Risk,uncertainty, and asset prices

We identify the relative importance of changes in the conditional variance of fundamentals (which we call “uncertainty”) and changes in risk aversion in the determination of the term structure, equity prices, and risk premiums. Theoretically, we introduce persistent time-varying uncertainty about the fundamentals in an external habit model. The model matches the dynamics of dividend and consumption growth, including their volatility dynamics and many salient asset market phenomena. While the variation in price–dividend ratios and the equity risk premium is primarily driven by risk aversion, uncertainty plays a large role in the term structure and is the driver of countercyclical volatility of asset returns.     

Profit or politics? Understanding renationalizations in Russia

This study examines firm-level determinants of the government incentives to acquire controlling stakes in private companies. Using a novel hand-collected dataset of 153 largest listed and unlisted Russian companies, I investigate the methods and the rationales of a 2004–2008 wave of selected nationalizations in a post-privatization market. I find robust evidence that formerly privatized and domestically-owned companies in strategically important sectors face the highest risks of corporate control transfers from private to state hands. I also find that the corporate tax evasion is another significant determinant of a firm nationalization. Contrary to commonly held beliefs, there is little evidence that renationalizations in Russia are driven by firm profitability factors: the government neither systematically “cherry-picks” best performers nor addresses market failures by rescuing national champions in financial distress. These results contribute to t'he politics and finance literature by providing new firm-level evidence on the importance of strategic nationalism in the government's decision to intervene into the corporate control structures.     

Volatility transmission in emerging European foreign exchange markets

This paper studies the dynamics of volatility transmission between Central European (CE) currencies and the EUR/USD foreign exchange using model-free estimates of daily exchange rate volatility based on intraday data. We formulate a flexible yet parsimonious parametric model in which the daily realized volatility of a given exchange rate depends both on its own lags as well as on the lagged realized volatilities of the other exchange rates. We find evidence of statistically significant intra-regional volatility spillovers among the CE foreign exchange markets. With the exception of the Czech and, prior to the recent turbulent economic events, Polish currencies, we find no significant spillovers running from the EUR/USD to the CE foreign exchange markets. To measure the overall magnitude and evolution of volatility transmission over time, we construct a dynamic version of the Diebold–Yilmaz volatility spillover index and show that volatility spillovers tend to increase in periods characterized by market uncertainty.     

In- and out-of-sample specification analysis of spot rate models: Further evidence for the period 1982–2008

We review and construct consistent in-sample specification and out-of-sample model selection tests on conditional distributions and predictive densities associated with continuous multifactor (possibly with jumps) and (non)linear discrete models of the short term interest rate. The results of our empirical analysis are used to carry out a “horse-race” comparing discrete and continuous models across multiple sample periods, forecast horizons, and evaluation intervals. Our evaluation involves comparing models during two distinct historical periods, as well as across our entire weekly sample of Eurodollar deposit rates from 1982 to 2008. Interestingly, when our entire sample of data is used to estimate competing models, the “best” performer in terms of distributional “fit” as well as predictive density accuracy, both in-sample and out-of-sample, is the three factor Chen (Chen, 1996) model examined by Andersen, Benzoni and Lund (2004). Just as interestingly, a logistic type discrete smooth transition autoregression (STAR) model is preferred to the “best” continuous model (i.e. the one factor Cox, Ingersoll, and Ross (CIR: 1985) model) when comparing predictive accuracy for the “Stable 1990s” period that we examine. Moreover, an analogous result holds for the “Post 1990s” period that we examine, where the STAR model is preferred to a two factor stochastic mean model. Thus, when the STAR model is parameterized using only data corresponding to a particular sub-sample, it outperforms the “best” continuous alternative during that period. However, when models are estimated using the entire dataset, the continuous CHEN model is preferred, regardless of the variety of model specification (selection) test that is carried out. Given that it is very difficult to ascertain the particular future regime that will ensue when constructing ex ante predictions, thus, the CHEN model is our overall “winning” model, regardless of sample period.     

Incorporating the dynamics of leverage into default prediction

A firm’s current leverage ratio is one of the core characteristics of credit quality used in statistical default prediction models. Based on the capital structure literature, which shows that leverage is mean-reverting to a target leverage, we forecast future leverage ratios and include them in the set of default risk drivers. An out-of-sample analysis of default predictions from a hazard model reveals that the discriminative power increases substantially when leverage forecasts are included. We further document that credit ratings contain information beyond the one contained in standard variables but that this information is unrelated to forecasts of leverage ratios.     

Pricing the term structure with linear regressions

We show how to price the time series and cross section of the term structure of interest rates using a three-step linear regression approach. Our method allows computationally fast estimation of term structure models with a large number of pricing factors. We present specification tests favoring a model using five principal components of yields as factors. We demonstrate that this model outperforms the Cochrane and Piazzesi (2008) four-factor specification in out-of-sample exercises but generates similar in-sample term premium dynamics. Our regression approach can also incorporate unspanned factors and allows estimation of term structure models without observing a zero-coupon yield curve.     

