Knowledge that Transforms

We test the effectiveness of Bank of Japan (BOJ)’s foreign exchange interventions on conditional first and second moments of exchange rate returns and traded volumes, using a bivariate EGARCH model of the Yen/USD market from 5-13-1991 to 3-16-2004. We also estimate a friction model of BOJ’s intervention reaction function based on reducing short-term market disorderliness and supplementing domestic monetary policy. Important finding of this study are that: (i) we find ineffectiveness of BOJ interventions in influencing exchange rate trends pre-1995, in general, but effectiveness post-1995; (ii) FED intervention amplified the effectiveness of the BOJ transactions; (iii) interventions amplified market volatility and volumes through a ‘learning by trading’ process; (iv) BOJ’s interventions were based on ‘leaning against the wind’ motivations on the exchange rate trend and volumes; and (v) BOJ interventions were vigorously used in support of domestic monetary policy objectives post-1995. Though some of our findings confirm recent studies, our analysis goes deeper to provide new findings with important implications for central banks and foreign exchange market participants.

This paper reports a systematic examination of the determinants of deposit-related retail banking fees using a set of survey data that is unusual for its size, specificity, and sampling properties. The analysis focuses explicitly on six different fees associated with checking accounts and automated teller machine (ATM) usage. A preliminary analysis documents that, on average, multimarket banks charge substantially higher fees than do typically smaller, single-market banks. A more detailed econometric analysis yields results consistent with predictions of recent models. In particular, it finds that the greater the presence of multimarket banks in the local market, the higher are the retail deposit fees of single-market banks (except in highly concentrated markets) and the weaker is the positive relationship between those fees and market concentration.

We investigate the relation between option-based executive compensation and market measures of risk for a sample of commercial banks during the period of 1992–2000. We show that following deregulation, banks have increasingly employed stock option-based compensation. As a result, the structure of executive compensation induces risk-taking, and the stock of option-based wealth also induces risk-taking. The results are robust across alternative risk measures, statistical methodologies, and model specifications. Overall, our results support a management risk-taking hypothesis over a managerial risk aversion hypothesis. Our results have important implications for regulators in monitoring the risk levels of banks.

Fiscal rules, such as the excessive deficit procedure and the stability and growth pact (SGP), aim at constraining government behavior. [Milesi-Ferretti, G., 2003. Good, bad or ugly? On the effects of fiscal rules with creative accounting, Journal of Public Economics, 88, 377–394] develops a model in which governments circumvent such rules by reverting to creative accounting. The amount of this depends on the reputation cost for the government and the economic cost of sticking to the rule. We provide empirical evidence of creative accounting in the European Union. We find that the SGP rules have induced governments to use stock-flow adjustments, a form of creative accounting, to hide deficits. The tendency to substitute stock-flow adjustments for budget deficits is especially strong for the cyclical component of the deficit, as in times of recession the cost of reducing the deficit is particularly large.

This paper examines the contribution of market makers to the liquidity and the efficiency of the options market in a unique setup of an order-driven computerized trading system, in which market makers and other participants operate under equitable conditions. The main findings are: (1) liquidity increased – a 60% increase in trading volume and a 35% decrease of bid–ask spreads; (2) the efficiency of shekel–euro options trading improved – deviations from put–call parity decreased significantly by 12%, and skewness decreased by about 30%. We also find that the net cost to the exchange is out weighted by the benefit to the trading public and that the presence of market makers encouraged trading between other participants far beyond their own trading.

This paper applies the extreme-value (EV) generalised pareto distribution to the extreme tails of the return distributions for the S&P500, FT100, DAX, Hang Seng, and Nikkei225 futures contracts. It then uses tail estimators from these contracts to estimate spectral risk measures, which are coherent risk measures that reflect a user’s risk-aversion function. It compares these to VaR and expected shortfall (ES) risk measures, and compares the precision of their estimators. It also discusses the usefulness of these risk measures in the context of clearinghouses setting initial margin requirements, and compares these to the SPAN measures typically used.

