Federal Reserve Bank of Atlanta

Journal Article A Closed-Form GARCH Option Valuation Model Get access Steven L. Heston, Steven L. Heston Goldman Sachs & Company Search for other works by this author on: Oxford Academic Google Scholar Saikat Nandi Saikat Nandi Research Department, Federal Reserve Bank of Atlanta Address all correspondence to Saikat Nandi, Research Department, Federal Reserve Bank of Atlanta, 104 Marietta Street, N.W, Atlanta, GA 30303, or e-mail: saikat.u.nandi@atl.frb.org. Search for other works by this author on: Oxford Academic Google Scholar The Review of Financial Studies, Volume 13, Issue 3, July 2000, Pages 585–625, https://doi.org/10.1093/rfs/13.3.585 Published: 15 June 2015

We study how unexpected changes in uncertainty about fiscal policy affect economic activity. First, we estimate tax and spending processes for the United States with time-varying volatility to uncover evidence of time-varying volatility. Second, we estimate a VAR for the US economy using the time-varying volatility found in the previous step. Third, we feed the tax and spending processes into an otherwise standard New Keynesian model. Both in the VAR and in the model, we find that unexpected changes in fiscal volatility shocks can have a sizable adverse effect on economic activity. An endogenous increase in markups is a key mechanism. (JEL E12, E23, E32, E52, E62)

This article revisits the no-recall assumption in job search models with take-it-or-leave-it offers. Workers who can recall previously encountered potential employers in order to engage them in Bertrand bidding have a distinct advantage over workers without such attachments. Firms account for this difference when hiring a worker. When a worker first meets a firm, the firm offers the worker a sufficient share of the match rents to avoid a bidding war in the future. The pair share the gains to trade. In this case, the Diamond paradox no longer holds.

Structural vector autoregressions (SVARs) are widely used for policy analysis and to provide stylized facts for dynamic stochastic general equilibrium (DSGE) models; yet no workable rank conditions to ascertain whether an SVAR is globally identified have been established. Moreover, when nonlinear identifying restrictions are used, no efficient algorithms exist for small-sample estimation and inference. This paper makes four contributions towards filling these important gaps in the literature. First, we establish general rank conditions for global identification of both identified and exactly identified models. These rank conditions are sufficient for general identification and are necessary and sufficient for exact identification. Second, we show that these conditions can be easily implemented and that they apply to a wide class of identifying restrictions, including linear and certain nonlinear restrictions. Third, we show that the rank condition for exactly identified models amounts to a straightforward counting exercise. Fourth, we develop efficient algorithms for small-sample estimation and inference, especially for SVARs with nonlinear restrictions.

We show how changes in the volatility of the real interest rate at which small open emerging economies borrow have an important effect on variables like output, consumption, investment, and hours. We start by documenting the strong evidence of time-varying volatility in the real interest rates faced by four emerging economies: Argentina, Brazil, Ecuador, and Venezuela. We estimate a stochastic volatility process for real interest rates. Then, we feed this process in a standard small open economy business cycle model. We find that an increase in real interest rate volatility triggers a fall in output, consumption, investment, hours, and debt. (JEL E13, E20, E32, E43, F32, F43, 011)

We consider two kinds of answers to the title question: Do random shifts in monetary policy account for historical recessions, and would changes in the systematic component of monetary policy have allowed reductions in inflation or output variance without substantial costs. The answer to both questions is no. We use weak identifying assumptions and include extensive discussion of these assumptions, including a completely specified dynamic stochastic equilibrium model in which our identifying assumptions can be shown to be approximately satisfied.

This article uses a simple New Keynesian dynamic stochastic general equilibrium model as a prior for a vector autoregression, and shows that the resulting model is competitive with standard benchmarks in terms of forecasting, and can be used for policy analysis.

