Predicting Financial Crises: The Role of Asset Prices
Tristan Hennig (International Monetary Fund), et al.
We explore the early warning properties of a composite indicator which summarizes signals from a range of asset price growth and asset price volatility indicators to capture mispricing of risk in asset markets. Using a quarterly panel of 108 advanced and emerging economies over 1995-2017, we show that the combination of rapid asset price growth and low asset price volatility is a good predictor of future financial crises. Elevated levels of our indicator significantly increase the probability of entering a crisis within the next three years relative to normal times when the indicator is not elevated. The indicator outperforms credit-based early warning metrics, a result robust to prediction horizons, methodological choices, and income groups. Our results are consistent with the idea that measures based on asset prices can offer critical information about systemic risk levels to policymakers.
Identifying Financial Crises Using Machine Learning On Textual Data
Mary Chen (Federal Reserve Bank of Boston), et al.
We use machine learning techniques on textual data to identify financial crises. The onset of a crisis and its duration have implications for real economic activity, and as such can be valuable inputs into macroprudential, monetary, and fiscal policy. The academic literature and the policy realm rely mostly on expert judgment to determine crises, often with a lag. Consequently, crisis durations and the buildup phases of vulnerabilities are usually determined only with the benefit of hindsight. Although we can identify and forecast a portion of crises worldwide to various degrees with traditional econometric techniques and using readily available market data, we find that textual data helps in reducing false positives and false negatives in out-of-sample testing of such models, especially when the crises are considered more severe. Building a framework that is consistent across countries and in real time can benefit policymakers around the world, especially when international coordination is required across different government policies.
Measuring Systemic Financial Stress and its Risks for Growth
Sulkhan Chavleishvili (Aarhus U.) and Manfred Kremer European Central Bank
This paper proposes a general statistical framework for systemic financial stress indices which measure the severity of financial crises on a continuous scale. Several index designs from the financial stress and systemic risk literature can be represented as special cases. We introduce an enhanced daily variant of the CISS (composite indicator of systemic stress) for the euro area and the US. The CISS aggregates a representative set of stress indicators using their time-varying cross-correlations as systemic risk weights, computationally similar to how portfolio risk is computed from the risk characteristics of individual assets. A boot-strap algorithm provides test statistics. Single-equation and system quantile growth-at-risk regressions show that the CISS has stronger effects in the lower tails of the growth distribu-tion. Simulations based on a quantile VAR suggest that systemic stress is a major driver of the Great Recession, while its contribution to the COVID-19 crisis appears to be small.
Decoding Financial Crises: Analyzing Predictors and Evolution
Young SIk Jeong and Yaein Baek (Korea Institute for International Economic Policy)
We examine factors that predict financial crises and the evolution of financial crises using non-traditional methodologies, such as machine learning and system dynamics. Firstly, in our random forest model, the top six most important predictors among 12 indicators for the entire period (1870-2017) are the slope of the yield curve, the CPI, consumption, the debt service ratio, equity return, and public debt. Secondly, even though the manifestations of financial crises differ in each case, five common characteristics have been identified by examining various past financial crisis cases using a system dynamics approach (causal loop diagram). The first characteristic is a feedback loop that reinforces credit expansion. Next, the feedback loop leads to the buildup of financial crisis risk. Third, there is the shock that triggers the financial crisis. Fourth, there are risk-spreading factors. Lastly, individual financial crises do not end in themselves but have the common characteristic of becoming the seeds of new crises. In conclusion, two key findings emerge. First, the financial crisis is a systemic problem rather than an individual risk factor. Second, in diagnosing the recent situation, the results point to the risk of the financial crisis spreading.
An Empirical Evaluation of the Effects of Interest Rates and the Financial Crisis on the Global Stock Market Index
P. Raja Babu and V. Thangavel (St. Francis Institute of Mgt. and Research Mumbai)
Any nation that experiences a financial crisis will see negative consequences on its financial environment and economy. The financial crisis caused financial institutions to be disrupted, the stock market to fluctuate, and unemployment. The majority of the research concentrated on the long-term consequences of the financial crisis on the economy as well as how it affected currency and stock market movements and how it affected the global economy. However, since the 1929 Global Financial Crisis (GFC), no research has offered a thorough examination of the effects of financial crises on the global economy. The main objectives of this study are to review through the evaluation of the global financial crisis since 1929 and to examine its impacts.
Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno