relative-value

An Analysis of CMS Spread trades, searching for the highest carry. (Note, carry alone is not a judge of a great trade).

1. 1y expiry 10s30s EUR SL CMS cap spread 0.40-0.70 for 15.75cts, giving a 1y carry of 97% of the premium. Breakeven: 0.55%. CMS adjusted forward: 0.53%. Spot: 0.68%.
2. 1y expiry 5s30s EUR SL CMS cap spread 1.10-1.50 for 22.5cts, giving a 1y carry of 95% of the premium. Breakeven: 1.33%. CMS adjusted forward: 1.33%. Spot: 1.53%. Reference levels: adjusted 1y forwards 10s30s: 0.53, 5s30s: 1.33%.

In our recent report, Finding extreme carry in CMS spread caps, we argue that trades in CMS spread caps offer high, even extreme, carry. In particular, we scanned over 7,500 trades in EUR and USD, for five CMS spread combinations (5s30s, 2s30s, etc.), with expiries up to 10 years, and over 50 strikes, with the aim of maximising carry. We selected some trades in USD CMS cap spreads, in 5s30s and 5s10s, as the most attractive in carry terms. In this report, we turn our attention to CMS spread caps in EUR, some of which offer high carry too. We focus on what we consider the two most promising slopes: EUR 10s30s, and 5s30s, for caps with one-year expiry. Fig. 1: EUR 5s30s CMS spread cap, single-look. 1-year carry (as % of premium) vs strike, for different option expiries Source: Nomura Research The usual results for carry in Figure 1 do not appear too attractive at first glance. However, we can substantially improve the carry by trading a spread of CMS spread caps – i.e., buy and sell the caps at different strikes. Our optimisation over the strikes shows that, for these two slopes (EUR 5s30s and 10s30s), we can obtain better carry by buying the cap vs selling another cap with a higher strike.

Long-dated USD callable bond issuance into Germany and Taiwan is continual, but this year's unprecedented demand from Taiwan has driven lower right vols lower. The recent jump was driven by regulatory changes. As this was an unintended consequence of regulatory loosening, we expect the window to close and callable issuance to decrease to more normal levels. In this report, we present and compare five forward vol strategies which all benefit from this effect.

We recommend a bear steepener trade in the EUR long end – buy 15y30y ATMF payer vs10y10y ATMF+31bp payer, delta-neutral and at a zero initial cost. This trade has considerable pickups over both forwards and spot, and can also benefit from renewed regulatory-driven asset-liability hedging flows. It is long vega, and earns decent carry from curve/vol roll-down and time decay. Because of the recent flattening of the curve, the current entry level is attractive in our opinion.

We consider a productive asset, called equipment or capital good, and we examine the properties of, as well as the interactions between, the operating policies, which are determined by its optimal utilization and maintenance, and the capital policy of scrapping, which defines the optimal time when the productive asset is retired from its current use. Starting with an abstract model and using the approach of optimal control, initially we characterize the various types of equipment by assigning to them a single total profit index, which indicates how the above policies affect the flow of operating revenues plus capital gains or losses. This index is a function of market-determined prices. So using it we then investigate how the operating and capital policies are influenced by the rate of discount, the price of new equipment, and the rise or fall of the price of new equipment relative to the value of its output. Among other interesting results, we find that the effects of these prices on the nature and interactions of optimal policies depend crucially on whether the equipment is of the usual profit making type, where output is the main source of revenue, or of the antique type, where the main source of revenue is capital gains.

We propose and demonstrate a methodology for the construction and analysis of efficient, effective and balanced portfolios of R&D projects with interactions. The methodology is based on an extended data envelopment analysis (DEA) model that quantifies some the qualitative concepts embedded in the balanced scorecard (BSC) approach. The methodology includes a resource allocation scheme, an evaluation of individual projects, screening of projects based on their relative values and on portfolio requirements, and finally a construction and evaluation of portfolios. The DEA-BSC model is employed in two versions, first to evaluate individual R&D projects, and then to evaluate alternative R&D portfolios. To generate portfolio alternatives, we apply a branch-and-bound algorithm, and use an accumulation function that accounts for possible interactions among projects. The entire methodology is illustrated via an example in the context of a governmental agency charged with selecting technological projects.