Measuring the effects of dollar appreciation on Asia: A FAVAR approach

Exchange rate shocks have mixed effects on economic activity in both theory and empirical VAR models. In this paper, we extend the empirical literature by considering the implications of a positive shock to the U.S. dollar in a factor-augmented vector autoregression (FAVAR) model for the U.S. and three large Asian economies: Korea, Japan and China. The FAVAR framework allows us to represent a country’s aggregate economic activity by a latent factor, generated from a broad set of underlying observable economic indicators. To control for global conditions, we also include in the FAVAR a “global conditions index,” which is another latent factor generated from the economic indicators of major trading partners. We find that a dollar appreciation shock reduces economic activity and inflation not only for the U.S. economy, but also for all three Asian economies. This result, which is robust to a number of alternative specifications, suggests that in spite of their disparate economic structures and policy regimes, the dollar appreciation shock affects the Asian economies primarily through its impact on U.S. aggregate demand; and this demand channel dominates the expenditure-switching channel that affects a country’s export competitiveness.     

Inferring Information Frequency and Quality

We develop a microstructure model that, in contrast to previousmodels, allows one to estimate the frequency and quality ofprivate information. In addition, the model produces stationaryasset price and trading volume series. We find evidence thatinformation arrives frequently within a day and that this informationis of high quality. The frequent arrival of information, whilein contrast to previous microstructure model estimates, accordswith nonmodel-based estimates and the related literature testingthe mixture-of-distributions hypothesis. To determine if theestimates are correctly reflecting the arrival of latent information,we estimate the parameters over half-hour intervals within theday. Comparison of the parameter estimates with measures ofpersistent price changes reveals that the estimates reflectthe arrival of latent information.     

Copula‐Based Formulas to Estimate Unexpected Credit Losses (The Future of Basel Accords?)

The model used to estimate the capital required to cover unexpected credit losses in financial institutions (Basel II) has some drawbacks that reduce its ability to capture potential joint extreme losses in downturns. This paper suggests an alternative approach based on Copula Theory to overcome such flaws. Similarly to Basel II, the suggested model assumes that defaults are driven by a latent variable which varies as a response to an unobserved factor. On the other hand, the use of copulas allows the identification of asymmetric dependence between defaults which has been registered in the literature. As an example, a specific copula family (Clayton) is adopted to represent the association between the latent variables and a formula to estimate potential unexpected losses at a certain level of confidence is derived. Simulations reveal that, in most of the cases, the alternative model outperforms Basel II for portfolios with right‐tail‐dependent probabilities of default (supposedly, a good representation for real loan portfolios).     

Bayesian inference for generalized linear mixed models of portfolio credit risk

The aims of this paper are threefold. First, we highlight the usefulness of generalized linear mixed models (GLMMs) in the modelling of portfolio credit default risk. The GLMM-setting allows for a flexible specification of the systematic portfolio risk in terms of observed fixed effects and unobserved random effects, in order to explain the phenomena of default dependence and time-inhomogeneity in historical default data. Second, we show that computational Bayesian techniques such as the Gibbs sampler can be successfully applied to fit models with serially correlated random effects, which are special instances of state space models. Third, we provide an empirical study using Standard and Poor's data on U.S. firms. A model incorporating rating category and sector effects, and a macroeconomic proxy variable for state-of-the-economy suggests the presence of a residual, cyclical, latent component in the systematic risk.     

Irish credit unions: Differential regulation based on business model complexity

This study examines the business model complexity of Irish credit unions using a latent class approach to measure structural performance over the period 2002 to 2013. The latent class approach allows the endogenous identification of a multi-class framework for business models based on credit union specific characteristics. The analysis finds a three class system to be appropriate with the multi-class model dependent on three financial viability characteristics. This finding is consistent with the deliberations of the Irish Commission on Credit Unions (2012) which identified complexity and diversity in the business models of Irish credit unions and recommended that such complexity and diversity could not be accommodated within a one size fits all regulatory framework. The analysis also highlights that two of the classes are subject to diseconomies of scale. This may suggest credit unions would benefit from a reduction in scale or perhaps that there is an imbalance in the present change process. Finally, relative performance differences are identified for each class in terms of technical efficiency. This suggests that there is an opportunity for credit unions to improve their performance by using within-class best practice or alternatively by switching to another class.     

