Rodrigo Herrera

• PhD in Economics
• Professor (Associate) at University of Talca

We propose a multivariate dynamic intensity peaks‐over‐threshold model to capture extremes in multivariate return processes. The random occurrence of extremes is modeled by a multivariate dynamic intensity model, while temporal clustering of their size is captured by an autoregressive multiplicative error model. Applying the model to daily returns...

This paper develops a new class of dynamic models for forecasting extreme financial risk. This class of models is driven by the score of the conditional distribution with respect to both the duration between extreme events and the magnitude of these events. It is shown that the models are a feasible method for modeling the time-varying arrival inte...

In this paper we make use of option-implied volatilities to build a time-varying implied correlation matrix. Then, we use this matrix to estimate jointly both the covariance matrix of the returns and the implied covariance matrix dynamics. Finally, we do a backtest and show that the proposed model can effectively use the risk-neutral information to...

Air pollution, particularly PM2.5 particulate matter, is a significant issue in Santiago, the capital of Chile. Santiago’s pollution problem is exacerbated by its unique geographic location nestled against the Andes mountain range in the central valley of Chile. This paper uses network models that were developed primarily to analyze systemic risk i...

This paper examines the dynamics of connectedness among the realized volatility indices of 16 clean energy stocks belonging to the SPGCE and the implied volatility indices of two important stock markets—the S&P 500 and the STOXX50—and two commodities markets—the crude oil and gold markets. The empirical results show a unidirectional connectedness f...

Forecasting the risk of extreme losses is an important issue in the management of financial risk and has attracted a great deal of research attention. However, little attention has been paid to extreme losses in a higher frequency intraday setting. This paper proposes a novel marked point process model to capture extreme risk in intraday returns, t...

When prediction intervals are constructed using unobserved component models (UCM), problems can arise due to the possible existence of components that may or may not be conditionally heteroscedastic. Accurate coverage depends on correctly identifying the source of the heteroscedasticity. Different proposals for testing heteroscedasticity have been...

This paper examines extreme co-movements between the Australian and Canadian currencies, often known as commodity currencies, and gold and oil markets respectively. Here, two main approaches based on extreme value theory are compared in the context of explaining the co-movements between the markets in times of market instability. On the one hand, t...

This paper proposes a methodology based on a system of dynamic multiple linear equations that incorporates hourly, daily and annual seasonal characteristics for predicting hourly pm25 pollution concentrations for 11 meteorological stations in Santiago, Chile. It is demonstrated that the proposed model has the potential to match or even surpass the...

This paper investigates the relation between risk taking and market power in the US banking sector by introducing the effect of geographical spillovers caused by the transmission of risk taking among banks. For this purpose, we use spatial econometrics. Our results support a negative relation between risk taking and market power. The transmission o...

We analyze the degree of mutual excitation that exists between extreme events across the stock markets of OECD member nations and the Brent and WTI crude oil markets. For this analysis, marked point process models are proposed which are able to capture the dynamics of the intensity of occurrence and comovement during periods of crisis. The results...

Forecasting the risk of extreme losses is an important issue in the management of financial risk. There has been a great deal of research examining how option implied volatilities (IV) can be used to forecast asset return volatility. However, the role of IV in the context of predicting extreme risk has received relatively little attention. The pote...

Modeling and forecasting the volatility of asset returns is an important issue in the management of financial risk and has attracted a great deal of research attention. In recent years, research attention has shifted its focus to the issue of accurate estimates of risk measures such as Value-at-Risk (VaR) that capture the risk of extreme losses. Fo...

We propose a Markov-Switching Multifractal Peaks-Over-Threshold (MSM-POT) model to capture the dynamic behavior of the random occurrences of extreme events exceeding a high threshold in time series of returns. This approach allows introducing changes of regimes in the conditional mean function of the inter-exceedance times (i.e., the time between t...

We investigated the relationship between extreme prices of crude oil and natural gas. We found evidence that extreme events in these markets exhibit a self-enforcing dynamic and endogenous behavior, where the intensity of extreme events in the oil market influences the occurrence and magnitude of extreme events in the natural gas market. JEL classi...

