Identifying and Predicting Financial Earthquakes Using Hawkes Processes

This dissertation attempts to identify and predict earthquakes in the financial market (financial crashes) using Hawkes processes. Models based on Hawkes processes were first used in the earthquake literature. The dissertation shows Hawkes processes also match investors' self-exciting herding behavior around crashes, which has similar characteristics to the self-exciting behavior of seismic activity around earthquakes. In Chapter 2 an Early Warning System based on Hawkes models is developed that indicates the arrival of a crash within a trading week. EWS based Hawkes model outperform EWS based on well-known and commonly used volatility models, proving that these models do not capture all information that can be used to predict crashes. Specification tests for Hawkes models with a specific focus on testing for cross-excitation, are designed in Chapter 3. Chapter 3 as well as Chapter 4, indicate that shocks in one financial market affect the occurrence (and magnitude) of shocks in other financial markets. Moreover, comparing predictions of models with and without cross-excitation, more accurate predictions are obtained including cross-excitation. The last Chapter (Chapter 5) develops methods to estimate non-affine Hawkes models using the information in option prices with the aid of Machine Learning techniques

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

Interpreting financial market crashes as earthquakes: A new early warning system for medium term crashes

To create probability predictions of a market crash occurring over a certain period, the authors propose a modeling framework based on similarities between stock returns around a market crash and seismic activity around earthquakes. They construct an early warning system for future crash days, test it on the S&P 500 Index data during the financial crisis, and conclude that their model improves on existing volatility models

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 U.S. stocks, bonds, and exchange rate data, we find strong evidence for cross-excitation within segments as well as between segments, which cannot simply be explained by volatility spillovers. Therefore, we recommend that univariate Hawkes models be extended to account for the cross-triggering phenomenon