Sean D. Campbell

Weather Forecasting for Weather Derivatives

We take a simple time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time-series modeling reveals both strong conditional mean dynamics and conditional variance dynamics in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. The approach can easily be used to produce not only short-horizon point forecasts, but also the long-horizon density forecasts of maximal relevance in weather derivatives contexts. We produce and evaluate both, with some success. We conclude that additional inquiry into nonstructural weather forecasting methods will likely prove useful in weather derivatives contexts.

Stock returns and expected business conditions: Half a century of direct evidence

We explore the macro/finance interface in the context of equity markets. In particular, using half a century of Livingston expected business conditions data we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a statistically and economically significant counter-cyclical fashion: depressed expected business conditions are associated with high expected excess returns. Moreover, inclusion of expected business conditions in otherwise standard predictive return regressions substantially reduces the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing. Expected business conditions retain predictive power even after controlling for an important and recently introduced non-financial predictor, the generalized consumption/wealth ratio, which accords with the view that expected business conditions play a role in asset pricing different from and complementary to that of the consumption/wealth ratio. We argue that time-varying expected business conditions likely capture time-varying risk, while time-varying consumption/wealth may capture time-varying risk aversion.

ANCHORING BIAS IN CONSENSUS FORECASTS AND ITS EFFECT ON MARKET PRICES

Previous empirical studies on the “rationality” of economic and financial forecasts generally test for generic properties such as bias or autocorrelated errors but provide only limited insight into the behavior behind inefficient forecasts. This paper tests for a specific form of forecast bias. In particular, we examine whether expert consensus forecasts of monthly economic releases are systematically biased toward the value of previous months’ releases. Such a bias would be consistent with the anchoring and adjustment heuristic described by Tversky and Kahneman (1974) or could arise from professional forecasters’ strategic incentives. We find broad-based and significant evidence for this form of bias, which in some cases results in sizable predictable forecast errors. To investigate whether market participants’ expectations are influenced by this bias, we examine interest rate reactions to economic news. We find that bond yields react only to the residual, or unpredictable, component of the forecast error and not to the component induced by anchoring, suggesting that expectations of market participants anticipate this bias embedded in expert forecasts.

Securitization Markets and Central Banking: An Evaluation of the Term Asset-Backed Securities Loan Facility

In response to the near collapse of US securitization markets in 2008, the Federal Reserve created the Term Asset-Backed Securities Loan Facility, which offered non-recourse loans to finance investors’ purchases of certain highly rated asset-backed securities. We study the effects of this program and find that it lowered interest rate spreads for some categories of asset-backed securities but had little impact on the pricing of individual securities. These findings suggest that the program improved conditions in securitization markets but did not subsidize individual securities. We also find that the risk of loss to the US government was small.

A Trend and Variance Decomposition of the Rent-Price Ratio in Housing Markets

We use the dynamic Gordon-growth model to decompose the rent-price ratio for owner-occupied housing in the U.S., four Census regions, and twenty-three metropolitan areas into three components: The expected present value of real rental growth, real interest rates, and future housing premia. We use these components to decompose the trend and variance in rent-price ratios for 1975-2005, for an early sub-sample (1975-1996), and for the recent housing boom (1997-2005). We have three main findings. First, variation in expected future real rents accounts for a small share of variation in our sample rent-price ratios; variation in real interest rates and housing premia account for most of the variability. Second, expected future real rates and housing premia were so strongly negatively correlated prior to 1997 that changes to real interest rates did not affect the rent-price ratio. After 1997, rates and premia have been positively correlated, and the decline in the rent-price ratio that has occurred in almost every geographic area in our sample since 1997 reflects both declining real rates and declining premia. Third, we show that in the recent housing boom, 65 percent of the decline in the aggregate rent-price ratio is due to a declining housing premium.