We study how monetary policy in China influences banks’ shadow banking activities. We develop and estimate the endogenously switching monetary policy rule that is based on institutional facts and at the same time tractable in the spirit of Taylor (1993). This development, along with two newly constructed micro banking datasets, enables us to establish the following empirical evidence. Contractionary monetary policy during 2009–2015 caused shadow banking loans to rise rapidly, offsetting the expected decline of traditional bank loans and hampering the effectiveness of monetary policy on total bank credit. We advance a theoretical explanation of our empirical findings. (JEL E32, E52, G21, O16, O23, P24, P34)

This paper shows how particle filtering facilitates likelihood-based inference in dynamic macroeconomic models. The economies can be non-linear and/or non-normal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those characterizing preferences and technology, and to compare different economies. Both tasks can be implemented from either a classical or a Bayesian perspective. We illustrate the technique by estimating a business cycle model with investment-specific technological change, preference shocks, and stochastic volatility. Copyright 2007, Wiley-Blackwell.

This article provides new tools for the evaluation of dynamic stochastic general equilibrium (DSGE) models and applies them to a large-scale new Keynesian model. We approximate the DSGE model by a vector autoregression, and then systematically relax the implied cross-equation restrictions and document how the model fit changes. We also compare the DSGE model's impulse responses to structural shocks with those obtained after relaxing its restrictions. We find that the degree of misspecification in this large-scale DSGE model is no longer so large as to prevent its use in day-to-day policy analysis, yet is not small enough to be ignored.

The dynamics of a linear (or linearized) dynamic stochastic economic model can be expressed in terms of matrices (A, B, C, D) that define a state space system for a vector of observables. An associated state space system (A, ^ B,C, ^D) determines a vector autoregression for those same observables. We present a simple condition for checking when these two state space systems match up and when they do not when there are equal numbers of economic and VAR shocks. We illustrate our condition with a permanent income example. (JEL C32, E32)

ABSTRACT We present evidence of a risk‐taking channel of monetary policy for the U.S. banking system. We use confidential data on banks’ internal ratings on loans to businesses over the period 1997 to 2011 from the Federal Reserve's Survey of Terms of Business Lending. We find that ex ante risk‐taking by banks (measured by the risk rating of new loans) is negatively associated with increases in short‐term interest rates. This relationship is more pronounced in regions that are less in sync with the nationwide business cycle, and less pronounced for banks with relatively low capital or during periods of financial distress.

The authors examine the economic effects of small business credit scoring (SBCS) and find that it is associated with expanded quantities, higher average prices, and greater risk levels for small business credits under $100,000. These findings are consistent with a net increase in lending to relatively risky "marginal borrowers" who would otherwise not receive credit, but who would pay relatively high prices when they are funded. The authors also find that 1) bank-specific and industrywide learning curves are important; 2) SBCS effects differ for banks that adhere to "rules" versus "discretion" in using the technology; and 3) SBCS effects differ for slightly larger credits.

This paper reviews the extant empirical studies of financial innovation. Adopting broad criteria and spanning a long time horizon, we found surprisingly few studies (39), with most (23) having been conducted since 1998. Especially striking is that only two studies test hypotheses advanced in many descriptive articles as to the economic/environmental conditions that encourage financial innovation. We offer conjectures as to why empirical studies of financial innovation are comparatively rare, including as a culprit the absence of accessible data. We urge financial regulators to undertake more surveys of financial innovation and to make the resulting data available to researchers.

In this paper, we develop algorithms to independently draw from a family of conjugate posterior distributions over the structural parameterization when sign and zero restrictions are used to identify structural vector autoregressions (SVARs). We call this family of conjugate posteriors normal‐generalized‐normal. Our algorithms draw from a conjugate uniform‐normal‐inverse‐Wishart posterior over the orthogonal reduced‐form parameterization and transform the draws into the structural parameterization; this transformation induces a normal‐generalized‐normal posterior over the structural parameterization. The uniform‐normal‐inverse‐Wishart posterior over the orthogonal reduced‐form parameterization has been prominent after the work of Uhlig (2005). We use Beaudry, Nam, and Wang's (2011) work on the relevance of optimism shocks to show the dangers of using alternative approaches to implementing sign and zero restrictions to identify SVARs like the penalty function approach. In particular, we analytically show that the penalty function approach adds restrictions to the ones described in the identification scheme.

