How to Identify and Forecast Bull and Bear Markets?

The state of the equity market, often referred to as a bull or a bear market, is of key importance for financial decisions and economic analyses. Its latent nature has led to several methods to identify past and current states of the market and forecast future states. These methods encompass semi-parametric rule-based methods and parametric regime-switching models. We compare these methods by new statistical and economic measures that take into account the latent nature of the market state. The statistical measure is based directly on the predictions, while the economic mea- sure is based on the utility that results when a risk-averse agent uses the predictions in an investment decision. Our application of this framework to the S&P500 shows that rule-based methods are preferable for (in-sample) identification of the market state, but regime-switching models for (out-of-sample) forecasting. In-sample only the direction of the market matters, but for forecasting both means and volatilities of returns are important. Both the statistical and the economic measures indicate that these differences are significant

On Crises, Crashes and Comovements

Crises and crashes in financial markets are investors’ worst fear. The combination of large losses, a persistent increase of price fluctuations, and a strengthening of comovements in prices causes investors great harm. While the severe consequences of crises and crashes are intuitively clear, many essential questions regarding the magnitude of the effects on specific fields in finance and the precise impact of the different factors have yet to be resolved. This dissertation provides answers to these questions from an investor’s perspective. Its main conclusion reads that the tendency of crises and crashes to spread to other assets and markets as well as over time is of crucial importance for determining their impact. Traditional models for comovements underestimate the risk of joint downward movements. Persistence exacerbates the effects of a crisis and increases the costs of ignoring its possibility beforehand. Moreover, this thesis concludes that investors can expect a compensation for the grave consequences of a crash that they are unable to evade. The size of this compensation indicates that crash risk may be equally important as the traditional risk in the normal fluctuations of asset prices. Furthermore, predictions on the likelihood of a crash can be improved by studying past returns. Besides these empirical contributions, this dissertation shows how various econometric techniques, including copulas and regime-switching models, can be used innovatively for the examination of crises, crashes and comovements

The effects of systemic crises when investors can be crisis ignorant

Systemic crises can largely affect asset allocations due to the rapid deterioration of the risk-return trade-off. We investigate the effects of systemic crises, interpreted as global simultaneous shocks to financial markets, by introducing an investor adopting a crisis ignorant or crisis conscious strategy. Including the possibility of a systemic crisis is a substantial improvement. Investments in risky assets fall, while allocations to countries less sensitive to a crisis grow relatively. An increasing probability of a crisis exacerbates these effects. The certainty equivalent costs of ignoring systemic crises are large, ranging from 0.65% per year unconditionally, to over 5% per month conditionally on a high probability for the occurrence of a crisis

Portfolio Implications of Systemic Crises

Systemic crises can have grave consequences for investors in international equity markets, because it causes the risk-return trade-off to deteriorate severely for a longer period. In this paper we propose a novel approach to include the possibility of systemic crises in asset allocation decisions. By combining regime switching models with Merton (1969)-style portfolio construction, our approach captures persistence of crises much better than existing models. Our analysis shows that incorporating systemic crises has a large impact on asset allocation decisions, while the costs of ignoring such crises are substantial. For an expected utility maximizing US investor, who can invest globally these costs range from 1.13% per year of his initial wealth when he has no prior information on the likelihood of a crisis, to over 3% per month if a crisis occurs with almost certainty. If a crisis is taken into account, the investor allocates less to risky assets, and particularly less to emerging markets, being most prone to a crisis. An investor facing short selling constraints withdraws completely from equity markets if the likelihood of a crisis increases

Cyclicality in Losses on Bank Loans

Cyclicality in the losses of bank loans is important for bank risk management. Because loans have a different risk profile than bonds, evidence of cyclicality in bond losses need not apply to loans. Based on unique data we show that the default rate and loss given default of bank loans share a cyclical component, related to the business cycle. We infer this cycle by a new model that distinguishes loans with large and small losses, and links them to the default rate and macro variables. The loss distributions within the groups stay constant, but the fraction of loans with large losses increases during downturns. Our model implies substantial time-variation in banks' capital reserves, and helps predicting the losses

Riding Bubbles

Bubbles can persist because investors are better off riding bubbles. We define bubbles in a natural way as significant, prolonged deviations from fundamental values measured by the well-known asset pricing models. Our real-time bubble detection system shows that –using US industry returns– periods of both higher volatility and higher abnormal returns follow noisy positive bubble signals. However, for the typical investor the risk-return trade-off improves. Riding bubbles generates annual abnormal returns of three to nine percent. These conclusions are robust to different assumptions and our system allows for alternative multifactor models as proxies for fundamental value

Selecting Copulas for Risk Management

Copulas offer financial risk managers a powerful tool to model the dependence between the different elements of a portfolio and are preferable to the traditional, correlation-based approach. In this paper we show the importance of selecting an accurate copula for risk management. We extend standard goodness-of-fit tests to copulas. Contrary to existing, indirect tests, these tests can be applied to any copula of any dimension and are based on a direct comparison of a given copula with observed data. For a portfolio consisting of stocks, bonds and real estate, these tests provide clear evidence in favor of the \\studt copula, and reject both the correlation-based Gaussian copula and the extreme value-based Gumbel copula. In comparison with the \\studt copula, we find that the Gaussian copula underestimates the probability of joint extreme downward movements, while the Gumbel copula overestimates this risk. Similarly we establish that the Gaussian copula is too optimistic on diversification benefits, while the Gumbel copula is too pessimistic. Moreover, these differences are significant

Stress Testing with Student's t Dependence

In this study we propose the use of the Student's t dependence function to model dependence between asset returns when conducting stress tests. To properly include stress testing in a risk management system, it is important to have accurate information about the (joint) probabilities of extreme outcomes. Consequently, a model for the behavior of risk factors is necessary, specifying the marginal distributions and their dependence. Traditionally, dependence is described by a correlation matrix, implying the use of the dependence function inherent in the multivariate normal (Gaussian) distribution. Recent studies have cast serious doubt on the appropriateness of the Gaussian dependence function to model dependence between extreme negative returns. The student's t dependence function provides an attractive alternative. In this paper, we introduce four tests to analyze the empirical fit of both dependence functions. The empirical results indicate that probabilities assigned to stress tests are largely influenced by the choice of dependence function. The statistical tests reject the Gaussian dependence function, but do not reject the Student's t dependence function

Interpreting Financial Market Crashes as Earthquakes

We propose a modeling framework which allows for creating probability predictions on a future market crash in the medium term, like sometime in the next five days. Our framework draws upon noticeable similarities between stock returns around a financial market crash and seismic activity around earthquakes. Our model is incorporated in an Early Warning System for future crash days. Testing our EWS on S&P 500 data during the recent financial crisis, we find positive Hanssen-Kuiper Skill Scores. Furthermore our modeling framework is capable of exploiting information in the returns series not captured by well known and commonly used volatility models. EWS based on our models outperform EWS based on the volatility models forecasting extreme price movements, while forecasting is much less time-consuming

Specification Testing in Hawkes Models

We propose various specification tests for Hawkes models based on the Lagrange Multiplier (LM) principle. Hawkes models can be used to model the occurrence of extreme events in financial markets. Our specific testing focus is on extending a univariate model to a multivariate model, that is, we examine whether there is a conditional dependence between extreme events in markets. Simulations show that the test has good size and power, in particular for sample sizes that are typically encountered in practice. Applying the specification test for dependence to US stocks, bonds and exchange rate data, we find strong evidence for cross-excitation within segments as well as between segments. Therefore, we recommend that univariate Hawkes models be extended to account for the cross-triggering phenomenon