part I
Basle Committee on Banking Supervision, 1995, An Internal Model-Based Approach to Market Risk Capital Requirements, Basle, Switzerland.
Bartolomei, S. M., and A. L. Sweet, 1989, A Note on a Comparison of Exponential Smoothing Methods for Forecasting Seasonal Series, International Journal of Forecasting, 5, 111-116.
Beltratti, A. and C. Morana, 1999, Computing Value at Risk with High Frequency Data, Journal of Empirical Finance, 6, 431-455.
Billo, M. and L. Pelizzon, 2000, Value-at-Risk: A Multivariate Switching Regime Approach, Journal of Empirical Finance, 7, 531-554.
Bollerslev, T., 1986, Generalized Autoregressive Conditional Heteroskedasticity, Journal of Economics, 31, 307-327.
Brooks, C., A. D. Clare and G. Persand, 2000, A Word of Caution on Calculating Market-Based Minimum Capital Risk Requirements, Journal of Banking and Finance, 14, 1557-1574.
Brooks, C., A. D. Clare and G. Persand, 2002, A Note on Estimating Market-Based Minimum Capital Risk Requirements: A Multivariate GARCH Approach, The Manchester School, 70, 666-681.
Cabedo, J. D. and I. Moya, 2003, Estimating Oil Price ‘Value at Risk’ Using the Historical Simulation Approach, Energy Economics, 25, 239-253.
Callen, J. L., C. C. Y. Kwan, P. C. Y. Yip and Y. Yuan, 1996, Netural Network Forecasting of Quarterly Accounting Earnings, International Journal of Forecasting 12, 1996, pp. 475-482.
Cassidy, C. and M. Gizycki, 1997, Measuring Trading Market Risk: Value-at-Risk and Backtesting Techniques, Reserve Bank of Australia, Research Discussion Paper.
Christoffersen, P. F. and F. X. Diebold, 2000, How Relevant is Volatility Forecasting for Financial Risk Management, Review of Economics and Statistics, 82, 12-22.
Christoffersen, P., J. Hahn and A. Inoue, 2001, Testing and Comparing Value-at-Risk Measures, Journal of Empirical Finance, 8, 325-342.
Dowd, K., 1998, Beyond Value-at-Risk, Wiley.
Engle, R., 1982, Autoregressive Conditional Heteroskedasticity with Estimates of Variance of United Kingdom Inflation, Econometrics, 50, 987-1007.
Giot, P. and S. Laurent, 2003, Market Risk in Commodity Markets: A VaR Approach, Energy Economics, 25, 435-457.
Giot, P. and S. Laurent, 2004, Modelling Daily Value-at-Risk Using Realized Volatility and ARCH Type Models, Journal of Empirical Finance, 11, 379-398.
Hendricks, D., 1996, Evaluation of Value-at-Risk Models Using Historical Data, Federal Reserve Bank of New York Economic Policy Review, April, 39-69.
Hsieh, D. A., 1993, Implications of Nonlinear Dynamics for Financial Risk Management, Journal of Financial and Quantitative Analysis, 28, 41-64.
J. P. Morgan / Reuters, 1996, RiskMetrics – Technical Document (4th edition), New York.
Jorion, P., 2002, Value-at-Risk: The New Benchmark for Controlling Market Risk (2d edition), New York McGraw-Hill.
Kupiec, P., 1995, Techniques for Varying the Accuracy of Risk Measurement Models, Journal of Derivatives, 2, 173-184.
Lacoursiere, C., 1997, VAR Comes to Energy Risk Management, Derivatives Strategy, March, 48-49.
Moosa, I. A. and B. Bollen, 2002, A Benchmark for Measuring Bias in Estimated Daily Value at Risk, International Review of Financial Analysis, 11, 85-100.
Pan, P. N. and W. H. Starbucks, 1990, Innocents in the Forest: Forecasting and Research Methods, Journal of Management, 16, 433-460.
Sadorsky, P., 1999, Oil Price Shocks and Stock Market Activity, Energy Economics, 21, 449-469.
Sarma, M., S. Thomas and A. Shah, 2003, Selecting of Value-at-Risk Models, Journal of Forecasting, 22, 337-358.
Siegl, T. and A. West, 2001, Statistical Bootstrapping Methods in VaR Calculation, Applied Mathematical Finance, 8, 167-181.
Swanson, N. R. and H. White, 1997, Forecasting Economic Time Series Using Flexible Versus Fixed Specification and Linear versus Nonlinear Econometric Models, International Journal of Forecasting, 13, 439-461.
part II
Ahn, D. H., R. Dittmar, and A. R. Gallant, 2002, Quadratic Term Structure Models: Theory and Evidence, The Review of Financial Studies, 15, 243-288.
Andersen, T. G., 1996, Return Volatility and Trading Volume: An Information Flow Interpretation to Stochastic Volatility, Journal of Finance, 51, 169-204.
Bahmani-Oskooee, M. and F. Brown, 2004, Kalman Filter Approach to Estimate the Demand for International Reserves, Applied Economics, 36, 1655-1668.
Bai, J. and P. Perron, 2003, Computation and Analysis of Multiple Structural Change Models, Journal of Applied Economics, 18, 1-22.
Baillie, R. T. and R. J. Myers, 1991, Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge, Journal of Applied Economics, 6, 109-124.
Bjørnland, H. C., 2001, Identifying Domestic and Imported Core Inflation, Applied Economics, 33, 1819-1831.
Bollerslev, T., 1986, Generalized Autoregressive Conditional Heteroskedasticity, Journal of Economics, 31, 307-327.
Campbell, J. T., 1934, The Poisson Correlation Function, Proceedings of the Edinburgh Mathematical Society, Series 2, 18-26.
