Khaldoun Khashanah and Linyan Miao
This paper empirically investigates the structural evolution of the US financial systems. It particularly aims to explore if the structure of the financial systems changes when…
Abstract
Purpose
This paper empirically investigates the structural evolution of the US financial systems. It particularly aims to explore if the structure of the financial systems changes when the economy enters a recession.
Design/methodology/approach
The empirical analysis is conducted through the statistical approach of principal components analysis (PCA) and the graph theoretic approach of minimum spanning trees (MSTs).
Findings
The PCA results suggest that the VIX was the dominant factor influencing the financial system prior to the recession; however, the monetary policy represented by the three‐month T‐bill yield became the leading factor in the system during the recession. By analyzing the MSTs, we find evidence that the structure of the financial system during the economic recession is substantially different from that during the period of economic expansion. Moreover, we discover that the financial markets are more integrated during the economic recession. The much stronger integration of the financial system was found to start right before the advent of the recession.
Practical implications
Research findings will help individuals, institutions, regulators, central bankers better understand the market structure under the economic turmoil, so more efficient strategies can be used to minimize the systemic risk.
Originality/value
This study compares the structure of the US financial markets in economic expansion and contraction periods. The structural dynamics of the financial system are explored, focusing on the recent economic recession triggered by the US subprime mortgage crisis. We introduce a new systemic risk measure.
Details
Keywords
Fotios C. Harmantzis, Linyan Miao and Yifan Chien
This paper aims to test empirically the performance of different models in measuring VaR and ES in the presence of heavy tails in returns using historical data.
Abstract
Purpose
This paper aims to test empirically the performance of different models in measuring VaR and ES in the presence of heavy tails in returns using historical data.
Design/methodology/approach
Daily returns of popular indices (S&P500, DAX, CAC, Nikkei, TSE, and FTSE) and currencies (US dollar vs Euro, Yen, Pound, and Canadian dollar) for over ten years are modeled with empirical (or historical), Gaussian, Generalized Pareto (peak over threshold (POT) technique of extreme value theory (EVT)) and Stable Paretian distribution (both symmetric and non‐symmetric). Experimentation on different factors that affect modeling, e.g. rolling window size and confidence level, has been conducted.
Findings
In estimating VaR, the results show that models that capture rare events can predict risk more accurately than non‐fat‐tailed models. For ES estimation, the historical model (as expected) and POT method are proved to give more accurate estimations. Gaussian model underestimates ES, while Stable Paretian framework overestimates ES.
Practical implications
Research findings are useful to investors and the way they perceive market risk, risk managers and the way they measure risk and calibrate their models, e.g. shortcomings of VaR, and regulators in central banks.
Originality/value
A comparative, thorough empirical study on a number of financial time series (currencies, indices) that aims to reveal the pros and cons of Gaussian versus fat‐tailed models and Stable Paretian versus EVT, in estimating two popular risk measures (VaR and ES), in the presence of extreme events. The effects of model assumptions on different parameters have also been studied in the paper.