Springer Science and Business Media LLC

We show the dynamics of diverse beliefs is the primary propagation mechanism of volatility in asset markets. Hence, we treat the characteristics of the market beliefs as a primary, primitive, explanation of market volatility. We study an economy with stock and riskless bond markets and formulate a financial equilibrium model with diverse and time varying beliefs. Agents’ states of belief play a key role in the market, requiring an endogenous expansion of the state space. To forecast prices agents must forecast market states of belief which are beliefs of ‘‘others’’ hence our equilibrium embodies the Keynes ‘‘Beauty Contest.’’ A ‘‘market state of belief’’ is a vector which uniquely identifies the distribution of conditional probabilities of agents. Restricting beliefs to satisfy the rationality principle of Rational Belief (see Kurz, 1994, 1997) our economy replicates well the empirical record of the (i) moments of the price/dividend ratio, risky stock return, riskless interest rate and the equity premium; (ii) Sharpe ratio and the correlation between risky returns and consumption growth; (iii) predictability of stock returns and price/dividend ratio as expressed by: (I) Variance Ratio statistic for long lags, (II) autocorrelation of these variables, and (III) mean reversion of the risky returns and the predictive power of the price/dividend ratio. Also, our model explains the presence of stochastic volatility in asset prices and returns. Two properties of beliefs drive market volatility: (i) rationalizable over confidence implying belief densities with fat tails, and (ii) rationalizable asymmetry in frequencies of bull or bear states.

In specifying a finite factor model for the term structure of interest rates, one usually begins by modeling the dynamics of the underlying factors. In most cases, this is sufficient to completely determine the term structure model. However, a point that is often overlooked is that seemingly different specifications of the factor dynamics may generate indistinguishable term structure models, in the sense that they produce pathwise identical bond prices. Consequently, it is important to be able to determine, at the level of factor dynamics, the conditions under which the models they generate are indistinguishable. In the case of time-homogeneous affine term structure models (ATSMs), such conditions were first described in Dai and Singleton (J Finance 55:1943–1978, 2000). In this paper, we formalize and extend their results to a class of time-inhomogeneous ATSMs, and obtain a simple method for determining the indistinguishability of these models in terms of the underlying factor dynamics.

This paper studies the valuation of companies going public and defines a methodology to infer the growth expectations implicit in the prices of their Initial Public Offering (IPO). The proposed reverse-engineered DCF model is operable by individual investors, as it does not require access to private information or sell-side analysts’ forecasts. Applying the procedure to a sample of IPOs in three European countries (France, Italy, and Germany), we estimate the cash flow growth implied by offer prices and examine the bias of implied growth in comparison to the realized. We find that the estimated growth in cash flow is much higher than its actual realization, with the median IPO firm overvalued at the offering by 74%. Estimation errors increase with IPO firms’ leverage and underpricing, and decrease with age, size, and book-to-market ratios. Further tests find that post-IPO stock returns are lower for issues whose implied growth is more upward biased.

Many investors believe that they can effectively reduce risk by, among other ways, holding large combinations of investment assets. The purpose of this paper is to develop asymptotic approximations of the windfall and shortfall probabilities for an optimal portfolio of risky assets as the number of the assets becomes sufficiently large. We start by providing some heuristics to motivate our problem, then proceed to prove general large deviations theorems. We also present specific results with an application to the multivariate normal case. Both a theoretical analysis of the method and an empirical application justify the diversification tenet of the allocation strategies that many hedge funds and pension funds tend to adopt nowadays.

This paper presents theoretical work linking money demand to the perceptions of households about the risk that domestic currency may become inconvertible or that it may be devalued. An empirical investigation of the size of this effect is carried out using monthly data for Korea to estimate an augmented demand-for-money equation. It is found that the fear of inconvertibility arising from the 1997 Korean currency crisis may have caused broad money demand to fall by 4–5% points,equivalent to the loss of reserves of $6–7.5 billion (or about 30% of reserves as measured at end-November 1997).

In this paper we develop a multicriteria decision aid model, to investigate whether it is possible to replicate the credit ratings of Fitch on Asian banks using publicly available data. The model is developed with the Multi-group Hierarchical DIScrimination (MHDIS) approach, following a tenfold cross validation procedure. Five financial variables are selected from a list of nineteen ones through factor analysis. An additional set of five non-financial variables covering ownership, corporate governance, auditing, strength of bank’s franchise and its banking environment is also being used. The results show that equity/customer and short term funding, net interest margin and return on average equity are the most important financial variables. The number of shareholders, the number of subsidiaries and the banking environment of the country in which the banks operate are the most important non-financial ones. In terms of classification accuracies, the results show that the MHDIS model can replicate the credit ratings of Fitch with a satisfactory accuracy and is more efficient than discriminant analysis and ordered logistic regression that are used for comparison purposes.

This paper presents an analysis of diversification and portfolio value at risk for heavy-tailed dependent risks in models with multiple common shocks. We show that, in the framework of value at risk comparisons, diversification is optimal for moderately heavy-tailed dependent risks with common shocks and finite first moments, provided that the model is balanced, i.e., that all the risks are available for portfolio formation. However, diversification is inferior in balanced extremely heavy-tailed risk models with common factors. Finally, in several unbalanced dependent models, diversification is optimal, even though there is extreme heavy-tailedness in common shocks or in idiosyncratic parts of the risks. Analogues of the obtained results further hold for efficiency comparisons of linear estimators in random effects models with dependent and heavy-tailed observations.

Studying the dynamics of deposits is important for three reasons: first, it serves as an important component of liquidity stress testing; second, it is crucial to asset-liability management exercises and the allocation between liquid and illiquid assets; third, it is the support for a Liquidity at Risk methodology. Current models are based on $$\textit{AR}(1)$$ processes that often underestimate liquidity risk. Thus, a bank relying on those models may face failure in an event of crisis. We propose an alternative approach for modeling deposits, using panel data and a momentum term. The model enables the simulation of a variety of deposit trajectories, including episodes of financial distress, showing much higher drawdowns and realistic liquidity at risk estimates, as well as density plots that present a wide range of possible values, corresponding to booms and financial crises. Therefore, this methodology is more suitable for liquidity management at banks, as well as for conducting liquidity stress tests.

In this paper, we estimate the probability of a financial institution breaching the Common Equity Tier 1 capital under Basel III rules. We do so by applying the Merton model, where balance sheet data and market data are used to match the probability of default implied by the model with the probability of default implied by market quotations for credit default swaps. We provide an empirical analysis for several banks classified by the Financial Stability Board and the Basel Committee on Banking Supervision as Global Systemically Important Financial Institutions, evaluating how the probability of breaching the Common Equity Tier 1 Capital evolved from 2005 to 2015. We find that higher Common Equity Tier 1 Capital ratios do not necessarily imply lower probabilities of breaching capital requirements and vice versa. We also focus on the asset volatility calibrated according to our model and we find that it appears to be a good proxy for the risk-weighted asset density.