Search results

I build an equilibrium model trying to reconcile investor preferences with several features of the cat bond market. The driving force behind the model is a habit process, in that catastrophes are rare economic shocks that could bring investors closer to their subsistence level. The calibration requires shocks with an impact between −1% and −3% to explain a reasonable level of cat bond spreads. Such investor preferences are not only able to generate realistic cat bond returns and price comovement among different perils, but may also able to explain why cat bonds offer higher rewards compared to equally rated corporate bonds.

This chapter examines factors that cause violations of regularity conditions and biases in estimates of substitution. In the context of the Fourier demand system, failing to impose curvature restrictions but correcting for serial correlation results in few violations of the curvature conditions. In contrast, imposing curvature restrictions without correcting for serial correlation biases substitution estimates and can cause violations of monotonicity. For serially correlated data, results suggest that correcting for serial correlation may be more important than imposing curvature. Furthermore, the artificially break-adjusted data that are inconsistent with consumer optimization can severely bias estimates. Results from the Bank of England's (BOE) preferred non-break-adjusted data establish that money and goods are substitutes in demand.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

We investigate discrepancies between willingness to pay (WTP) and willingness to accept (WTA) in the context of a stated choice experiment. Using data on customer preferences for water services where respondents were able to both ‘sell’ and ‘buy’ the choice experiment attributes, we find evidence of non-linearity in the underlying utility function even though the range of attribute levels is relatively small. Our results reveal the presence of significant loss aversion in all the attributes, including price. We find the WTP–WTA schedule to be asymmetric around the current provision level and that the WTP–WTA ratio varies according to the particular provision change under consideration. Such reference point findings are of direct importance for practitioners and decision-makers using choice experiments for economic appraisal such as cost–benefit analysis, where failure to account for non-linearity in welfare estimates may significantly over- or under-state individual's preferences for gains and avoiding losses respectively.

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

This chapter explores the yield curve movements in the interest rate swap markets of four major currencies, the Japanese yen (JPY), the US dollar (USD), the pound sterling (GBP), and the Swiss franc (CHF), by principal component analysis (PCA), focusing on the explanatory power of each driver. Comparing the cumulative proportions of the first three principal components, the “level” changes seem to explain the yield curve movements far better than the “ratio” changes in the case of the JPY (96.1% vs. 38.3%) and CHF (97.2% vs. 41.9%), and they are only marginally worse for the USD (97.7% vs. 98.5%) and GBP (96.5% vs. 98.3%). In all markets, the explanatory power (proportion) of the first PC (PC1) is over 82%, and most of the movements can be explained by it. Furthermore, the explanatory power (cumulative proportion) from PC1 up to the third PC (PC3) is over 96%. Thus, it can be considered that most of the movements can be explained by the first three PCs. In addition, we investigate whether there is a structural change in yield curve movements before and after the global financial crisis of 2007–2008 (GFC). If we use daily “level” changes for the PCA, the GFC has no impact on the yield curve movements for all major currencies. The three PCs retain good explanatory power.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.