The Effect of Market Structure on the Incentives to Quote Aggressively: An Empirical Study of Nasdaq Market Makers

We use data on Nasdaq stocks to study arguments that preferencing reduces incentives to quote competitively. We examine a market maker's volume as a function of various measures of quoting aggressiveness. We find that more aggressive quoting does indeed result in more business. We also examine the relation between volume and quote aggressiveness as a function of the competitiveness. We find that in less (more) competitive markets, increased quote aggressiveness has a smaller (larger) impact on market share. We argue that preferencing arrangements could be more harmful to public investors in markets where competition is weak.     

Portfolio optimization with stochastic dominance constraints

We consider the problem of constructing a portfolio of finitely many assets whose return rates are described by a discrete joint distribution. We propose a new portfolio optimization model involving stochastic dominance constraints on the portfolio return rate. We develop optimality and duality theory for these models. We construct equivalent optimization models with utility functions. Numerical illustration is provided.     

Do Central Banks React to House Prices?

We estimate the Federal Reserve's, the Bank of England's, and the Bank of Japan's responses to house prices. We show that generalized method of moments estimates of a Taylor rule augmented with house prices are biased and dispersed. We then use full‐information methods and estimate the policy rule together with a VAR for the nonpolicy variables. These estimates are also biased. We propose an alternative approach and estimate a dynamic stochastic general equilibrium model embedded with a monetary rule with a direct response to house prices. We find that house prices played a separate role in the reaction functions of these central banks.     

The attributes,behavior, and performance of U.S. mutual funds

This article examines the risk and return characteristics of U.S. mutual funds. We employ an equilibrium version of the Arbitrage Pricing Theory (APT) and a principal-components-based statistical technique to identify performance benchmarks. We also consider the Capital Asset Pricing Model (CAPM) as an alternative. We implement a procedure for overcoming the rotational indeterminacy of factor models. This procedure is a hybrid of statistical factor estimation and prespecification of factors. We estimate measures of timing ability for the CAPM and extend it to the APT. We find that this timing test is misspecified due to noninformation-based changes in mutual fund betas. We develop a modification of the timing measure that, under certain conditions, distinguishes true timing ability from noninformation-based beta changes.     

Dynamic copula models for the spark spread

We propose a non-symmetric copula to model the evolution of electricity and gas prices by a bivariate non-Gaussian autoregressive process. We identify the marginal dynamics as driven by normal inverse Gaussian processes, estimating them from a series of observed UK electricity and gas spot data. We estimate the copula by modeling the difference between the empirical copula and the independent copula. We then simulate the joint process and price options written on the spark spread. We find that option prices are significantly influenced by the copula and the marginal distributions, along with the seasonality of the underlying prices.     

Intra-lndustry Effects of Corporate Capital Investment Announcements

We examine how announcements of corporate capital investments by one firm affect the stock prices of its competitors. We find that on average, rivals experience a signifi cantly negative valuation effect. The results suggest that for the sample as a whole, the competitive effect dominates the contagion effect. We further examine various factors that could potentially explain the heterogeneous intra-industry effects of capital investment announcements. We find that rivals' share prices are more adversely affected when the announcer experiences a higher announcement effect or is the first mover in the industry. We also show that rivals experience a greater wealth loss when they have poorer investment opportunities or higher financial leverage.     

Pricing Perpetual Put Options by the Black–Scholes Equation with a Nonlinear Volatility Function

We investigate qualitative and quantitative behavior of a solution of the mathematical model for pricing American style of perpetual put options. We assume the option price is a solution to the stationary generalized Black–Scholes equation in which the volatility function may depend on the second derivative of the option price itself. We prove existence and uniqueness of a solution to the free boundary problem. We derive a single implicit equation for the free boundary position and the closed form formula for the option price. It is a generalization of the well-known explicit closed form solution derived by Merton for the case of a constant volatility. We also present results of numerical computations of the free boundary position, option price and their dependence on model parameters.     

COGNITIVE DISSONANCE AND MUTUAL FUND INVESTORS

We present evidence from questionnaire responses of mutual fund investors about recollections of past fund performance. We find that investor memories exhibit a positive bias, consistent with current psychological models. We find that the degree of bias is conditional upon previous investor choice, a phenomenon related to the well-known theory of cognitive dissonance. Psychological and economic frictions in the mutual fund industry are examined via a cross-sectional study of equity mutual funds. We find an unusually high frequency of poorly performing funds, consistent with investor “inertia.” We also examine the differential responses of investment dollars to past performance, controlling for survivorship. These show that the effect is confined to the top quartile. We find little evidence that the response to poor performance is unusual.     

Convergence,capital accumulation and the nominal exchange rate

This paper develops a flexible price, two-sector growth model with a nominal side to study the role of the exchange rate in transition dynamics. We adopt a standard small open economy model with traded and nontraded goods, where the engines of growth are exogenous productivity improvements and capital accumulation. We enhance this standard framework by adding a preference for real money holdings, captured by money-in-the-utility. We follow Schmitt-Grohé and Uribe (2003) and assume that the interest rate on bonds issued by the small open economy is debt-dependent, and interpret it as a simple financial friction. We show analytically that the choice of the exchange rate regime influences the transition dynamics of a small open economy through the balance sheet of the central bank. We then calibrate the model to explore the quantitative significance of our results. We find that the choice of the exchange rate regime has significant and lasting effects on prices, consumption, investment and sectoral allocations, and the composition of financial assets.     

