The Impact of Regulation and Economic Conditions on the Dynamics of Financial Markets

This dissertation encompasses four studies on selected topics in financial regulation and financial stability. The first paper asks whether there is empirical evidence of cyclicality in regulatory capital requirements prescribed by Basel regimes. This much debated issue was until then only addressed in theoretical papers, or simulation studies. While we do not find evidence on cyclicality in the Basel I or Basel II Standardized Approach, we find statistically and economically significant evidence concerning Basel II IRB portfolios. The second paper implements an agent based model to simulate an artificial asset market. This setup is then used to assess the impact of (i) a short selling ban, (ii) a Tobin Tax like transaction tax, (iii) mandatory Value-at-Risk limits and (iv) arbitrary combinations of these. I present results that show that while reducing volatility, a short selling ban nurtures market bubbles, and a Tobin Tax increases the variance of the returns. In this model a mandatory risk limit is beneficial from all stability perspectives taken. I examine the robustness of the model regarding its initial parameterization and show that high levels of a Tobin Tax lead to substantial market turbulence. The third paper considers the question which macroeconomic variables are linked to a time series of special interest from a financial stability perspective: firm defaults. Furthermore, we evaluate the empirical evidence of a hidden credit cycle by adding a latent factor to our models. We conclude that there is no empirical support of a hidden credit cycle in Austria once sufficient regressors are included and industry sectors differ in their respective macro drivers. The forth paper extends this work by implementing Bayesian Model Averaging (BMA) - a modern technique to counter model uncertainty. Furthermore we enrich this statistical approach by combining BMA with Bayesian ridge regression. We draw the conclusion that BMA is indeed a powerful tool to counter model uncertainty. Interest rates and components of inflation are distilled as major drivers for firm failures in Austria. (author's abstract

Model Uncertainty and Aggregated Default Probabilities: New Evidence from Austria

Understanding the determinants of aggregated default probabilities (PDs) has attracted substantial research over the past decades. This study addresses two major difficulties in understanding the determinants of aggregate PDs: Model uncertainty and multicollinearity among the regressors. We present Bayesian Model Averaging (BMA) as a powerful tool that overcomes model uncertainty. Furthermore, we supplement BMA with ridge regression to mitigate multicollinearity. We apply our approach to an Austrian dataset. Our findings suggest that factor prices like short term interest rates and energy prices constitute major drivers of default rates, while firms' profits reduce the expected number of failures. Finally, we show that the results of our baseline model are fairly robust to the choice of the prior model size.Series: Research Report Series / Department of Statistics and Mathematic

Regulatory Medicine Against Financial Market Instability: What Helps And What Hurts?

Do we know if a short selling ban or a Tobin Tax result in more stable asset prices? Or do they in fact make things worse? Just like medicine regulatory measures in financial markets aim at improving an already complex system, cause side effects and interplay with other measures. In this paper an agent based stock market model is built that tries to find answers to the questions above. In a stepwise procedure regulatory measures are introduced and their implications on market liquidity and stability examined. Particularly, the effects of (i) a ban on short selling (ii) a mandatory risk limit, i.e. a Value-at-Risk limit, (iii) an introduction of a Tobin Tax, i.e. transaction tax on trading, and (iv) any arbitrary combination of the measures are observed and discussed. The model is set up to incorporate non-linear feedback effects of leverage and liquidity constraints leading to fire sales. In its unregulated version the model outcome is capable of reproducing stylised facts of asset returns like fat tails and clustered volatility. Introducing regulatory measures shows that only a mandatory risk limit is beneficial from every perspective, while a short selling ban – though reducing volatility – increases tail risk. The contrary holds true for a Tobin Tax: it reduces the occurrence of crashes but increases volatility. Furthermore, the interplay of measures is not negligible: measures block each other and a well chosen combination can mitigate unforeseen side effects. Concerning the Tobin Tax the findings indicate that an overdose can do severe harm.Tobin Tax, transaction tax, short selling ban, Value-at-Risk limits, risk management herding, agent based models

What Drives Aggregate Credit Risk?

A deep understanding of the drivers of credit risk is valuable for financial institutions as well as for regulators from multiple viewpoints. The systemic component of credit risk drives losses across portfolios and thus poses a threat to financial stability. Traditional approaches consider macroeconomic variables as drivers of aggregate credit risk (ACR). However, recent literature suggests the existence of a latent risk factor influencing ACR, which is regularly interpreted as the latent credit cycle. We explicitly model this latent factor by adding an unobserved component to our models, which already include macroeconomic variables. In this paper we make use of insolvency rates of Austrian corporate industry sectors to model realized probabilities of default. The contribution of this paper to the literature on ACR risk is threefold. First, in order to cope with the lack of theory behind ACR drivers, we implement state-of-the-art variable selection algorithms to draw from a rich set of macroeconomic variables. Second, we add an unobserved risk factor to a state space model, which we estimate via a Kalman filter in an expectation maximization algorithm. Third, we analyze whether the consideration of an unobserved component indeed improves the fit of the estimated models.credit risk, unobserved component models, state space, Kalman filter, stress testing

Quantifying the Cyclicality of Regulatory Capital – First Evidence from Austria

With the financial crisis spreading to the real economy, the discussion about potential procyclical implications of Basel II received a surge of attention. While existing research approaches the topic either from a theoretical perspective or from an empirical perspective that draws on simulated data, we are first in studying the cyclicality of risk weights on the basis of realized data. Furthermore, we are able to differentiate not only between Basel I and Basel II, but also between the Standardized Approach (StA) and the internal ratings-based (IRB) approach. We argue that without knowledge of these approaches’ presumably distinct cyclicality of risk weights, any measure to dampen procyclicality is premature. For this purpose, we first study which banks opt for implementation of the IRB approach and then set up a panel model to quantify the cyclicality of capital requirements. While we find no evidence of cyclicality in portfolios subject to the Basel II StA, we find economically substantial and statistically significant effects in IRB portfolios.

Softshell: Dynamic Scheduling on GPUs

In this paper we present Softshell, a novel execution model for devices composed of multiple processing cores operating in a single instruction, multiple data fashion, such as graphics processing units (GPUs). The Softshell model is intuitive and more flexible than the kernel-based adaption of the stream processing model, which is currently the dominant model for general purpose GPU computation. Using the Softshell model, algorithms with a relatively low local degree of parallelism can execute efficiently on massively parallel architectures. Softshell has the following distinct advantages: (1) work can be dynamically issued directly on the device, eliminating the need for synchronization with an external source, i.e., the CPU; (2) its three-tier dynamic scheduler supports arbitrary scheduling strategies, including dynamic priorities and real-time scheduling; and (3) the user can influence, pause, and cancel work already submitted for parallel execution. The Softshell processing model thus brings capabilities to GPU architectures that were previously only known from operating-system designs and reserved for CPU programming. As a proof of our claims, we present a publicly available implementation of the Softshell processing model realized on top of CUDA. The benchmarks of this implementation demonstrate that our processing model is easy to use and also performs substantially better than the state-of-the-art kernel-based processing model for problems that have been difficult to parallelize in the past