Predicting Volatility in the Foreign Exchange Market

Measures of volatility implied in option prices are widely believed to be the best available volatility forecasts. In this article, we examine the information content and predictive power of implied standard deviations (ISDs) derived from Chicago Mercantile Exchange options on foreign currency futures. The article finds that statistical time-series models, even when given the advantage of “ex post” parameter estimates, are outperformed by ISDs. ISDs, however, also appear to be biased volatility forecasts. Using simulations to investigate the robustness of these results, the article finds that measurement errors and statistical problems can substantially distort inferences. Even accounting for these, however, ISDs appear to be too variable relative to future volatility.     

Conditional Properties of Hedge Funds: Evidence from Daily Returns*

Using daily returns on a set of hedge fund indices, we study (i) the properties of the indices' conditional density functions and (ii) the presence of asymmetries in conditional correlations between hedge fund indices and other investments and between hedge fund indices themselves. We use the SNP approach to obtain estimates of conditional densities of hedge fund returns and then proceed to examine their properties. In general, a nonparametric GARCH(1,1) model appears to provide the best fit for all strategies. We find that the conditional third and fourth moments are significantly affected by changes in the current volatility of returns on hedge fund indices. We examine changes in the conditional probability of tail events and report significant changes in the probability of extreme events when the conditioning information changes. These results have important implications for models of hedge fund risk that rely on probability of tail events. We formally test for the presence of asymmetries in conditional correlations to determine if there is contagion between hedge funds and other investments and between various hedge fund indices in extreme down markets versus extreme up markets. We generally do not find strong evidence in support of asymmetric correlations.     

Are there nonlinearities in short‐term interest rates?

The present paper investigates the characteristics of short‐term interest rates in several countries. We examine the importance of nonlinearities in the mean reversion and volatility of short‐term interest rates. We examine various models that allow the conditional mean (drift) and conditional variance (diffusion) to be functions of the current short rate. We find that different markets require different models. In particular, we find evidence of nonlinear mean reversion in some of the countries that we examine, linear mean reversion in others and no mean reversion in some countries. For all countries we examine, there is strong evidence of the need for the volatility of interest rate changes to be highly sensitive to the level of the short‐term interest rate. Out‐of‐sample forecasting performance of one‐factor short rate models is poor, stemming from the inability of the models to accommodate jumps and discontinuities in the time series data.     

Pacific Basin stock markets and international capital asset pricing

In this paper, we follow Harvey (1991) to investigate whether rates of return on Pacific Basin stock markets can be explained by conditional version of International Capital Asset Pricing Model (ICAPM), which allows for time-varying expected returns, variances, and covariances. The results show that most individual Pacific Basin markets can be described by the conditional ICAPM. However, the multiple markets' tests do not support the conditional ICAPM formulation, and the estimates of world reward to risk ratio are not the same across these markets. Furthermore, the Ghysels and Hall test (Ghysels & Hall, 1990a, 1990b) shows that the estimates of parameter are also unstable in the conditional ICAPM formulation. This implies that it is difficult to use world return to describe the relationship between expected return and risk for the Pacific Basin stock markets.     

REGIME-SWITCHING IN AUSTRALIAN SHORT-TERM INTEREST RATES

This paper applies a generalized regime-switching (GRS) model of the short-term interest rate to Australian data. The model allows the short rate to exhibit both mean reversion and conditional heteroscedasticity and nests the popular generalized autoregressive conditional heteroscedasticity (GARCH) and regime-switching specifications. It is shown that empirical estimates of many popular interest rate models provide curious results which imply that innovations to the short rate process are extremely persistent, and that the short rate is potentially non-stationary. The source of these curious results, which are also present in US and European interest rates, is identified in the context of the GRS model, which is shown, via specification and forecasting tests, to capture the features of Australian short-term interest rate data better than existing models. The stochastic process of short-term interest rates in Australia is compared with evidence from the US and Europe, highlighting a number of important differences.     

A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility

We show that, for three common SARV models, fitting a minimummean square linear filter is equivalent to fitting a GARCH model.This suggests that GARCH models may be useful for filtering,forecasting, and parameter estimation in stochastic volatilitysettings. To investigate, we use simulations to evaluate howthe three SARV models and their associated GARCH filters performunder controlled conditions and then we use daily currency andequity index returns to evaluate how the models perform in arisk management application. Although the GARCH models produceless precise forecasts than the SARV models in the simulations,it is not clear that the performance differences are large enoughto be economically meaningful. Consistent with this view, wefind that the GARCH and SARV models perform comparably in testsof conditional value-at-risk estimates using the actual data.     

