Modelling and forecasting the global financial crisis: Initial findings using heterosckedastic log-periodic models

The financial crisis of 2007-2009 has begun in July 2007 when a loss of confidence by investors in the value of securitized mortgages in the United States resulted in a liquidity crisis. World stock markets peaked in October 2007 and then entered a period of high volatility which culminated with the market crashes in September and October 2008. Since March 2009, the world stock markets have rebounded, but strong uncertainties still remain. In order to get more insights into the current world markets operation, we consider log-periodic models of price movements, which has been largely used in the past to forecast financial crashes and "anti-bubbles". Both the original and an extended model which accounts for heteroskedasticity and autocorrelation are fitted to the American S&P500 index. The empirical analysis reveal three interesting points: i) the log-periodic models outperform standard financial models when long-term out-of-sample forecasting is of concern. ii) the log-periodic-AR(1)- GARCH(1,1) model has residuals with better statistical properties than the original model and iii) the current market rebound should peak at the beginning of 2010.Log-periodic models, Crashes, Anti-Bubbles, Long-term Forecasting, Out-of-sample Forecasting.

Credit Risk Management (Cont.)

In this issue we publish the fourth part of professor Fantazzini’s consultation series on econometric analysis of financial data in risk management. This time it deals with the topic of credit risk management. After having described one-dimensional models of credit risk in the previous issue the author is analyzing multidimensional models which make it possible to assess the default probability of borrower’s portfolioCredit Risk; Value at Risk; Expected Shortfall; CreditMetrics; KMV; CreditRisk+; CreditPortfolioView; Backtesting; Berkowitz Test

Econometric Analysis of Financial Data in Risk Management

This part completes the consultation series of Dean Fantazzini dealing with econometric analysis of financial data in credit risk management. Particularly, analysis of multidimensional credit risk models is continued from the previous discussionCredit Risk; Value at Risk; Expected Shortfall; CreditMetrics; KMV; CreditRisk+; CreditPortfolioView; Backtesting; Berkowitz Test

Global oil risks in the early 21st century

The Deepwater Horizon incident demonstrated that most of the oil left is deep offshore or in other difficult to reach locations. Moreover, obtaining the oil remaining in currently producing reservoirs requires additional equipment and technology that comes at a higher price in both capital and energy. In this regard, the physical limitations on producing ever-increasing quantities of oil are highlighted as well as the possibility of the peak of production occurring this decade. The economics of oil supply and demand are also briefly discussed showing why the available supply is basically fixed in the short to medium term. Also, an alarm bell for economic recessions is shown to be when energy takes a disproportionate amount of total consumer expenditures. In this context, risk mitigation practices in government and business are called for. As for the former, early education of the citizenry of the risk of economic contraction is a prudent policy to minimize potential future social discord. As for the latter, all business operations should be examined with the aim of building in resilience and preparing for a scenario in which capital and energy are much more expensive than in the business-as-usual one.Peak oil, Economic risks, Energy transition risks, Government risks, Business risks

Does the hashrate affect the bitcoin price?

This paper investigates the relationship between the bitcoin price and the hashrate by disentangling the effects of the energy efficiency of the bitcoin mining equipment, bitcoin halving, and of structural breaks on the price dynamics. For this purpose, we propose a methodology based on exponential smoothing to model the dynamics of the Bitcoin network energy efficiency. We consider either directly the hashrate or the bitcoin cost-of-production model (CPM) as a proxy for the hashrate, to take any nonlinearity into account. In the first examined sub-sample (01/08/2016-04/12/2017), the hashrate and the CPMs were never significant, while a significant cointegration relationship was found in the second sub-sample (11/12/2017-24/02/2020). The empirical evidence shows that it is better to consider the hashrate directly rather than its proxy represented by the CPM when modeling its relationship with the bitcoin price. Moreover, the causality is always uni-directional going from the bitcoin price to the hashrate (or its proxies), with lags ranging from 1 week up to 6 weeks later. These findings are consistent with a large literature in energy economics, which showed that oil and gas returns affect the purchase of the drilling rigs with a delay of up to 3 months, whereas the impact of changes in the rig count on oil and gas returns is limited or not significant

