparametric estimator for the SPD implicit in option prices and we derive its asymptotic sampling theory. This estimator provides an arbitrage‐free method of pricing new, complex, or illiquid securities while capturing those features of the data that are most relevant from an asset‐pricing perspective, for example, negative skewness and excess kurtosis for asset returns, and volatility “smiles” for option prices. We perform Monte Carlo experiments and extract the SPD from actual S&P 500 option prices.

Pension Funding, Share Prices, and National Savings

Published: 9/1981,  Volume: 36,  Issue: 4  |  DOI: 10.1111/j.1540-6261.1981.tb04885.x  |  Cited by: 77

MARTIN FELDSTEIN, STEPHANIE SELIGMAN

This paper examines empirically the effect of unfunded pension obligations on corporate share prices and discusses the implications of these estimates for national saving, the decline of the stock market in recent years, and the rationality of corporate financial behavior. The analysis uses the information on inflation‐adjusted income and assets which large firms were required to provide for 1976 and subsequent years.The evidence for a sample of nearly 200 manufacturing firms is consistent with the conclusion that share prices fully reflect the value of unfunded pension obligations. Since the conventional accounting measure of the unfunded pension liability has a number of problems (which we examine in the paper), it would be more accurate to say that the data are consistent with the conclusion that shareholders accept the conventional measure as the best available information and reduce share prices by a corresponding amount.The most important implication of the share price response is that the existence of unfunded private pension liabilities does not necessarily entail a reduction in total private saving. Because the pension liability reduces the equity value of the firm, shareholders are given notice of its existence and an incentive to save more themselves. For this reason, unfunded private pensions differ fundamentally from the unfunded Social Security pension and the other unfunded federal government civilian and military pensions.

Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation

Published: 8/2000,  Volume: 55,  Issue: 4  |  DOI: 10.1111/0022-1082.00265  |  Cited by: 825

Andrew W. Lo, Harry Mamaysky, Jiang Wang

Technical analysis, also known as “charting,” has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis—the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution—conditioned on specific technical indicators such as head‐and‐shoulders or double bottoms—we find that over the 31‐year sample period, several technical indicators do provide incremental information and may have some practical value.

A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks

Published: 7/1994,  Volume: 49,  Issue: 3  |  DOI: 10.1111/j.1540-6261.1994.tb00081.x  |  Cited by: 601

JAMES M. HUTCHINSON, ANDREW W. LO, TOMASO POGGIO

AbstractWe propose a nonparametric method for estimating the pricing formula of a derivative asset using learning networks. Although not a substitute for the more traditional arbitrage‐based pricing formulas, network‐pricing formulas may be more accurate and computationally more efficient alternatives when the underlying asset's price dynamics are unknown, or when the pricing equation associated with the no‐arbitrage condition cannot be solved analytically. To assess the potential value of network pricing formulas, we simulate Black‐Scholes option prices and show that learning networks can recover the Black‐Scholes formula from a two‐year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta‐hedge options out‐of‐sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis function networks, multilayer perceptron networks, and projection pursuit. To illustrate the practical relevance of our network pricing approach, we apply it to the pricing and delta‐hedging of S&P 500 futures options from 1987 to 1991.