The Dollar, the Oil Price and Policy Divergence

There is currently a clear divergence of policy between the United States, Japan and Germany. With the US in recession and concern growing over the severity of the slump, interest rates have been cut in a move to revive the economy. In contrast Japan and Germany are both experiencing strong growth and monetary policy remains tight to combat inflation. This divergence was seen most clearly when the Federal Reserve Board lowered its discount rate to 6 per cent on 1 February, the day after the Bundesbank had raised its Lombard rate to 9 per cent. With G7 increasingly concerned about domestic factors, less emphasis is placed upon stable exchange rates and as a result the dollar is at an all-time low. The last two G7 communiqués have stressed ‘stability oriented monetary policies’, an ambiguous phrase which fails to define ‘stability’ either in terms of exchange rates, inflation or growth. Thus both the German and Japanese policy of high interest rates to reduce inflation and low US interest rates aimed at stimulating the economy can be termed as ‘stability oriented’. This analysis focuses on these divergent policy responses in two alternative scenarios to the world forecast we presented last month. The first scenario considers what might happen if the Federal Reserve Board were to stimulate the US economy by further cuts in interest rates, whilst Japanese and German rates were unchanged in the face of inflationary pressures. This case may be relevant if the recent US loosening of monetary policy is not sufficient to encourage growth because of a ‘credit crunch’, so that a more expansionary policy is required by the Fed. As a consequence, policy diverges further and the dollar weakens. The second scenario focuses upon a reduction in inflationary pressures in Japan and Germany brought about by an oil price fall. In this case we assume that US policy is already loose enough to avoid a prolonged recession, but that German and Japanese monetary policy is relaxed as inflationary forces recede. In this case policies converge. Each scenario thus concentrates on one of !he two features which are causing the policy divergence amongst G3 countries: recession in the US, inflation in Germany and Japan.     

Trading Volume: Implications of an Intertemporal Capital Asset Pricing Model

We derive an intertemporal asset pricing model and explore its implications for trading volume and asset returns. We show that investors trade in only two portfolios: the market portfolio, and a hedging portfolio that is used to hedge the risk of changing market conditions. We empirically identify the hedging portfolio using weekly volume and returns data for U.S. stocks, and then test two of its properties implied by the theory: Its return should be an additional risk factor in explaining the cross section of asset returns, and should also be the best predictor of future market returns.     

Determinants of Mortgage Default and Consumer Credit Use: The Effects of Foreclosure Laws and Foreclosure Delays

The mortgage default decision is part of a complex household credit management problem. We examine how factors affecting mortgage default spill over to other credit markets. As home equity turns negative, homeowners default on mortgages and home equity lines of credit at higher rates, whereas they prioritize repaying credit cards and auto loans. Larger unused credit card limits intensify the preservation of credit cards over housing debt. Although mortgage nonrecourse statutes increase default on all types of housing debt, they reduce credit card defaults. Foreclosure delays increase default rates for housing and nonhousing debts. Our analysis highlights the interconnectedness of debt repayment decisions.     

A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks

We propose a nonparametric method for estimating the pricing formula of a derivative asset using learning networks. Although not a substitute for the more traditional arbitrage-based pricing formulas, network-pricing formulas may be more accurate and computationally more efficient alternatives when the underlying asset's price dynamics are unknown, or when the pricing equation associated with the no-arbitrage condition cannot be solved analytically. To assess the potential value of network pricing formulas, we simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis function networks, multilayer perceptron networks, and projection pursuit. To illustrate the practical relevance of our network pricing approach, we apply it to the pricing and delta-hedging of S&P 500 futures options from 1987 to 1991.     

Public Information and Coordination: Evidence from a Credit Registry Expansion

This paper provides evidence that lenders to a firm close to distress have incentives to coordinate: lower financing by one lender reduces firm creditworthiness and causes other lenders to reduce financing. To isolate the coordination channel from lenders' joint reaction to new information, we exploit a natural experiment that forced lenders to share negative private assessments about their borrowers. We show that lenders, while learning nothing new about the firm, reduce credit in anticipation of other lenders' reaction to the negative news about the firm. The results show that public information exacerbates lender coordination and increases the incidence of firm financial distress.