We propose a multivariate dynamic intensity peaks-over-threshold model to capture ex- treme events in a multivariate time series of returns. The random occurrence of extreme events exceeding a threshold is modeled by means of a multivariate dynamic intensity model allowing for feedback effects between the individual processes. We propose alternativ...

Abstract Abnormally high price spikes in spot electricity markets represent a significant risk to market participants. As such, a literature has developed that focuses on forecasting the probability of such spike events, moving beyond simply forecasting the level of price. Many univariate time series models have been proposed to deal with spikes wi...

Forecasting the risk of extreme losses is an important issue in the management of financial risk. There has been a great deal of research examining how option implied volatilities (IV) can be used to forecasts asset return volatility. However, the impact of IV in the context of predicting extreme risk has received relatively little attention. The r...

We propose a multivariate dynamic intensity peaks-over-threshold model to capture extreme events in a multivariate time series of returns. The random occurrence of extreme events exceeding a threshold is modeled by means of a multivariate dynamic intensity model allowing for feedback effects between the individual processes. We propose alternative...

Primary concerns for traders since the deregulation of electricity markets include both the selection of optimal trading limits and risk quantification. These concerns came about as a consequence of unique stylized attributes exhibited by electricity spot prices, such as clustering of extremes, heavy-tails and common spikes. The authors of this pap...

Given the growing need for managing financial risk and the recent global crisis, risk prediction plays an increasingly important role in banking and finance. In this paper, we show how recent advances in the statistical analysis of extreme events can provide solid fundamentals for modeling these events. Our approach combines self-exciting marked po...

We analyze empirically the existence and the extent of financial contagion by means of extreme value theory in the Asian crisis. We consider two key markets, the stock exchange and the foreign exchange using daily data in the period 1992-2001. We present several notions of financial contagion as a significant change in volatility tail dependence (V...

Crude oil is a dynamically traded commodity that affects many economies. We propose a collection of marked self-exciting point processes with dependent arrival rates for extreme events in oil markets and related risk measures. The models treat the time among extreme events in oil markets as a stochastic process. The main advantage of this approach...

Existing papers on extreme dependence use symmetrical thresholds to define simultaneous stock market booms or crashes such as the joint occurrence of the upper or lower one percent return quantile in both stock markets. We show that the probability of the joint occurrence of extreme stock returns may be higher for asymmetric thresholds than for sym...

The analysis of return series from financial markets is often based on the Peaks-over-threshold (POT) model. This model assumes independent and identically distributed observations and therefore a Poisson process is used to characterize the occurrence of extreme events. However, stylized facts such as clustered extremes and serial dependence typica...

We demonstrate the usefulness of Extreme value Theory (EVT) to evaluate magnitudes of stock market crashes and provide some extensions. A common practice in EVT is to compute either unconditional quantiles of the loss distribution or conditional methods linking GARCH models to EVT. Our approach combines self-exciting models for exceedances over a g...

El presente estudio tiene por objeto analizar comparativamente la eficiencia técnica de la banca chilena y alemana, mediante fronteras estocásticas de producción y costo y análisis envolvente de datos. En ambas fronteras estocásticas se utilizó una tecnología translogarítmica. Con el análisis envolvente de datos se calculó el índice de Malmquist pa...

We consider a fault tolerance version of the Uncapacitated Facility Location Problem with User Preferences. As a consequence, our problem, which we wish to name the Uncapacitated Facility Location Problem with user preferences and q-level of reliability (q-level UFLPP), is much more difficult to solve. A computational study shows the advantages and...

El presente estudio tiene por objeto analizar la eficiencia técnica de la banca chilena, mediante fronteras estocásticas de producción. De los resultados obtenidos para el periodo 1991-2000, se puede mencionar que los bancos chilenos son altamente eficientes, tanto en costos (alrededor de un 80% de eficiencia en promedio) como en producción (alcanz...