The empirical risk–return relation: A factor analysis approach

Existing empirical literature on the risk–return relation uses relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large data sets, to summarize a large amount of economic information by few estimated factors, and find that three new factors—termed “volatility,” “risk premium,” and “real” factors—contain important information about one-quarter-ahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 16–20% of the one-quarter-ahead variation in excess stock market returns, and exhibit stable and statistically significant out-of-sample forecasting power. We also find a positive conditional risk–return correlation.     

A multi-country test of the Fisher model for stock returns

This article provides a test of the Fisher model, linking expected stock returns and inflation, based on international data. Since the Fisher model is ‘universal’ and calls for a slope of 1 in any country, we improve the testing power by conducting a joint test over eight countries. The pooling of data for several countries seems to reduce the small-sample bias. We test the Fisher model, using an instrumental variable approach, for holding-period horizons ranging from 1–12 months. The Fisher model is not rejected at any horizon: however, the magnitude of the slope coefficient lends stronger support at long horizons. This study using multi-country panel data provides evidence corroborating the finding of Boudoukh and Richardson (1993) that the Fisher model holds at long horizons (5 years), using 180 years of US data.     

Equity market information,bank holding company risk,and market discipline

For market discipline to be effective, market factors such as changes in firm equity and debt values and returns, must influence firm decision making. In banking, this can occur directly via bank management or indirectly though supervisory examinations and oversight influencing bank management. In this study, we investigate whether equity market variables can provide timely information and add value to accounting models that predict changes in bank holding company (BOPEC) risk ratings over the 1988–2000 period. Using a variety of equity market indicators, the findings suggest that one-quarter lagged market data adds forecast value to lagged financial statement data and prior supervisory information in the logistic regressions. Furthermore, using extensive out-of-sample testing for the years 2001–2003, we find: (1) that multiple models estimated over different phases of the business and banking cycles are superior to a single model for forecasting BOPEC rating changes; (2) that equity data adds economically significant power in forecasting BOPEC rating upgrades and performs well for identifying no changes; (3) that for downgrades, the accounting model forecasts the best; (4) that modeling the three possible risk ratings categories simultaneously (downgrade, no change and upgrade) minimizes both Type I and Type II classification errors; and (5) that using multiple models to forecast risk ratings enhances the overall percentage of correct classifications.     

A cyclical model of exchange rate volatility

In this paper, we investigate the long run dynamics of the intraday range of the GBP/USD, JPY/USD and CHF/USD exchange rates. We use a non-parametric filter to extract the low frequency component of the intraday range, and model the cyclical deviation of the range from the long run trend as a stationary autoregressive process. We use the cyclical volatility model to generate out-of-sample forecasts of exchange rate volatility for horizons of up to 1 year under the assumption that the long run trend is fully persistent. As a benchmark, we compare the forecasts of the cyclical volatility model with those of the range-based EGARCH and FIEGARCH models of Brandt and Jones (2006). Not only does the cyclical volatility model provide a very substantial computational advantage over the EGARCH and FIEGARCH models, but it also offers an improvement in out-of-sample forecast performance.     

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.     

Market risk model selection and medium-term risk with limited data: Application to ocean tanker freight markets

The estimation of medium-term market risk dictated by limited data availability, is a challenging issue of concern amongst academics and practitioners. This paper addresses the issue by exploiting the concepts of volatility and quantile scaling in order to determine the best method for extrapolating medium-term risk forecasts from high frequency data. Additionally, market risk model selection is investigated for a new dataset on ocean tanker freight rates, which refer to the income of the capital good — tanker vessels. Certain idiosyncrasies inherent in the very competitive shipping freight rate markets, such as excessive volatility, cyclicality of returns and the medium-term investment horizons – found in few other markets – make these issues challenging. Findings indicate that medium-term risk exposures can be estimated accurately by using an empirical scaling law which outperforms the conventional scaling laws of the square and tail index root of time. Regarding the market risk model selection for short-term investment horizons, findings contradict most studies on conventional financial assets: interestingly, freight rate market risk quantification favors simpler specifications, such as the GARCH and the historical simulation models.     

Liquidity risk of corporate bond returns: conditional approach

We study the exposure of the US corporate bond returns to liquidity shocks of stocks and Treasury bonds over the period 1973–2007 in a regime-switching model. In one regime, liquidity shocks have mostly insignificant effects on bond prices, whereas in another regime, a rise in illiquidity produces significant but conflicting effects: Prices of investment-grade bonds rise while prices of speculative-grade (junk) bonds fall substantially (relative to the market). Relating the probability of these regimes to macroeconomic conditions we find that the second regime can be predicted by economic conditions that are characterized as “stress.” These effects, which are robust to controlling for other systematic risks (term and default), suggest the existence of time-varying liquidity risk of corporate bond returns conditional on episodes of flight to liquidity. Our model can predict the out-of-sample bond returns for the stress years 2008–2009. We find a similar pattern for stocks classified by high or low book-to-market ratio, where again, liquidity shocks play a special role in periods characterized by adverse economic conditions.     