Many large financial institutions compute the Value-at-Risk (VaR) of their trading portfolios using historical simulation based methods, but the methods’ properties are not well understood. This paper theoretically and empirically examines the historical simulation method, a variant of historical simulation introduced by Boudoukh et al. [Boudoukh, J., Richardson, M., Whitelaw, R., 1998. The best of both worlds, Risk 11(May) 64–67] (BRW), and the filtered historical simulation method (FHS) of Barone-Adesi et al. [Barone-Adesi, G., Bourgoin F., Giannopoulos, K., 1998. Don’t look back. Risk 11(August) 100–104; Barone-Adesi, G., Giannopoulos K., Vosper L., 1999. VaR without correlations for nonlinear portfolios. Journal of Futures Markets 19(April) 583–602]. The historical simulation and BRW methods are both under-responsive to changes in conditional risk; and respond to changes in risk in an asymmetric fashion: measured risk increases when the portfolio experiences large losses, but not when it earns large gains. The FHS method is promising, but its risk estimates are variable in small samples, and its assumption that correlations are constant is violated in large samples. Additional refinements are needed to account for time-varying correlations; and to choose the appropriate length of the historical sample period.

This paper aims at improving our understanding of internal risk rating systems (IRS) at large banks, of the way in which they are implemented, and at verifying if IRS produce consistent estimates of banks’ loan portfolio credit risk. An important property of our work is that the size of our data set allows us to derive measures of credit risk without making any assumptions about correlations between loans, by applying Carey’s [Carey, Mark, 1998. Credit risk in private debt portfolios. Journal of Finance LIII (4), 1363–1387] non-parametric Monte Carlo re-sampling method. We find substantial differences between the implied loss distributions of two banks with equal “regulatory” risk profiles; both expected losses and the credit loss rates at a wide range of loss distribution percentiles vary considerably. Such variation will translate into different levels of required economic capital. Our results also confirm the quantitative importance of size for portfolio credit risk: for common parameter values, we find that tail risk can be reduced by up to 40% by doubling portfolio size. Our analysis makes clear that not only the formal design of a rating system, but also the way in which it is implemented (e.g. a rating grade composition; the degree of homogeneity within rating classes) can be quantitatively important for the shape of credit loss distributions and thus for banks’ required capital structure. The evidence of differences between lenders also hints at the presence of differentiated market equilibria, that are more complex than might otherwise be supposed: different lending or risk management “styles” may emerge and banks strike their own balance between risk-taking and (the cost of) monitoring (that risk).

Stock index tracking requires to build a portfolio of stocks (a replica) whose behavior is as close as possible to that of a given stock index. Typically, much fewer stocks should appear in the replica than in the index, and there should be no low frequency or integrated (persistent) components in the tracking error. The latter property is not satisfied by many commonly used methods for index tracking. These are based on the in-sample minimization of a loss function, but do not take into account the dynamic properties of the index components. Moreover, most existing methods do not take into account the known structure of the index weight system. In this paper we represent the index components with a dynamic factor model. In this model the price of each stock in the index is driven by a set of common and idiosyncratic factors. Factors can be either integrated or stationary. We develop a procedure that, in a first step, builds a replica that is driven by the same persistent factors as the index. This procedure is grounded in recent results which suggest the application of principal component analysis for factor estimation even for integrated processes. In a second step, it is also possible to refine the replica so that it minimizes a specific loss function, as in the traditional approach. In both steps the replica weights depend on the existing information on the index weights system. An extended set of Monte Carlo simulations and an application to the most widely used index in the European stock market, the EuroStoxx50 index, provide substantial support for our approach.

Systemic crises can have grave consequences for investors in international equity markets, because they cause the risk-return trade-off to deteriorate severely for a longer period. We propose a novel approach to include the possibility of systemic crises in asset allocation decisions. By combining regime switching models with Merton [Merton, R.C., 1969. Lifetime portfolio selection under uncertainty: The continuous time case. Review of Economics and Statistics 51, 247–257]-style portfolio construction, our approach captures persistence of crises much better than existing models. Our analysis shows that incorporating systemic crises greatly affects asset allocation decisions, while the costs of ignoring them is substantial. For an expected utility maximizing US investor, who can invest globally these costs range from 1.13% per year of his initial wealth when he has no prior information on the likelihood of a crisis, to over 3% per month if a crisis occurs with almost certainty. If a crisis is taken into account, the investor allocates less to risky assets, and particularly less to the crisis prone emerging markets.