On the efficacy of financial regulations.

Regulatory failures have been a significant contributor to the financial crisis, but that does not automatically mean more regulation is called for. The crisis happened because fi nancial institutions and the whole economy used seemingly infi nite amounts of cheap credit to create an asset price bubble. The banks played their part by creating all these complex structured products that continue causing difficulties. They did this under direct regulatory oversight. Such excessive credit expansion is how most financial crises have played out throughout history. The exact same process can be prevented from happening in the future, but surely the next crisis will take a different form. It will be something completely unforeseen. One cannot regulate against such unforeseen events. The crisis has its roots in the most regulated parts of the financial system, the banks, whilst the least regulated part, the hedge funds, are mostly innocent. Is the problem lack of regulation? Or is the problem lack of understanding on how to regulate financial institutions properly? Depending upon the answer to the question, the correct approach to future financial regulations will be very different. The unique element this time around has been the extensive use of statistical models to forecast prices, and risk as well as to price complex assets. It was the models that failed. Such models embed an assumption of risk being exogenous; market participants react to the financial system but do not change it. In practice, this is nonsense. Market participants, especially in a crisis, receive the same signals and react in a similar way; they exert significant price impact resulting in risk being endogenous. This implies financial risk models are the least reliable when we need them the most and that regulation by risk sensitivity, such as risk sensitive bank capital, may increase financial instability. The root causes of the crisis are the same as in most financial crises throughout history. These crises have happened under a wide range of regulatory mechanisms. Blaming the crisis on a narrow set of obvious regulatory causes, such as bonuses, hedge funds, universal banking, shadow banking, structured credit, lack of regulations, inadequate risk management is attacking a straw man. It takes the focus away from the necessary detailed examination of the causes of fi nancial instability, which is the only way to design effective regulatory mechanisms. We do not clearly understand what went wrong, and know even less how to design regulations to prevent such episodes from happening in the future, whilst maintaining the effi ciency of the financial system. This is why it would be preferable to study what went wrong and then in a few years carefully change regulations at a time when we know more. There is no hurry, we still haven’t solved this crisis and the next one will not come immediately after the current crisis. The costs of inappropriate regulations are high and we do have the time to wait.

Regulating hedge funds.

Due to the ever-increasing amounts under management and their unregulated and opaque nature, hedge funds have emerged as a key concern for policymakers. While until now, hedge funds have been left essentially unregulated, we are seeing increasing calls for regulation for both microprudential and macroprudential reasons. In our view, most calls for the regulation of hedge funds are based on a misperception of the effectiveness of financial regulations, perhaps coupled with a lack of understanding of the positive contribution of hedge funds to the financial system. There are real concerns about consumer protection following from the expansion of the consumer base. However, it would be misguided to relax accreditation criteria. A more important issue is the investment of regulated institutions, in particular pension funds, in hedge funds. Since such institutions to enjoy direct or indirect government protection, the investment in hedge funds has to be regulated. However, such regulations are best implemented on the demand side by the pension fund regulator, rather than by directly regulating the hedge fund advisors themselves. Hedge funds provide considerable benefits, not only to their investors and advisors, but more importantly to the economy at large by facilitating price discovery, market efficiency, diversifi cation, and by being potentially able to put a floor under a crisis, a function not easily implemented by regulated institutions due to a minimum capital ratios, relative performance evaluation and other considerations. It would however be imprudent to leave hedge fund advisors completely unregulated since the failure of a systematically important hedge fund has the potential to create such uncertainty as to impede trading and in a worst case scenario cause significant damage to the real economy. These issues cannot be addressed by standard regulatory methodology such as disclosure and activity restrictions. Indeed, supervisors would be well advised to leave the hedge fund sector unregulated in their normal day-to-day activities. However, the regulator needs to have the power to resolve the informational uncertainty caused by the failure of a systematically important hedge funds. Prime brokers and other client banks would in such a scenario have a de facto or a de jure obligation to participate in the expedient removal of the uncertainty. To this end targeted consultation and contingency planning is essential.