The Impact of Systematic Trend and Uncertainty on Mortality and Disability in a Multistate Latent Factor Model for Transition Rates

Multiple state functional disability models do not generally include systematic trend and uncertainty. We develop and estimate a multistate latent factor intensity model with transition and recovery rates depending on a stochastic frailty factor to capture trend and uncertainty. We estimate the model parameters using U.S. Health and Retirement Study data between 1998 and 2012 with Monte Carlo maximum likelihood estimation method. The model shows significant reductions in disability and mortality rates during this period and allows us to quantify uncertainty in transition rates arising from the stochastic frailty factor. Recovery rates are very sensitive to the stochastic frailty. There is an increase in expected future lifetimes as well as an increase in future healthy life expectancy. The proportion of lifetime spent in disability on average remains stable with no strong support in the data for either morbidity compression or expansion. The model has widespread application in costing of government-funded aged care and pricing and risk management of long-term-care insurance products.     

Correlation smile matching for collateralized debt obligation tranches with α-stable distributions and fitted Archimedean copula models

As an extension of the standard Gaussian copula model to price collateralized debt obligation (CDO) tranche swaps we present a generalization of a one-factor copula model based on stable distributions. For special parameter values these distributions coincide with Gaussian or Cauchy distributions, but changing the parameters allows a continuous deformation away from the Gaussian copula. All these factor copulas are embedded in a framework of stochastic correlations. We furthermore generalize the linear dependence in the usual factor approach to a more general Archimedean copula dependence between the individual trigger variable and the common latent factor. Our analysis is carried out on a non-homogeneous correlation structure of the underlying portfolio. CDO tranche market premia, even throughout the correlation crisis in May 2005, can be reproduced by certain models. From a numerical perspective, all these models are simple, since calculations can be reduced to one-dimensional numerical integrals.     

A fully consistent,minimal model for non-linear market impact

We propose a minimal theory of non-linear price impact based on the fact that the (latent) order book is locally linear, as suggested by reaction–diffusion models and general arguments. Our framework allows one to compute the average price trajectory in the presence of a meta-order that consistently generalizes previously proposed propagator models. We account for the universally observed square-root impact law, and predict non-trivial trajectories when trading is interrupted or reversed. We prove that our framework is free of price manipulation and that prices can be made diffusive (albeit with a generic short-term mean-reverting contribution). Our model suggests that prices can be decomposed into a transient ‘mechanical’ impact component and a permanent ‘informational’ component.     

Volume and Volatility: News or Noise?

This paper presents a market microstructure model that is consistent with several empirical regularities. The model embeds separate latent ARCH‐like volatility processes: one representing movements in the underlying fundamental and one representing noise caused by the trading process. This structure allows the regularities to depend either on news or noise. The heteroskedasticity and persistence in the data are due to both ARCH‐like processes. The model has difficulty in simultaneously capturing the size and persistence of trading volume. Several extensions of the basic model, particularly including a constant level of non‐informational trading, improve the model's ability to capture the relevant characteristics of the data.     

Unconventional monetary policy and the spillovers to emerging markets

Unconventional monetary policy such as Quantitative Easing (QE) is often considered to have considerable spillover effects on emerging market economies (EME). Aims at quantifying these effects so far mostly use high-frequency data around announcement dates, panels or VAR models. This paper proposes an alternative way to estimate the effects of QE on emerging markets that allows us to include macroeconomic, i.e. low-frequency, data together with announcement dates. A Qual VAR is estimated that integrates binary information of QE announcements with an otherwise standard VAR, including US and emerging market variables. A key advantage is that the model accounts for the endogeneity and forecastability of QE announcements. The model uncovers the Fed's latent, unobservable propensity for QE and generates impulse responses for EME variables to QE shocks. The results suggest that QE has significant effects on EME's financial conditions and plays a sizable role in explaining capital inflows, equity prices and exchange rates.     