Macroeconomic Volatility, Predictability and Uncertainty in the Great Moderation: Evidence from the Survey of Professional Forecasters

An emerging and influential literature finds a large and significant decline in macroeconomic volatility since the middle of the 1980s. In this paper, I examine the extent to which the decline in annual and quarterly real output volatility since the onset of this period of Great Moderation can be attributed to changes in macroeconomic uncertainty and macroeconomic predictability. I use point forecasts of future real output growth from the Survey of Professional Forecasters (SPF) between 1969 and 2003 as a proxy for the predictable component of real output growth. The results indicate that declining predictability has played a significant role in the Great Moderation. Prior to the Great Moderation, professional forecasts explained roughly 30 percent of the variance in output growth. Post-moderation, the predictive ability of professional forecasts quickly vanished. This decline in predictability implies that interpreting the decline in the volatility of output shocks identified from a fixed parameter autoregressive model overstates the decline in macroeconomic uncertainty by between 20-40 percent. I also examine forecasts of the probability of a decline in real output from the SPF. Consistent with the findings from the point forecast data, these probability forecasts indicate that the decline in macroeconomic uncertainty as measured from an autoregressive model is overstated. While both the average probability of a decline in output and the uncertainty surrounding future declines in output computed from an autoregressive model decrease sharply after the mid-1980s, the SPF probability forecasts exhibit no such decrease. I assess the economic significance of the overstatement in the decline of macroeconomic uncertainty in terms of its effects on forecasts of the future equity premium. These results indicate that using the decline in the total volatility of real output growth along with the standard CCAPM model overstates the decline in the future equity premium by roughly 20 percent.

Alternative Estimates of the Presidential Premium

Since the early 1980s much research, including the most recent contribution of Santa-Clara and Valkanov (2003), has concluded that there is a stable, robust and significant relationship between Democratic presidential administrations and robust stock returns. Moreover, the difference in returns does not appear to be accompanied by any significant differences in risk across the presidential cycle. These conclusions are largely based on OLS estimates of the difference in returns across the presidential cycle. We re-examine this issue using more efficient estimators of the presidential premium. Specifically, we exploit the considerable and persistent heteroskedasticity in stock returns to construct more efficient weighted least squares (WLS) and generalized autoregressive conditional heteroskedasticity (GARCH) estimators of the difference in expected excess stock returns across the presidential cycle. Our findings provide considerable contrast to the findings of previous research. Across the different WLS and GARCH estimates we find that the point estimates are considerably smaller than the OLS estimates and fluctuate considerably across different sub samples. We show that the large difference between the WLS, GARCH and OLS estimates is driven by differing stock market performance during very volatile market environments. During periods of elevated market volatility, excess stock returns have been markedly higher under Democratic than Republican administrations. Accordingly, the WLS and GARCH estimators are less sensitive to these episodes than the OLS estimator. Ultimately, these results are consistent with the conclusion that neither risk nor return varies significantly across the presidential cycle.

Risk Transfer Across Economic Sectors using Credit Default Swaps

Credit default swaps (CDS) play an important role in distributing risk in the global financial system.

Empirically Evaluating Systemic Risks in CCPs: The Case of Two CDS CCPs

Using a unique data set of weekly CDS positions of major CDS clearing dealers, we empirically evaluate the systemic stability of two CCPs that clear CDS contracts. Our methodology considers both micro- and macroprudential risks to the central clearing system. We show that individual clearing members may pose considerable risk to a CCP as losses of individual dealers in a stress scenario may be large. We show that the combined stress losses of a clearing member across multiple CCPs may be substantially larger than its losses to a single CCP due to a high and positive correlation in the economic exposures of the clearing member across CCPs. Last, we show that there is a potential for several clearing members to experience large losses simultaneously to a given CCP highlighting crowded trades concerns. The methodology explored in this paper documents the feasibility of performing integrated and coordinated stress testing of CCPs on an ongoing basis. These results suggest that the accumulation of systemic risk in the central clearing system may be non-trivial and that the proposed stress tests may be a useful tool for measuring and managing such risk.