The independence axiom and asset returns

This paper integrates models of atemporal risk preference that relax the independence axiom into a recursive intertemporal asset-pricing framework. The resulting models are amenable to empirical analysis using market data and standard Euler equation methods. We are thereby able to provide the first nonlaboratory-based evidence regarding the usefulness of several new theories of risk preference for addressing standard problems in dynamic economics. Using both stock and bond returns data, we find that a model incorporating risk preferences that exhibit first-order risk aversion accounts for significantly more of the mean and autocorrelation properties of the data than models that exhibit only second-order risk aversion. Unlike the latter class of models which require parameter estimates that are outside of the admissible parameter space, e.g., negative rates of time preference, the model with first-order risk aversion generates point estimates that are economically meaningful. We also examine the relationship between first-order risk aversion and models that employ exogenous stochastic switching processes for consumption growth.     

A Bayesian encompassing test using combined value-at-risk estimates

The Value at Risk (VaR) is a risk measure that is widely used by financial institutions in allocating risk. VaR forecast estimation involves the conditional evaluation of quantiles based on the currently available information. Recent advances in VaR evaluation incorporate conditional variance into the quantile estimation, yielding the Conditional Autoregressive VaR (CAViaR) models. However, the large number of alternative CAViaR models raises the issue of identifying the optimal quantile predictor. To resolve this uncertainty, we propose a Bayesian encompassing test that evaluates various CAViaR models predictions against a combined CAViaR model based on the encompassing principle. This test provides a basis for forecasting combined conditional VaR estimates when there are evidences against the encompassing principle. We illustrate this test using simulated and financial daily return data series. The results demonstrate that there are evidences for using combined conditional VaR estimates when forecasting quantile risk.     

Capital asset prices versus time series models as predictors of inflation: The expected real rate of interest and market efficiency

This paper examines the robustness of one month treasury bills as predictors of inflation. The evidence is inconsistent with the joint hypothesis that (1) the expected real rate of interest was constant for one-month bills and (2) that markets are efficient with regard to the time series of inflation. When the expected real rate of interest is set equal to the conditional expectation given the time series of real rates, the results are much more consistent with the efficient markets model. In more positive terms, the failure to confirm market efficiency appears to be the result of naive estimates of the expected real rate.     

Estimating GARCH models using support vector machines*

Abstract

Support vector machines (SVMs) are a new nonparametric tool for regression estimation. We will use this tool to estimate the parameters of a GARCH model for predicting the conditional volatility of stock market returns. GARCH models are usually estimated using maximum likelihood (ML) procedures, assuming that the data are normally distributed. In this paper, we will show that GARCH models can be estimated using SVMs and that such estimates have a higher predicting ability than those obtained via common ML methods.     

Time varying consumption covariance and dynamics of the equity premium: Evidence from the G7 countries

We examine implications of time-varying correlation and covariance between excess equity returns and consumption growth for the equity premium of the G7 countries. We find that the correlation and covariance are higher when there is a negative shock to labor income and a positive shock to returns. The combined effect is that the correlation and covariance are countercyclical and so is the equity premium. We test asset pricing models with time-varying consumption risk and find that the conditional price of risk is generally positive. These results survive several robustness checks. Our results highlight the importance of labor income for understanding dynamics of the equity premium.     

If You Want My Advice: Status Motives and Audit Consultations About Accounting Estimates

Effective consultation is critical for improving the audit of estimates. In an experiment where audit managers acted as consultants to other auditors, we examine conditions in which consultants either recommend estimates that differ substantially from advice‐seekers’ assessments (contrariness) or recommend narrower reasonable ranges of estimates (precision). Psychology theory argues that both of these attributes can improve estimates. We examine whether these attributes depend on consultants' status motives, that is, the desire to gain respect from or power over others. We find that active status motives lead consultants with higher specialized knowledge to provide recommendations that are less contrary, but more precise. However, consultants increase precision by tightening range bounds in a manner that is not counter to management's preference and thus unlikely to prompt the audit team to challenge the estimate. We also find that higher consultant decision authority constrains precision. Our findings suggest limits to consultation's potential effectiveness in improving estimates. For instance, our findings suggest that firms and standard setters direct consultants to focus scrutiny on the range bound that is most likely to constrain management opportunism.     

Estimating exponential affine models with correlated measurement errors: Applications to fixed income and commodities

Exponential affine models (EAMs) are factor models popular in financial asset pricing requiring a dynamic term structure, such as for interest rates and commodity futures. When implementing EAMs it is usual to first specify the model in state-space form (SSF) and then to estimate it using the Kalman filter. To specify the SSF, a structure of the measurement error must be provided which is not specified in the EAM itself. Different specifications of the measurement errors will result in different SSFs, leading to different parameter estimates. In this paper we investigate the influence of the measurement error specification on the parameter estimates. Using market data for both fixed income and commodities we provide evidence that measurement errors are cross-sectionally and serially correlated, which is not consistent with the independent identically distributed (iid) assumptions commonly adopted in the literature. Using simulated data we show that measurement error assumptions affect parameter estimates, especially in the presence of serial correlation. We provide a new specification, the augmented state-space form (ASSF), as a solution to these biases and show that the ASSF gives much better estimates than the basic SSF.     