Monetary policy regimes: Implications for the yield curve and bond pricing

We develop a multivariate dynamic term structure model, which takes into account the nonlinear (time-varying) relation between interest rates and the state of the economy. In contrast to the classical term structure literature, in which nonlinearities are captured by increasing the number of latent state variables or by latent regime shifts, in our no-arbitrage framework the regimes are governed by thresholds and are directly linked to economic fundamentals. Specifically, starting from a simple monetary policy model for the short rate, we introduce a parsimonious and tractable model for the yield curve, which takes into account the possibility of regime shifts in the behavior of the Federal Reserve. In our empirical analysis, we show the merit of our approach three dimensions: interpretable bond dynamics, accurate short end yield curve pricing, and yield curve implications.     

A Risk Model with Multilayer Dividend Strategy

Abstract

In recent years various dividend payment strategies for the classical collective risk model have been studied in great detail. In this paper we consider both the dividend payment intensity and the premium intensity to be step functions depending on the current surplus level. Algorithmic schemes for the determination of explicit expressions for the Gerber-Shiu discounted penalty function and the expected discounted dividend payments are derived. This enables the analytical investigation of dividend payment strategies that, in addition to having a sufficiently large expected value of discounted dividend payments, also take the solvency of the portfolio into account. Since the number of layers is arbitrary, it also can be viewed as an approximation to a continuous surplus-dependent dividend payment strategy. A recursive approach with respect to the number of layers is developed that to a certain extent allows one to improve upon computational disadvantages of related calculation techniques that have been proposed for specific cases of this model in the literature. The tractability of the approach is illustrated numerically for a risk model with four layers and an exponential claim size distribution.     

Corporate Governance and Its Impact on R&D Investment in Emerging Markets

Corporate R&D activities are inherently risky but also difficult to monitor. Against this background, we examine the impact of ownership concentration and legal shareholder rights protection on corporate R&D investments in emerging markets. Based on a comprehensive sample of publicly listed firms from 24 countries, we find that R&D intensity is lower in firms with (strategic) block ownership, and this effect is more pronounced in countries with stronger shareholder rights protection. This suggests that, similar to the situation in developed economies, dispersed ownership, which allows shareholders to diversify their investment risks, is beneficial for corporate R&D and that this effect is intensified by more developed institutions.     

Bayesian Premium Rating with Latent Structure

We propose a fully Bayesian approach to non-life risk premium rating, based on hierarchical models with latent variables for both claim frequency and claim size. Inference is based on the joint posterior distribution and is performed by Markov Chain Monte Carlo. Rather than plug-in point estimates of all unknown parameters, we take into account all sources of uncertainty simultaneously when the model is used to predict claims and estimate risk premiums. Several models are fitted to both a simulated dataset and a small portfolio regarding theft from cars. We show that interaction among latent variables can improve predictions significantly. We also investigate when interaction is not necessary. We compare our results with those obtained under a standard generalized linear model and show through numerical simulation that geographically located and spatially interacting latent variables can successfully compensate for missing covariates. However, when applied to the real portfolio data, the proposed models are not better than standard models due to the lack of spatial structure in the data.     

Testing the bivariate mixture hypothesis using German Stock market data

According to the bivariate mixture hypothesis (BMH) as proposed by Tauchen and Pitts (1983) and Harris (1986, 1987) the daily price changes and the corresponding trading volume on speculative markets follow a joint mixture of distributions with the unobservable number of daily information events serving as the mixing variable. Using German stock market data of 15 major companies the distributional properties of the BMH is tested employing maximum-likelihood as well as generalised method of moments estimation techniques. In addition to providing a new approach for the pointwise estimation of the latent information arrival rate based on the maximum-likelihood method, we investigate the time-series properties of the BMH. the major results can be summarised as follows: (i) the distributional characteristics of the data (especially leptokurtosis and skewness in the distribution of price changes and volume respectively) cannot be explained satisfactorily by the BMH; univariate mixture models for price changes and trading volume separately reveal a possible specification error in the model; (ii) a univariate normal mixture model can account for the observed distributional characteristics of price changes; (iii) the estimated process of the latent information rate cannot fully explain the time-series characteristics of the data (especially the volatility clustering or ARCH-effects).     