Multivariate autoregressive modeling of time series count data using copulas

We introduce the Multivariate Autoregressive Conditional Double Poisson model to deal with discreteness, overdispersion and both auto and cross-correlation, arising with multivariate counts. We model counts with a double Poisson and assume that conditionally on past observations the means follow a Vector Autoregression. We resort to copulas to introduce contemporaneous correlation. We apply it to the study of sector and stock-specific news related to the comovements in the number of trades per unit of time of the most important US department stores traded on the NYSE. We show that the market leaders inside a specific sector are related to their size measured by their market capitalization.     

VOLATILITY TRANSMISSION AND PATTERNS IN BUND FUTURES

We analyze intraday volatility behavior for the Bund futures contract that is traded simultaneously at two competing exchanges. We investigate the transmission of volatility between the exchanges. We find that the lead/lag relations are restricted to a few minutes and do not reveal a dominant leader. We then analyze patterns in intraday volatility. We find that volatility behaves similarly at both exchanges; i.e., it decreases from the opening until early afternoon and increases thereafter. The same pattern is detected in explanatory variables such as traded volume and time-between-trades.     

Price discovery in spot and futures markets: a reconsideration

We reconsider the issue of price discovery in spot and futures markets. We use a threshold error correction model to allow for arbitrage opportunities to have an impact on the return dynamics. We estimate the model using quote midpoints, and we modify the model to account for time-varying transaction costs. We find that (a) the futures market leads in the process of price discovery and (b) the presence of arbitrage opportunities has a strong impact on the dynamics of the price discovery process.     

Style Drift in Private Equity

Abstract:  We introduce the concept of style drift to private equity investment. We present theory and evidence pertaining to style drifts in terms of a fund manager's stated focus on particular stages of entrepreneurial development. We develop a model that derives conditions under which style drifts are less likely among younger fund managers. We also show ways in which changes in market conditions can affect style drifts, and differences for funds committed to early-stage investments compared to funds committed to late-stage investments. We find some evidence of a positive relation between style drifting and investment performance.     

Correlation dynamics in European equity markets

We examine correlation dynamics using daily data from 1993 to 2002 on the five largest Euro-zone stock market indices. We also study, for comparison, the correlations of a sample of individual stocks. We employ both unconditional and conditional estimation methodologies, including estimation of the conditional correlations using the symmetric and asymmetric DCC-MVGARCH model, extended with the inclusion of a deterministic time trend. We confirm the presence of a structural break in market index correlations reported by previous researchers and, using an innovative likelihood-based search, we find that the it occurred at the beginning the process of monetary integration in the Euro-zone. We find mixed evidence of asymmetric correlation reactions to news of the type modelled by conventional asymmetric DCC-MVGARCH specifications.     

Dynamic efficiency of stock markets and exchange rates

We use generalized Hurst exponents to investigate long-range dependence across countries that have implemented an inflation targeting monetary policy regime and have a floating currency regime. We show that the degree of long-range dependence has changed after the 2008 crisis for equity markets but not as much for exchange rate markets. We compare results for developed and emerging economies and find that there still are some important differences but not as they were before the crisis. We also include an additional set of relevant countries and find that our results are more pronounced for inflation targeters. We discuss several implications of these results.     

Bootstrapping Parametric Models of Mortality

We consider a general problem of modeling a mortality law of a population of failing units with some parametric function. In this setting we define a mortality table of crude rates as a statistical estimator with multinomial distribution and show its consistency as well as asymptotic normality. We further derive the statistical properties of parameter estimators in a parametric mortality model based on a weighted square loss function. We use the obtained results to study consistency and appropriateness of the parametric bootstrap method in our setting. We derive the conditions on the assumed parametric mortality law and the loss function, under which the bootstrap is consistent for estimating the model parameters, their standard errors and corresponding confidence intervals. We apply our results to a model of Aggregate US Mortality Table based on a so called mixture of extreme value distributions suggested by Carriere ().     

What we spend and what we get: Public and private provision of crime prevention and criminal justice

In this paper, we consider a number of issues regarding crime prevention and criminal justice. We begin by considering how crime is measured and present both general and specific evidence on the level of crime in a variety of countries. Crime is pervasive and varies substantially across countries. We outline the arguments for some public role in crime prevention, enforcement, prosecution, defence, adjudication and punishment. We consider the relative roles of the public and private sectors in crime control and criminal justice. We discuss various measures for the effectiveness of the criminal justice system. We conclude by suggesting some potential areas for research.     

The joint distribution of Parisian and hitting times of Brownian motion with application to Parisian option pricing

We study the joint law of Parisian time and hitting time of a drifted Brownian motion by using a three-state semi-Markov model, obtained through perturbation. We obtain a martingale to which we can apply the optional sampling theorem and derive the double Laplace transform. This general result is applied to address problems in option pricing. We introduce a new option related to Parisian options, being triggered when the age of an excursion exceeds a certain time or/and a barrier is hit. We obtain an explicit expression for the Laplace transform of its fair price.     

Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio

We study the impact of machine learning (ML) models for credit default prediction in the calculation of regulatory capital by financial institutions. We do so by using a unique and anonymized database from a major Spanish bank. We first compare the statistical performance of five models based on supervised learning like Logistic Lasso, Trees (CART), Random Forest, XGBoost and Deep Learning, with a well-known model like Logit. We measure the statistical performance through different metrics, and for different sample sizes and features available. We find that ML models outperform, even when relatively low amount of data is used. We then translate this statistical performance into economic impact by estimating the savings in capital when using an advanced ML model instead of a simpler one to compute the risk-weighted assets following the Internal Ratings Based (IRB) approach. Our benchmark results show that implementing XGBoost instead of Logistic Lasso could yield savings from 12.4% to 17% in terms of regulatory capital requirements.