Range-Based Estimation of Stochastic Volatility Models

We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that range-based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise. Hence range-based Gaussian quasi-maximum likelihood estimation produces highly efficient estimates of stochastic volatility models and extractions of latent volatility. We use our method to examine the dynamics of daily exchange rate volatility and find the evidence points strongly toward two-factor models with one highly persistent factor and one quickly mean-reverting factor.     

Stock returns,quantile autocorrelation,and volatility forecasting

We examine stock return autocorrelation at various quantiles of the returns' distribution and use it to forecast stock return volatility. Our empirical results show that the strength of the autoregression varies across the quantiles of the returns' distribution in terms of both magnitude and persistence. Specifically, the autoregression order and magnitude of the coefficients is lower in the left tail in comparison with the right tail. Additionally, we show that the quantile autoregressive (QAR) framework proposed in this study improves out-of-sample volatility forecasting performance compared to the generalised autoregressive conditional heteroscedasticity (GARCH)-type models and other quantile-based models. We also observe greater outperformance in QAR estimates during periods of financial turmoil. Moreover, the QAR method also explains the stylized ‘leverage effect’ associated with asset returns in the presence of volatility asymmetry.     

Cross‐sectional Tests of Conditional Asset Pricing Models: Evidence from the German Stock Market

We study the performance of conditional asset pricing models and multifactor models in explaining the German cross‐section of stock returns. We focus on several variables, which (according to previous research) are associated with market expectations on future market excess returns or business cycle conditions. Our results suggest that the empirical performance of the Capital Asset Pricing Model (CAPM) can be improved when allowing for time‐varying parameters of the stochastic discount factor. A conditional CAPM using the term spread explains the returns on our size and book‐to‐market sorted portfolios about as well as the Fama‐French three‐factor model and performs best in terms of the Hansen‐Jagannathan distance. Structural break tests do not necessarily indicate parameter instability of conditional model specifications. Another major finding of the paper is that the Fama‐French model – despite its generally good cross‐sectional performance – is subject to model instability. Unconditional models, however, do a better job than conditional ones at capturing time‐series predictability of the test portfolio returns.     

On the Efficiency of Conditional Heteroskedasticity Models

This paper discusses how conditional heteroskedasticity models can be estimated efficiently without imposing strong distributional assumptions such as normality. Using the generalized method of moments (GMM) principle, we show that for a class of models with a symmetric conditional distribution, the GMM estimates obtained from the joint estimating equations corresponding to the conditional mean and variance of the model are efficient when the instruments are chosen optimally. A simple ARCH(1) model is used to illustrate the feasibility of the proposed estimation procedure.     

Size,time-varying beta,and conditional heteroscedasticity in UK stock returns

The purpose of this study is to examine the relationship between firm size and time-varying betas of UK stocks. We extend the Schwert and Seguin (1990)(Journal of Finance 45, 1120–1155) methodology by explicitly modeling conditional heteroscedasticity in the market model residual returns. Our results show that the time-varying coefficient is not statistically significant for both small and large firm stock indexes. We also find that accounting for GARCH effects in the Schwert-Seguin market model yields beta estimates that are markedly differently from those when conditional heteroscedasticity is ignored. Event studies that ignore conditional heteroscedasticity may bias the abnormal returns of small and large firms, thereby leading to a different conclusion regarding the significance of an information event.     

Investors' Reactions to Management Earnings Guidance: The Joint Effect of Investment Position, News Valence, and Guidance Form

We report the results of an experiment that shows that investors' earnings- and investment-related judgments are jointly influenced by their investment position (long versus short), the news valence of guidance issued by management, and the amount of ambiguity in the guidance. Prior research indicates that guidance form (point versus range) has no effect on investors' earnings estimates made in reaction to management guidance. We extend this research by showing that guidance form matters, conditional on investment position and news valence. Similarly, prior research indicates that investors who hold long (short) positions in a stock are more optimistic (pessimistic) about the company's prospects. We extend this research by showing that the effect of investment position documented in prior studies is conditional on news valence and guidance form. We contribute to prior literature on the effects of investment position and guidance form by delineating boundary conditions for each of these effects.     

Modeling conditional return autocorrelation

Empirical estimates of conditional return autocorrelation are generated over the period 1973 to 2000 for S&P500 index data, as well as for a small selection of individual U.S. stocks. We find that conditional autocorrelation is highly variable, and these dynamics are consistent with changes in point autocorrelation estimates generated in various subperiods. The conditional autocorrelation estimates for some stocks exhibited a pattern of mean reversion, while for others, evidence of long-term trends and structural breaks was found. While we were unable to uncover what characteristics drive the nature of these autocorrelation patterns, our analysis ruled out industry, investor type or degree of internationalisation as explanations.