Maximum likelihood estimation of non-affine volatility processes

In this paper we develop a new estimation method for extracting non-affine latent stochastic volatility and risk premia from measures of model-free realized and risk-neutral integrated volatility. We estimate non-affine models with nonlinear drift and constant elasticity of variance and we compare them to the popular square-root stochastic volatility model. Our empirical findings are: (1) the square-root model is misspecified; (2) the inclusion of constant elasticity of variance and nonlinear drift captures stylized facts of volatility dynamics and (3) the square-root stochastic volatility model is explosive under the risk-neutral probability measure.     

Estimating Latent Variables and Jump Diffusion Models Using High-Frequency Data

This article proposes a new approach to exploit the informationin high-frequency data for the statistical inference of continuous-timeaffine jump diffusion (AJD) models with latent variables. Forthis purpose, we construct unbiased estimators of the latentvariables and their power functions on the basis of the observedstate variables over extended horizons. With the estimates ofthe latent variables, we propose a generalized method of moments(GMM) procedure for the estimation of AJD models with the distinguishingfeature that moments of both observed and latent state variablescan be used without resorting to path simulation or discretizationof the continuous-time process. Using high frequency returnobservations of the S&P 500 index, we implement our estimationapproach to various continuous-time asset return models withstochastic volatility and random jumps.     

Pricing catastrophe options with stochastic claim arrival intensity in claim time

We model claim arrival and loss uncertainties jointly in a doubly-binomial framework to price an Asian-style catastrophe (CAT) option with a non-traded underlying loss index using the no-arbitrage martingale pricing methodology. We span these uncertainties by benchmarking to the shadow price of a one-claim bond and the premium of a reinsurance contract. We implement a stochastic time change from calendar time to claim time to more efficiently price the CAT option as a random sum – a binomial sum of claim time binomial Asian option prices. This choice of the operational time dimension allows us to incorporate different patterns of catastrophe arrivals by adjusting the claim arrival probability. We demonstrate this versatility by incorporating a mean-reverting Ornstein-Uhlenbeck intensity arrival process. Simulation results verify our model predictions and demonstrate how the claim arrival probability varies with the expected claim arrival intensity.     

Evaluating the Behavioural Performance of Alternative Logit Models: An Application to Corporate Takeovers Research

Abstract:  Econometric models involving a discrete outcome dependent variable abound in the finance and accounting literatures. However, much of the literature to date utilises a basic or standard logit model. Capitalising on recent developments in the discrete choice literature, we examine three advanced (or non-IID) logit models, namely: nested logit, mixed logit and latent class MNL. Using an illustration from corporate takeovers research, we compare the explanatory and predictive performance of each class of advanced model relative to the standard model. We find that in all cases the more advanced logit model structures, which correct for the highly restrictive IID and IIA conditions, provide significantly greater explanatory power than standard logit. Mixed logit and latent class MNL models exhibited the highest overall predictive accuracy on a holdout sample, while the standard logit model performed the worst. Moreover, the analysis of marginal effects of all models indicates that use of advanced models can lead to more insightful and behaviourally meaningful interpretations of the role and influence of explanatory variables and parameter estimates in model estimation. The results of this paper have implications for the use of more optimal logit structures in future research and practice.     

Valuation of Residential Mortgage-Backed Securities with Default Risk Using an Intensity-Based Approach

This paper develops a valuation model for fixed-rate mortgages, mortgage pools, and residential mortgage-backed securities (RMBS's) using an intensity-based approach. This model incorporates full prepayment, partial prepayment, and default in valuing a mortgage. Full prepayment is further classified into “refinancing” and “sale of a house” depending on the reason. The time of occurrence of each of these three types of prepayment and default is modeled as the first jump time of a Cox process. Under these conditions, the valuation formula for a mortgage as well as a partial differential equation (PDE) that the mortgage value satisfies is provided. As for implementation of the model, the short-term riskless interest rate and the house price are adopted as state variables. Each intensity process is specified in a manner that allows a jump in intensity depending on the state variables and the borrower's incentive for prepayment or default. Through such specifications, it is shown that our model has characteristics similar to some structural models in previous literature. As for the numerical method for valuation, we propose a simple backward induction technique on a tree instead of the commonly used Monte Carlo method. Additionally, the method for estimating the model is discussed, and the results of numerical simulations are reported.This paper represents the view of the author and does note necessarily the views of the Mitsubishi UFJ Securities Co., Ltd. or members of its staff.