We analyze the effects of competition on price dispersion in the airline industry, using panel data from 1993:Q1 through 2006:Q3. Competition has a negative effect on price dispersion, in line with the textbook treatment of price discrimination. This effect is pronounced for routes with consumers characterized by relatively heterogeneous elasticities of demand. On routes with a homogeneous customer base, the effects of competition on price dispersion are smaller. Our results contrast with those of Borenstein and Rose, who found that price dispersion increases with competition. We reconcile the different results by showing that the cross-sectional estimator suffers from omitted-variable bias. (c) 2009 by The University of Chicago. All rights reserved.

Unprecedented levels of US subprime mortgage defaults precipitated a severe global financial crisis in late 2008, plunging much of the industrialized world into a deep recession. However, the fundamental reasons for why US mortgages defaulted at such spectacular rates remain largely unknown. This paper presents empirical evidence showing that the ability to perform basic mathematical calculations is negatively associated with the propensity to default on one's mortgage. We measure several aspects of financial literacy and cognitive ability in a survey of subprime mortgage borrowers who took out loans in 2006 and 2007, and match them to objective, detailed administrative data on mortgage characteristics and payment histories. The relationship between numerical ability and mortgage default is robust to controlling for a broad set of sociodemographic variables, and is not driven by other aspects of cognitive ability. We find no support for the hypothesis that numerical ability impacts mortgage outcomes through the choice of the mortgage contract. Rather, our results suggest that individuals with limited numerical ability default on their mortgage due to behavior unrelated to the initial choice of their mortgage.

ABSTRACT Over the years, many asset pricing studies have employed the sample cross‐sectional regression (CSR) R 2 as a measure of model performance. We derive the asymptotic distribution of this statistic and develop associated model comparison tests, taking into account the impact of model misspecification on the variability of the CSR estimates. We encounter several examples of large R 2 differences that are not statistically significant. A version of the intertemporal capital asset pricing model (CAPM) exhibits the best overall performance, followed by the Fama–French three‐factor model. Interestingly, the performance of prominent consumption CAPMs is sensitive to variations in experimental design.

BACKGROUND: Respiratory syncytial virus (RSV) is a major cause of hospitalized acute respiratory illness (ARI) among young children. With RSV vaccines and immunoprophylaxis agents in clinical development, we sought to update estimates of US pediatric RSV hospitalization burden. METHODS: Children <5 years old hospitalized for ARI were enrolled through active, prospective, population-based surveillance from November 1, 2015, to June 30, 2016, at 7 US pediatric hospital sites. Clinical information was obtained from parent interviews and medical records. Midturbinate nasal and throat flocked swabs were collected and tested for RSV by using molecular diagnostic assays at each site. We conducted descriptive analyses and calculated population-based rates of RSV-associated hospitalizations. RESULTS: Among 2969 hospitalized children included in analyses, 1043 (35%) tested RSV-positive; 903 (87%) children who were RSV-positive were <2 years old, and 526 (50%) were <6 months old. RSV-associated hospitalization rates were 2.9 per 1000 children <5 years old and 14.7 per 1000 children <6 months old; the highest age-specific rate was observed in 1-month-old infants (25.1 per 1000). Most children who were infected with RSV (67%) had no underlying comorbid conditions and no history of preterm birth. CONCLUSIONS: During the 2015-2016 season, RSV infection was associated with one-third of ARI hospitalizations in our study population of young children. Hospitalization rates were highest in infants <6 months. Most children who were RSV-positive had no history of prematurity or underlying medical conditions, suggesting that all young children could benefit from targeted interventions against RSV.

In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions. This paper develops Bayesian methods for computing the exact finite-sample distribution of conditional forecasts. It broadens the class of conditional forecasts to which the methods can be applied. The methods work for both structural and reduced-form VAR models and, in contrast to common practices, account for parameter uncertainty in finite samples. Empirical examples under both a flat prior and a reference prior are provided to show the use of these methods.