The method of moments ratio estimator for the tail shape parameter

The so-called Hill estimator for the shape parameter of the tail distribution is known to be downwardly biased. The Hill estimator is a moment estimator, based on the first conditional moment of the highest logarithmically transformed data. We propose a new estimator for the tail index based on the ratio of the second to the first conditional moment. This estimator has a smaller bias than the Hill estimator. We provide simulation results that demonstrate a sizable reduction in bias when a is large, while the MSE is moderated as well. The new estimator is applied to stock return data in order to resolve a long standing issue in economics

Tail index and quantile estimation with very high frequency data

A precise estimation of the tail shape of forex returns is of critical importance for proper risk assessment. We improve upon the efficiency of conventional estimators that rely on a first order expansion of the tail shape, by using the second order expansion. Here we advocate a moments estimator for the second term. The paper uses both Monte Carlo simulations and the high frequency foreign exchange recordings collected by the Olsen corporation to illustrate the technique

Incentives for Effective Risk Management

Under the new Capital Accord banks can choose between different type of risk management systems. Using a stylized model of risk management systems which differ in quality and by modelling the relationship between the bank board and the risk manager, we consider the incentives for the adoption of a particular system. We show that in some cases banks may adversely adopt an unsophisticated risk management system in order to evade regulation

QMM. A Quarterly Macroeconomic Model of the Icelandic Economy

This paper documents and describes Version 2.0 of the Quarterly Macroeconomic Model of the Central Bank of Iceland (QMM). QMM and the underlying quarterly database have been under construction since 2001 at the Research and Forecasting Division of the Economics Department at the Bank and was first implemented in the forecasting round for the Monetary Bulletin 2006.1 in March 2006. QMM is used by the Bank for forecasting and various policy simulations and therefore plays a key role as an organisational framework for viewing the medium-term future when formulating monetary policy at the Bank. This paper is mainly focused on the short and medium-term properties of QMM. Steady state properties of the model are documented in a paper by Daníelsson (2009).

Optimal Portfolio Allocation under a Probabilistic Risk Constraint and the Incentives for Financial Innovation

We derive, in a complete markets environment, an investor's optimal portfolio allocation subject to both a budget constraint and a probabilistic risk constraint. We demonstrate that the set of feasible portfolios need not be connected or convex, while the number of local optima increases exponentially with the number of securities implying that finding the optimal portfolio is computationally complex (NP hard). The resulting optimal portfolio allocation may not be monotonic in the state-price density. A novel type of financial innovation, which splits states of nature, is shown to weakly enhance welfare, restore monotonicity in the state-price density, and may reduce complexity

Using a bootstrap method to choose the sample fraction in tail index estimation

Tail index estimation depends for its accuracy on a precise choice of the sample fraction, i.e. the number of extreme order statistics on which the estimation is based. A complete solution to the sample fraction selection is given by means of a two step subsample bootstrap method. This method adaptively determines the sample fraction that minimizes the asymptotic mean squared error. Unlike previous methods, prior knowledge of the second order parameter is not required. In addition, we are able to dispense with the need for a prior estimate of the tail index which already converges roughly at the optimal rate. The only arbitrary choice of parameters is the number of Monte Carlo replications

Leverage-induced systemic risk under Basle II and other credit risk policies

We use a simple agent based model of value investors in financial markets to test three credit regulation policies. The first is the unregulated case, which only imposes limits on maximum leverage. The second is Basle II and the third is a hypothetical alternative in which banks perfectly hedge all of their leverage-induced risk with options. When compared to the unregulated case both Basle II and the perfect hedge policy reduce the risk of default when leverage is low but increase it when leverage is high. This is because both regulation policies increase the amount of synchronized buying and selling needed to achieve deleveraging, which can destabilize the market. None of these policies are optimal for everyone: Risk neutral investors prefer the unregulated case with low maximum leverage, banks prefer the perfect hedge policy, and fund managers prefer the unregulated case with high maximum leverage. No one prefers Basle II.Comment: 27 pages, 8 figure