Multi-period corporate default prediction with stochastic covariates

We provide maximum likelihood estimators of term structures of conditional probabilities of corporate default, incorporating the dynamics of firm-specific and macroeconomic covariates. For US Industrial firms, based on over 390,000 firm-months of data spanning 1980 to 2004, the term structure of conditional future default probabilities depends on a firm's distance to default (a volatility-adjusted measure of leverage), on the firm's trailing stock return, on trailing S&P 500 returns, and on US interest rates. The out-of-sample predictive performance of the model is an improvement over that of other available models.     

Asymmetric effect of basis on dynamic futures hedging: Empirical evidence from commodity markets

The dynamic minimum variance hedge ratios (MVHRs) have been commonly estimated using the Bivariate GARCH model that overlooks the basis effect on the time-varying variance–covariance of spot and futures returns. This paper proposes an alternative specification of the BGARCH model in which the effect is incorporated for estimating MVHRs. Empirical investigation in commodity markets suggests that the basis effect is asymmetric, i.e., the positive basis has greater impact than the negative basis on the variance and covariance structure. Both in-sample and out-of-sample comparisons of the MVHR performance reveal that the model with the asymmetric effect provides greater risk reduction than the conventional models, illustrating importance of the asymmetric effect when modeling the joint dynamics of spot and futures returns and hence estimating hedging strategies.     

When can you immunize a bond portfolio?

This paper presents a condition equivalent to the existence of a Riskless Shadow Asset that guarantees a minimum return when the asset prices are convex functions of interest rates or other state variables. We apply this lemma to immunize default-free and option-free coupon bonds and reach three main conclusions. First, we give a solution to an old puzzle: why do simple duration matching portfolios work well in empirical studies of immunization even though they are derived in a model inconsistent with equilibrium and shifts on the term structure of interest rates are not parallel, as assumed? Second, we establish a clear distinction between the concepts of immunized and maxmin portfolios. Third, we develop a framework that includes the main results of this literature as special cases. Next, we present a new strategy of immunization that consists in matching duration and minimizing a new linear dispersion measure of immunization risk.     

Stock repurchases: How firms choose between a self tender offer and an open-market program

In practice, open-market stock repurchase programs outnumber self tender offers by approximately 10–1. This evidence is puzzling given that tender offers are more efficient in disbursing free cash and in signaling undervaluation – the two main motivations suggested in the literature for repurchasing shares. We provide a theoretical model to explore this puzzle. In the model, tender offers disburse free cash quickly but induce information asymmetry and hence require a price premium. Open-market programs disburse free cash slowly, and hence do not require a price premium, but because they are slow, result in partial free cash waste. The model predicts that the likelihood that a tender offer will be chosen over an open-market program increases with the agency costs of free cash and decreases with uncertainty (risk), information asymmetry, ownership concentration, and liquidity. These predictions are generally consistent with the empirical evidence.     

Another look at the role of the industrial structure of markets for international diversification strategies

This paper re-examines the extent to which gains from international diversification are due to differences in industrial structure across countries. Recent papers by Roll (1992), Journal of Finance 47, 3–42 and Heston and Rouwenhorst (1994), Journal of Financial Economics 36, 3–27 investigate this issue and find conflicting evidence. Using a new database, the Dow Jones World Stock Index, with coverage in 25 countries and over 66 industry classifications, we decompose comprehensively both country and industrial sources of variation. We confirm that little of the variation in country index returns can be explained by their industrial composition. We also uncover differences in the proportion of variation in industry index returns that is captured by country and industry factors and discuss the implications for global diversification strategies.     

Alternative bankruptcy prediction models using option-pricing theory

We examine the empirical properties of the theoretical Black–Scholes–Merton (BSM) bankruptcy model. We evaluate the predictive ability of various existing modifications of the BSM model and extend prior studies by estimating volatility directly from market-observable returns on firm value. We show that parsimonious models using our direct market-observable volatility estimate perform better than alternative, more sophisticated, models. Our findings suggest the adoption of simpler modelling approaches relying on market data when implementing the BSM model.     

The role of trading intensity estimating the implicit bid–ask spread and determining transitory effects

In this paper, we investigate the information content of trading intensity applying the Madhavan, Richardson and Roomans (1997) structural model to express trading intensity as trading momentum in duration and volume. Using both transactions and intraday data from the Helsinki Stock Exchange Limit Order Bookmarket, we find that momentum in duration and volume enhances the information effect. We reach this conclusion based on the parametric effect determined by the sign and the magnitude of the coefficients associated with the trading intensity variables, the trading effect determined by the ratio of transitory effects to permanent effects, and the economic effect determined by the size of the implicit bid–ask spread. While we find that the implicit bid–ask spread and transitory effects are decreasing toward the end of the trading day in consistency with information models in the literature, there is a surge of trades at the market close, most probably due to information uncertainty at market opening in New York.