Health-Status Insurance: How Markets Can Provide Health Security

None of us has health insurance, really. If you develop a long-term condition such as heart disease or cancer, and if you then lose your job or are divorced, you can lose your health insurance. You now have a preexisting condition, and insurance will be enormously expensive -- if it's available at all.Free markets can solve this problem, and provide life-long, portable health security, while enhancing consumer choice and competition. "Heath-status insurance" is the key. If you are diagnosed with a long-term, expensive condition, a health-status insurance policy will give you the resources to pay higher medical insurance premiums. Health-status insurance covers the risk of premium reclassification, just as medical insurance covers the risk of medical expenses. With health-status insurance, you can always obtain medical insurance, no matter how sick you get, with no change in out-of-pocket costs. With health-status insurance, medical insurers would be allowed to charge sick people more than healthy people, and to compete intensely for all customers. People would have complete freedom to change jobs, move, or change medical insurers. Rigorous competition would allow us to obtain better medical care at lower cost. Most regulations and policy proposals aimed at improving long-term insurance -- including those advanced in Barack Obama's presidential campaign -- limit competition and consumer choice by banning risk-based premiums, forcing insurers to take all comers, strengthening employer-based or other forced pooling mechanisms, or introducing national health insurance. The individual health insurance market is already moving in the direction of health-status insurance. To let health-status insurance emerge fully, we must remove the legal and regulatory pressure to provide employer-based group insurance over individual insurance and remove regulations limiting risk-based pricing and competition among health insurers

Determinacy and Identification with Taylor Rules

The new-Keynesian, Taylor-rule theory of inflation determination relies on explosive dynamics. By raising interest rates in response to inflation, the Fed induces ever-larger inflation or deflation, unless inflation jumps to one particular value on each date. However, economics does not rule out inflationary or deflationary equilibria. Attempts to fix this problem assume that people believe the government will choose to blow up the economy if alternative equilibria emerge, by following policies we usually consider impossible. Therefore, inflation is just as indeterminate under “active” interest rate targets as it is under fixed interest rate targets. If one accepts the new-Keynesian solution, the parameters of the Taylor rule relating interest rates to inflation and other variables are not identified without unrealistic assumptions. Thus, Taylor rule regressions cannot be used to argue that the Fed conquered inflation by moving from a “passive” to an “active” policy in the early 1980s. The updated version presented here merges the content of two companion papers, w13409 and w13410, into a single current version. Thus, as of September 2010, w13409 and w13410 are identical.

Portfolio Advice for a Multifactor World

Asset returns, it turns out, do not follow the Capital Asset Pricing Model, and are somewhat predictable over time. I survey and interpret the large body of recent work that adapts traditional portfolio theory to answer, what should an investor do about these new facts in finance? I survey the extension of the famous 2 - fund' theorem to an N-fund'' theorem in which investors either hedge or assume the additional, non-market, sources of priced risk; I survey the burgeoning literature on time-varying portfolio rules and the Bayesian literature that advocates a great deal of caution. In a survey, I emphasize the risk-sharing nature of asset markets, I note the likelihood that many supposed anomalies will not last, and I emphasize the fact that the average investor must hold the market so portfolio decisions must be driven by differences between an investor and the average investor.

Commentary on "Macroeconomic implications of changes in the term premium"

Macroeconomics ; Finance

The Dog That Did Not Bark: A Defense of Return Predictability

To question the statistical significance of return predictability, we cannot specify a null that simply turns off that predictability, leaving dividend growth predictability at its essentially zero sample value. If neither returns nor dividend growth are predictable, then the dividend-price ratio is a constant. If the null turns off return predictability, it must turn on the predictability of dividend growth, and then confront the evidence against such predictability in the data. I find that the absence of dividend growth predictability gives much stronger statistical evidence against the null, with roughly 1-2% probability values, than does the presence of return predictability, which only gives about 20% probability values. I argue that tests based on long-run return and dividend growth regressions provide the cleanest and most interpretable evidence on return predictability, again delivering about 1-2% probability values against the hypothesis that returns are unpredictable. I show that Goyal and Welch's (2005) finding of poor out-of-sample R2 does not reject return forecastability. Out-of-sample R2 is poor even if all dividend yield variation comes from time-varying expected returns.

Where is the market going? Uncertain facts and novel theories

The author surveys the statistical evidence on average stock return and the economic theories that try to explain it. The statistical evidence suggests a period of low returns, followed by a slow reversion to a high long-term average. However, that evidence is quite uncertain. Standard economics predicts tiny stock returns. The author surveys new economic models that predict high returns, but by fundamentally changing the description of stock market risk. He warns that a low forecast for stock returns does not mean one should sell.Stocks ; Risk

Stocks as Money: Convenience Yield and the Tech-Stock Bubble

What caused the rise and fall of tech stocks? I argue that a mechanism much like the transactions demand for money drove many stock prices above the 'fundamental value' they would have had in a frictionless market. I start with the Palm/3Com microcosm and then look at tech stocks in general. High prices are associated with high volume, high volatility, low supply of shares, wide dispersion of opinion, and restrictions on long-term short selling. I review competing theories, and only the convenience yield view makes all these connections.

The Risk and Return of Venture Capital

This paper measures the mean, standard deviation, alpha and beta of venture capital investments, using a maximum likelihood estimate that corrects for selection bias. Since firms go public when they have achieved a good return, estimates that do not correct for selection bias are optimistic. The selection bias correction neatly accounts for log returns. Without a selection bias correction, I find a mean log return of about 100% and a log CAPM intercept of about 90%. With the selection bias correction, I find a mean log return of about 7% with a -2% intercept. However, returns are very volatile, with standard deviation near 100%. Therefore, arithmetic average returns and intercepts are much higher than geometric averages. The selection bias correction attenuates but does not eliminate high arithmetic average returns. Without a selection bias correction, I find an arithmetic average return of around 700% and a CAPM alpha of nearly 500%. With the selection bias correction, I find arithmetic average returns of about 53% and CAPM alpha of about 45%. Second, third, and fourth rounds of financing are less risky. They have progressively lower volatility, and therefore lower arithmetic average returns. The betas of successive rounds also decline dramatically from near 1 for the first round to near zero for fourth rounds. The maximum likelihood estimate matches many features of the data, in particular the pattern of IPO and exit as a function of project age, and the fact that return distributions are stable across horizons.

Using Production Based Asset Pricing to Explain the Behavior of Stock Returns Over the Business Cycle

The investment return is defined as the real return that results from marginally increasing investment at date r, and then reaping the extra output and decreasing investment at date t+1 to leave the production plan for other dates unchanged. This paper constructs investment returns from investment data and a production function, and compares investment returns to stock returns, in order to explain forecasts of stock returns by business cycle related variables, and to explain forecasts of future economic activity by stock returns.