Bluford H. Putnam

Irving Fisher and statistical approaches to risk

Abstract Irving Fisher has been recognized as one of the most prominent economists in the US in the first half of the 20th century. His contribution to financial economics has not been well recognized, however. This article describes Fishers pioneering efforts to apply statistical methods to the analysis of investment risk. In addition, it will argue that Fishers statistical analysis of risk had a Bayesian philosophy of probability theory. Finally, the highs and lows of Fishers investment strategy for the 1920s and 1930s will be discussed.

Examining the Causes and Potential Policy Responses to Emerging Market Currency Contagion

The differentiation in economic fundamentals and policies is highly pronounced among emerging markets countries. Yet emerging-market sector exposures often share a relative lack of liquidity, and during currency market sell-off episodes, such liquidity challenges can exacerbate market impacts and cause contagion. This article examines the causes and mechanics of the currency contagion in emerging markets countries that occurred in the spring of 2013 and went into the winter of 2013–2014. This episode has often been blamed on the potential ending of quantitative easing (QE) by the U.S. Federal Reserve. Our key conclusion is that, rather than the U.S. debate about ending QE, the episode was instead caused by the following factors: relative out-performance by the U.S. equity market in 2013, economic growth deceleration in many emerging markets countries, rising political risks, and the mechanics of asset allocation shifts away from emerging market exposures.

A Bayesian methodology to explore the effects of memory loss in currency markets

Abstract As time goes by, people tend to establish new norms based on more recent events and information. Old norms and standards are eclipsed by new developments. The same type of behavior appears to operate in financial markets. New and unexpected information can jolt a market, but over time market participants adjust to new patterns of information, of data flow, of market volatility, and even of structural change. On another level, one can ask the question of how much weight to give new information relative to old information. Market participants do this naturally, as they weigh new information, such as just-released economic data, compare it to their previously held expectations and then revise their expectations of future data releases and potential market reactions. When the question is viewed in this perspective, it seems quite natural to assume that new pieces of information, such as economic data releases, should be given more weight in assessing expectations of future market behavior than releases of the same type of data several months or even several years previously. That is, the information value of certain specific types of information, such as specific economic data releases, probably decays as time passes. This study proposes the use of a dynamic Bayesian technology to explore the effects of time decay in the value placed on specific bits of information that are received at regular intervals through time. The methodology that is being proposed is the main focus of this study and, to clarify this methodology, it is applied to the currency markets asking questions about the time decay pattern of certain economic data. The results reported in this example are purely for illustrative purposes, since they are specific to the data studied, the model specification of currency market behavior, and a wide variety of other assumptions embedded in this specific approach to currency analysis. Nevertheless, the results are interesting, if only because they demonstrate both the basic idea that new information is more valuable than old information and provide a useful and practical method of analyzing the issue of the time decay factor in how markets process information.