Predicting the bear stock market: Macroeconomic variables as leading indicators

This paper investigates whether macroeconomic variables can predict recessions in the stock market, i.e., bear markets. Series such as interest rate spreads, inflation rates, money stocks, aggregate output, unemployment rates, federal funds rates, federal government debt, and nominal exchange rates are evaluated. After using parametric and nonparametric approaches to identify recession periods in the stock market, we consider both in-sample and out-of-sample tests of the variables’ predictive ability. Empirical evidence from monthly data on the Standard & Poor’s S&P 500 price index suggests that among the macroeconomic variables we have evaluated, yield curve spreads and inflation rates are the most useful predictors of recessions in the US stock market, according to both in-sample and out-of-sample forecasting performance. Moreover, comparing the bear market prediction to the stock return predictability has shown that it is easier to predict bear markets using macroeconomic variables.     

Modelling and forecasting short-term interest rate volatility: A semiparametric approach

This paper employs a semiparametric procedure to estimate the diffusion process of short-term interest rates. The Monte Carlo study shows that the semiparametric approach produces more accurate volatility estimates than models that accommodate asymmetry, level effect and serial dependence in the conditional variance. Moreover, the semiparametric approach yields robust volatility estimates even if the short rate drift function and the underlying innovation distribution are misspecified. Empirical investigation with the U.S. three-month Treasury bill rates suggests that the semiparametric procedure produces superior in-sample and out-of-sample forecast of short rate changes volatility compared with the widely used single-factor diffusion models. This forecast improvement has implications for pricing interest rate derivatives.     

Term structure modelling with observable state variables

This paper proposes and implements a parsimonious three-factor model of the term structure whose dynamics is driven uniquely by observable state variables. This approach allows comparing alternative views on the way state variables – macroeconomic variables, in particular – influence the yield curve dynamics, avoids curse of dimensionality problems, and provides more reliable inference by using both the cross-sectional and the time series dimension of the data. I simulate the small-sample properties of the procedure and conduct in- and out-of-sample studies using a comprehensive set of US data. I show that even a parsimonious model where the level, slope and curvature factors of the term structure are driven by, respectively, inflation, monetary policy and economic activity consistently outperforms the (latent-variable) benchmark model in an out-of-sample study.     

Empirical Analysis of Business Cycle Synchronization and Shock Similarity Between Romania and the Eurozone

This study investigates benefits from a trading strategy based on the spillovers from international stock markets to the Polish emerging stock market. The analysis is conducted within the framework of factor and predictive generalized autoregressive conditional heteroskedasticity (GARCH) models of the Warsaw Stock Exchange main index, WIG. We apply an approach in which the mean equation of the GARCH model includes a deterministic part incorporating cross-markets linkages. Both in-sample and out-of-sample forecasts from the estimated models are calculated. The trading strategy is based on signals from the out-of-sample predictions. The models' performance and benefits from adopting such a strategy are evaluated using direction quality measures. Our results suggest that predictive models using cross-market linkages can produce superior out-of-sample forecasts compared to benchmarks.     

Nonparametric correlation models for portfolio allocation

This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major currencies. Results show the nonparametric model generally dominates the others when evaluating in-sample. However, the semiparametric model is best for out-of-sample analysis.     

Portfolio performance of linear SDF models: an out-of-sample assessment

We evaluate linear stochastic discount factor models using an ex-post portfolio metric: the realized out-of-sample Sharpe ratio of mean–variance portfolios backed by alternative linear factor models. Using a sample of monthly US portfolio returns spanning the period 1968–2016, we find evidence that multifactor linear models have better empirical properties than the CAPM, not only when the cross-section of expected returns is evaluated in-sample, but also when they are used to inform one-month ahead portfolio selection. When we compare portfolios associated to multifactor models with mean–variance decisions implied by the single-factor CAPM, we document statistically significant differences in Sharpe ratios of up to 10 percent. Linear multifactor models that provide the best in-sample fit also yield the highest realized Sharpe ratios.     

The Gilt-Equity Yield Ratio and the Predictability of UK and US Equity Returns

A number of financial variables have been shown to be effective in explaining the time-series of aggregate equity returns in both the UK and the US. These include, inter alia , the equity dividend yield, the spread between the yields on long and short government bonds, and the lagged equity return. Recently, however, the ratio between the long government bond yield and the equity dividend yield – the gilt-equity yield ratio – has emerged as a variable that has considerable explanatory power for UK equity returns. This paper compares the predictive ability of the gilt-equity yield ratio with these other variables for UK and US equity returns, providing evidence on both in-sample and out-of-sample performance. For UK monthly returns, it is shown that while the dividend yield has substantial in-sample explanatory power, this is not matched by out-of sample forecast accuracy. The gilt-equity yield ratio, in contrast, performs well both in-sample and out-of-sample. Although the predictability of US monthly equity returns is much lower than for the UK, a similar result emerges, with the gilt-equity yield ratio dominating the other variables in terms of both in-sample explanatory power and out-of-sample forecast performance. The gilt-equity yield ratio is also shown to have substantial predictive ability for long horizon returns.     

