nep-fmk New Economics Papers
on Financial Markets
Issue of 2009‒08‒22
four papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. "Forecasting Volatility and Spillovers in Crude Oil Spot, Forward and Futures Markets" By Chia-Lin Chang; Michael McAleer; Roengchai Tansuchat
  2. Spatial Contagion of Global Financial Crisis By Ari Tjahjawandita; Tito Dimas Pradono; Rullan Rinaldi
  3. "Has the Basel II Accord Encouraged Risk Management During the 2008-09 Financial Crisis?" By Michael McAleer; Juan-Angel Jimenez-Martin; Teodosio Perez-Amaral
  4. "What Happened to Risk Management During the 2008-09 Financial Crisis?" By Michael McAleer; Juan-Angel Jimenez-Martin; Teodosio Perez-Amaral

  1. By: Chia-Lin Chang (Department of Applied Economics, National Chung Hsing University); Michael McAleer (Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo); Roengchai Tansuchat (Faculty of Economics, Maejo University and Faculty of Economics, Chiang Mai University)
    Abstract: Crude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at- Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia- Pacific), which are likely to be highly correlated. This paper analyses the volatility spillover effects across and within the four markets, using three multivariate GARCH models, namely the CCC, VARMA-GARCH and VARMA-AGARCH models. A rolling window approach is used to forecast the 1-day ahead conditional correlations. The paper presents evidence of volatility spillovers and asymmetric effects on the conditional variances for most pairs of series. In addition, the forecasted conditional correlations between pairs of crude oil returns have both positive and negative trends.
    Date: 2009–08
    URL: http://d.repec.org/n?u=RePEc:tky:fseres:2009cf641&r=fmk
  2. By: Ari Tjahjawandita (Department of Economics, Padjadjaran University); Tito Dimas Pradono (Department of Economics, Padjadjaran University); Rullan Rinaldi (Department of Economics, Padjadjaran University)
    Abstract: The global financial crisis triggered by the credit crisis in the USA as its epicenter, quickly spread across the globe. The crisis starts spreading around the world in the middle of 2007 and along the 2008, where stock markets in major economies fell, followed by collapses of large companies and leading financial institutions. In a world where economies are integrated, the spread of such crisis is unavoidable. In this paper, we try to estimate the spill over effect of the global financial crises across borders and regions. Using spatial econometrics method we employ distance based weight matrix to estimate the spatial dependence and spatial heterogeneity of the crises. On the sensitivity analysis, we also employ weights matrix that is corrected by the governance and the economic freedom index to shows how the virtual space of governance, economic institution and regimes affect the spread of the crises.
    Keywords: Global Financial Crises, Spillover Effect, Institutions, Globalization, Spatial Econometrics
    JEL: G1 C1
    Date: 2009–08
    URL: http://d.repec.org/n?u=RePEc:unp:wpaper:200906&r=fmk
  3. By: Michael McAleer (Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo); Juan-Angel Jimenez-Martin (Department of Quantitative Economics, Complutense University of Madrid); Teodosio Perez-Amaral (Department of Quantitative Economics, Complutense University of Madrid)
    Abstract: The Basel II Accord requires that banks and other Authorized Deposit-taking Institutions (ADIs) communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models to measure Value-at-Risk (VaR). The risk estimates of these models are used to determine capital requirements and associated capital costs of ADIs, depending in part on the number of previous violations, whereby realised losses exceed the estimated VaR. In this paper we define risk management in terms of choosing sensibly from a variety of risk models, discuss the selection of optimal risk models, consider combining alternative risk models, discuss the choice between a conservative and aggressive risk management strategy, and evaluate the effects of the Basel II Accord on risk management. We also examine how risk management strategies performed during the 2008-09 financial crisis, evaluate how the financial crisis affected risk management practices, forecasting VaR and daily capital charges, and discuss alternative policy recommendations, especially in light of the financial crisis. These issues are illustrated using Standard and Poor's 500 Index, with an emphasis on how risk management practices were monitored and encouraged by the Basel II Accord regulations during the financial crisis.
    Date: 2009–08
    URL: http://d.repec.org/n?u=RePEc:tky:fseres:2009cf643&r=fmk
  4. By: Michael McAleer (Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo); Juan-Angel Jimenez-Martin (Department of Quantitative Economics, Complutense University of Madrid); Teodosio Perez-Amaral (Department of Quantitative Economics, Complutense University of Madrid)
    Abstract: When dealing with market risk under the Basel II Accord, variation pays in the form of lower capital requirements and higher profits. Typically, GARCH type models are chosen to forecast Value-at-Risk (VaR) using a single risk model. In this paper we illustrate two useful variations to the standard mechanism for choosing forecasts, namely: (i) combining different forecast models for each period, such as a daily model that forecasts the supremum or infinum value for the VaR; (ii) alternatively, select a single model to forecast VaR, and then modify the daily forecast, depending on the recent history of violations under the Basel II Accord. We illustrate these points using the Standard and Poor's 500 Composite Index. In many cases we find significant decreases in the capital requirements, while incurring a number of violations that stays within the Basel II Accord limits.
    Date: 2009–08
    URL: http://d.repec.org/n?u=RePEc:tky:fseres:2009cf636&r=fmk

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