Economic Factors in a Model of Voting: The Case of The Netherlands, Great Britain, and Israel

A spatial model of voting in parliamentary elections is estimated by using survey data from The Netherlands, Great Brit-ain, and Israel. It is shown that more educated voters put more weight on the parties’ political programs. The choice of less educated voters depends primarily on their social and economic status. In the case of Israel, observance of religions traditions plays the same role as education in European countries: the more secular is the voter, the greater is the impact of the party’s policy platform on his choicespatial model; voting; survey data

Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets

This paper investigates the estimation of the Value-at-Risk (VaR) across various probability levels for the log-returns of a comprehensive dataset comprising four thousand crypto-assets. Employing four recently introduced Adaptive Conformal Inference (ACI) algorithms, we aim to provide robust uncertainty estimates crucial for effective risk management in financial markets. We contrast the performance of these ACI algorithms with that of traditional benchmark models, including GARCH models and daily range models. Despite the substantial volatility observed in the majority of crypto-assets, our findings indicate that ACI algorithms exhibit notable efficacy. In contrast, daily range models, and to a lesser extent, GARCH models, encounter challenges related to numerical convergence issues and structural breaks. Among the ACI algorithms, the Fully Adaptive Conformal Inference (FACI) and the Scale-Free Online Gradient Descent (SF-OGD) stand out for their ability to provide precise VaR estimates across all quantiles examined. Conversely, the Aggregated Adaptive Conformal Inference (AgACI) and the Strongly Adaptive Online Conformal Prediction (SAOCP) demonstrate proficiency in estimating VaR for extreme quantiles but tend to be overly conservative for higher probability levels. These conclusions withstand robustness checks encompassing the market capitalization of crypto-assets, time series size, and different forecasting methods for asset log-returns. This study underscores the promise of ACI algorithms in enhancing risk assessment practices in the context of volatile and dynamic crypto-asset markets

The Oil Price Crash in 2014/15: Was There a (Negative) Financial Bubble?

This paper suggests that there was a negative bubble in oil prices in 2014/15, which decreased them beyond the level justified by economic fundamentals. This proposition is corroborated by two sets of bubble detection strategies: the first set consists of tests for financial bubbles, while the second set consists of the log-periodic power law (LPPL) model for negative financial bubbles. Despite the methodological differences between these detection methods, they provided the same outcome: the oil price experienced a statistically significant negative financial bubble in the last months of 2014 and at the beginning of 2015. These results also hold after several robustness checks which consider the effect of conditional heteroskedasticity, model set-ups with additional restrictions, longer data samples, tests with lower frequency data and with an alternative proxy variable to measure the fundamental value of oil

Editorial for the Special Issue on 'Computational Methods for Russian Economic and Financial Modelling'

This double-issue contains 11 papers invited for the first special issue on “Computational methods for Russian economic and financial modelling”. It was an attempt to explore and bring together practical, state-of-the-art applications of computational techniques with a particular focus on Russia and the Commonwealth of Independent States. The response was beyond expectations and managed to cover a wide range of issues, so that a double-issue was considered: the first dealing with Finance and the second with Economics

Small Sample Properties of Copula-GARCH Modelling: A Monte Carlo Study

Copula-GARCH models have been recently proposed in the financial literature as a statistical tool to build flexible multivariate distributions. Our extensive simulation studies investigate the small sample properties of these models and examine how misspecification in the marginals may affect the estimation of the dependence function represented by the copula. We show that the use of normal marginals when the true Data Generating Process is leptokurtic or asymmetric, produces negatively biased estimates of the normal copula correlations. A striking result is that these biases reach their highest value when correlations are strongly negative, and viceversa. This result remains unchanged with both positively skewed and negatively skewed data, while no biases are found if the variables are uncorrelated. Besides, the effect of marginals asymmetry on correlations is smaller than that of leptokurtosis. We finally analyse the performance of these models in terms of numerical convergence and positive definiteness of the estimated copula correlation matrix.Copulas, Copula-GARCH models, Maximum Likelihood, Simulation, Small Sample Properties.