Resolving the deposit dilemma: A new DEA bank efficiency model

One of the weaknesses of current bank efficiency models is a disagreement as to the role of deposits in the bank production process. Some models view deposits as an input, while others view them as an output. Such disparity of approaches results in inconsistent efficiency estimates. In this study we propose an alternative Data Envelopment Analysis (DEA) bank efficiency model that treats deposits as an intermediate product, thus emphasizing the dual role of deposits in the bank production process. Consequently, the effect of the amount of deposits on bank efficiency depends on the efficiency at both stages of the bank production process. The main advantage of our model is that it does not require a researcher to make a judgment call as to whether having more (production approach) or less (intermediation approach) deposits is “better” for bank efficiency. Our unified framework has the potential to produce more consistent efficiency estimates.     

The Gilt-Equity Yield Ratio and the Predictability of UK and US Equity Returns

A number of financial variables have been shown to be effective in explaining the time-series of aggregate equity returns in both the UK and the US. These include, inter alia , the equity dividend yield, the spread between the yields on long and short government bonds, and the lagged equity return. Recently, however, the ratio between the long government bond yield and the equity dividend yield – the gilt-equity yield ratio – has emerged as a variable that has considerable explanatory power for UK equity returns. This paper compares the predictive ability of the gilt-equity yield ratio with these other variables for UK and US equity returns, providing evidence on both in-sample and out-of-sample performance. For UK monthly returns, it is shown that while the dividend yield has substantial in-sample explanatory power, this is not matched by out-of sample forecast accuracy. The gilt-equity yield ratio, in contrast, performs well both in-sample and out-of-sample. Although the predictability of US monthly equity returns is much lower than for the UK, a similar result emerges, with the gilt-equity yield ratio dominating the other variables in terms of both in-sample explanatory power and out-of-sample forecast performance. The gilt-equity yield ratio is also shown to have substantial predictive ability for long horizon returns.     

The real-time predictability of the size and value premium in Japan

This study examines whether the short-term variation in the Japanese size and value premium is sufficiently predictable to be exploited by a timing strategy. In the spirit of Pesaran and Timmermann [J. Finance 50 (1995) 1201], we employ a dynamic modeling approach in which we explicitly allow for permutations among the determinants in order to mitigate typical data-snooping biases. Using a base set of candidate regressors, we perform an in-sample estimation of all economically sensible models. Subsequently, a “best” model is determined according to a selection criterion. However, whereas most studies use in-sample model selection criteria, we introduce an out-of-sample training period to select our models. We then implement our strategy in a second-stage out-of-sample period: the trading period. All stages re-occur on a monthly basis via a rolling window framework. The results confirm sufficient predictability under lower transaction cost levels. Under high transaction costs scenarios it is more difficult to obtain incremental benefits.     

Surviving Chapter 11 Bankruptcies: Duration and Payoff?

Three of the authors previously developed a model to predict the duration of Chapter 11 bankruptcy and the payoff to shareholders ( Partington et al ., 2001 ). This work augments that study using a much larger sample to re-estimate the model and assess its stability. It also provides an opportunity for out-of-sample testing of predictive accuracy. The resulting models are based on Cox's proportional hazards model and the current article points to the need to test two important assumptions underlying the model. First, that the hazards are proportional and, second, that censoring is independent of the event studied. Using the extended data set, all the previously significant accounting variables drop out of the model and only two covariates of the original model remain significant. These are the market wide credit spread and the market capitalization of the firm, both measured immediately prior to the firm's entry to Chapter 11. Receiver operating characteristic curves are then used to assess the predictive accuracy of the original and extended models. The results show that Lachenbruch tests can provide a misleading indication of predictive ability out of sample. Using the Lachenbruch method of in-sample testing, both models show predictive power, but in a true out-of-sample test they fail dismally. The lessons of this work are relevant to better predicting the gains and losses likely to accrue to shareholders of companies in Chapter 11 bankruptcy and in similar administrative arrangements in other jurisdictions.     

Capital market imperfections and the theory of optimum currency areas

This paper studies how within- and cross-country capital market imperfections affect the welfare effects of forming a currency union. The analysis considers a bank-only world where intermediaries compete in Cournot fashion and monitoring and state verification are costly. The first part determines the credit market equilibrium and the optimal number of banks, prior to joining the union. The second part discusses the benefits from joining a currency union. A competition effect is identified and related to the added monitoring costs that banks may incur when operating outside their home country, through an argument akin to the Brander-Krugman “reciprocal dumping” model of bilateral trade. However, in our framework, whether joining a union raises welfare of the home country is ambiguous; it depends on the relative strength of “investment creation” and “intermediation diversion” effects.     

The predictability of aggregate returns on commodity futures

This paper provides evidence that aggregate returns on commodity futures (without the returns on collateral) are predictable, both in-sample and out-of-sample, by various lagged variables from the stock market, bond market, macroeconomics, and the commodity market. Out of the 32 candidate predictors we consider, we find that investor sentiment is the best in-sample predictor of short-horizon returns, whereas the level and slope of the yield curve have much in-sample predictive power for long-horizon returns. We find that it is possible to forecast aggregate returns on commodity futures out-of-sample through several combination forecasts (the out-of-sample return forecasting R² is up to 1.65% at the monthly frequency).     

恒生指数和沪深300股指期货套期保值效果对比研究

本文利用OLS、ECM、ECM-GARCH模型对沪深300股指期货和恒生指数期货的最优套期保值率进行了估算,并在风险最小化框架下对它们的套期保值效果进行了对比研究。结果发现:无论是哪种股指期货,不考虑期现货间存在的协整关系会使估算的最优套期保值率偏高,影响套期保值效果;其次是虽然在样本内外,沪深300股指期货的套期保值效果比恒生指数期货的好,但是沪深300股指期货套期保值效果的稳定性比恒生指数差。此时,ECM-GARCH和OLS模型分别为样本内外投资者利用沪深300指数期货进行套期保值时的最佳选择;对于恒生指数股指期货,最优模型是ECM。     

Forecasting variance using stochastic volatility and GARCH

This paper estimates the conditional variance of daily Swedish OMX-index returns with stochastic volatility (SV) models and GARCH models and evaluates the in-sample performance as well as the out-of-sample forecasting ability of the models. Asymmetric as well as weekend/holiday effects are allowed for in the variance, and the assumption that errors are Gaussian is released. Evidence is found of a leverage effect and of higher variance during weekends. In both in-sample and out-of-sample comparisons SV models outperform GARCH models. However, while asymmetry, weekend/holiday effects and non-Gaussian errors are important for the in-sample fit, it is found that these factors do not contribute to enhancing the forecasting ability of the SV models.     

Efficient exchange rate forecasts: Lagged models better than the random walk

The evidence of Meese amd Rogoff (1983) on the out-of-sample forecasting performance of structural exchange rate models in comparison to the random walk model portrays a disappointing picture of structural models. This paper re-considers the issue for the German mark for an updated period to include a larger set of structural models and lagged adjustment. Besides out-of-sample evidence, in-sample evidence is also examined. We conclude that while some stuctural models dominate the random walk, a lagged adjustment consideration can contribute towards better performance.     

A comparison of alternative bankruptcy prediction models

Early models of bankruptcy prediction employed financial ratios drawn from pre-bankruptcy financial statements and performed well both in-sample and out-of-sample. Since then there has been an ongoing effort in the literature to develop models with even greater predictive performance. A significant innovation in the literature was the introduction into bankruptcy prediction models of capital market data such as excess stock returns and stock return volatility, along with the application of the Black–Scholes–Merton option-pricing model. In this note, we test five key bankruptcy models from the literature using an up-to-date data set and find that they each contain unique information regarding the probability of bankruptcy but that their performance varies over time. We build a new model comprising key variables from each of the five models and add a new variable that proxies for the degree of diversification within the firm. The degree of diversification is shown to be negatively associated with the risk of bankruptcy. This more general model outperforms the existing models in a variety of in-sample and out-of-sample tests.     

Selecting credit rating models: a cross-validation-based comparison of discriminatory power

In commercial banking, various statistical models for corporate credit rating have been theoretically promoted and applied to bank-specific credit portfolios. In this paper, we empirically compare and test the performance of a wide range of parametric and nonparametric credit rating model approaches in a statistically coherent way, based on a ‘real-world’ data set. We repetitively (k times) split a large sample of industrial firms’ default data into disjoint training and validation subsamples. For all model types, we estimate k out-of-sample discriminatory power measures, allowing us to compare the models coherently. We observe that more complex and nonparametric approaches, such as random forest, neural networks, and generalized additive models, perform best in-sample. However, comparing k out-of-sample cross-validation results, these models overfit and lose some of their predictive power. Rather than improving discriminatory power, we perceive their major contribution to be their usefulness as diagnostic tools for the selection of rating factors and the development of simpler, parametric models.

A logistic regression point of view toward loss given default distribution estimation

We propose a new procedure to estimate the loss given default (LGD) distribution. Owing to the complicated shape of the LGD distribution, using a smooth density function as a driver to estimate it may result in a decline in model fit. To overcome this problem, we first apply the logistic regression to estimate the LGD cumulative distribution function. Then, we convert the result into the LGD distribution estimate. To implement the newly proposed estimation procedure, we collect a sample of 5269 defaulted debts from Moody’s Default and Recovery Database. A performance study is performed using 2000 pairs of in-sample and out-of-sample data-sets with different sizes that are randomly selected from the entire sample. Our results show that the newly proposed procedure has better and more robust performance than its alternatives, in the sense of yielding more accurate in-sample and out-of-sample LGD distribution estimates. Thus, it is useful for studying the LGD distribution.     

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.