Chan, W. H. and J. M. Maheu, 2002, Conditional Jump Dynamics in Stock Market Returns, Journal of Business & Economic Statistics, 20, 377-389.
Chan, W. H., 2003, A Correlated Bivariate Poisson Jump Model for Foreign Exchange, Empirical Economics, 28, 669-685.
Chan, W. H., 2004, Conditional Correlated Jump Dynamics in Foreign Exchange, Economics Letters, 83, 23-28.
Chang, K. H. and M. J. Kim, 2001, Jumps and Time-varying Correlations in Daily Foreign Exchange Rates, Journal of International Money and Finance, 20, 611-637.
Chaudhuri, K., 2001, Long-run Prices of Primary Commodities and Oil Prices, Applied Economics, 33, 531-538.
Das, S. R., 2001, The Surprise Element: Jumps in Interest Rates, Journal of Econometrics, 106, 27-65.
Engle, R., 1982, Autoregressive Conditional Heteroskedasticity with Estimates of Variance of United Kingdom Inflation, Econometrics, 50, 987-1007.
Eraker, B., M. Johannes, and N. Polson, 2003, The Impact of Jumps in Volatility and Returns, Journal of Finance, 63, 1269-1300.
Ewing, B. T., F. Malik, and O. Ozfidan, 2002, Volatility Transmission in the Oil and Natural Gas Markets, Energy Economics, 24, 525-538.
Hammoudeh, S., H. Li and B. Jeon, 2003, Causality and Volatility Spillovers among Petroleum Prices of WTI, Gasoline and Heating Oil in Different Locations, North American Journal of Economics and Finance, 14, 89-114.
Jiménez-Rodríguez, R., and M. Sánchez, 2005, Oil Price Shocks and Real GDP Growth: Empirical Evidence for Some OECD Countries, Applied Economics, 37, 201-228.
Johannes, M., 2003, The Statistical and Economic Role of Jumps in Continuous-time Interest Rate Models, Journal of Finance, 59, 227-260.
Jorion, P., 1988, On Jump Processes in the Foreign Exchange and Stock Markets, Review of Financial Studies, 1, 427-445.![new window](/gs32/images/newin.png)
M’Kendrick, A. G., 1926, Applications of Mathematics to Medical Problems, Proceedings of the Edinburgh Mathematical Society, 44, 98-130.
Maheu, J. M. and T. H. McCurdy, 2004, News Arrival, Jump Dynamics and Volatility Components for Individual Stock Returns, Journal of Finance, 59, 755-793.
Pan, J., 2002, The Jump-risk Premia Implicit in Options: Evidence from an Integrated Time-series Study”, Journal of Financial Economics, 63, 3-50.
Ross, S. A., 1989, Information and Volatility: The No-Arbitrage Martingale Approach to Timing and Resolution Irrelevancy, Journal of Finance, 44, 1-17.
Sadorsky, P., 1999, Oil Price Shocks and Stock Market Activity, Energy Economics, 21, 449-469.
Taylor, S. J., 1986, Modeling financial time series, Wiley, Chichester.
part III
Bai, J. and P. Perron, 1998, Estimating and Testing Linear Models with Multiple Structural Changes, Econometrica, 66, 47-78.
Bai, J. and P. Perron, 2003, Computation and Analysis of Multiple Structural Change Models, Journal of Applied Econometrics, 18, 1-22.
Chauvet, M and S. Potter, 2002, Predicting a Recession: Evidence from the Yield Curve in the Presence of Structural Breaks, Economic letters, 77, 245-253.
Chong, Y. Y. and D. F. Hendry, 1986, Econometric Evaluation of Linear Macroeconomic Models, Review of Economic Studies, 53, 671-690.
Clark, T. E. and M. W. McCracken, 2001, Tests of Equal Forecast Accuracy and Encompassing for the Nested Models, Journal of Econometrics, 105, 85-110.
Clark, T. E. and M. W. McCracken, 2004, Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts, Working Paper, Federal Reserve Bank of Kansas City and University of Missouri-Columbia.
Clements, M. P. and D. F. Hendry, 1993, On the Limitations of Comparing Mean Squared Forecast Errors: Comment, Journal of Forecasting, 12, 617-637.
Clements, M. P. and D. F. Hendry, 1998, Forecasting Economic Processes, International Journal of Forecasting, 14, 111-131.
Clements, M. P. and D. F. Hendry, 1999, Forecasting Non-stationary Economic Time Series, Cambridge, MA: MIT Press.
Diebold, F. X. and R. S. Mariano, 1995, Comparing Predictive Accuracy, Journal of Business and Economic Statistics, 13, 253-263.
Gabriel, A., S. Lopes and L. C. Nunes, 2003, Instability in Conintegration Regressions: A Brief Review with an Application to Money Demand in Portugal, Applied Economics, 35, 893-900.
Giacomini, R. and H. White, 2005, Test of Conditional Predictive Ability, Working Paper, University of California, San Diego.
Hamilton, J. D., 2001, A Parametric Approach to Flexible Non-linear Inference, Econometrica, 69, 537-573.
Harris, R. D. F. and J. Shen, 2003, Robust Estimation of the Optimal Hedge Ratio, Journal of Futures Markets, 23, 799-816.
Harvey, D. I., S. J. Leybourne and P. Newbold, 1998, Tests for Forecast Encompassing, Journal of Business and Economic Statistics, 16, 254-259.
Inoue, A. and L. Kilian, 2002, In-sample or Out-of-sample Tests of Predictability? Which One Should We Use? Working Paper, No. 195, European Central Bank.
Jardet, C., 2004, Why Did the Term Structure of Interest Rates Lose Its Predictive Power? Economic Modelling, 21, 509-524.
Koop, G. and S. Potter, 2000, Non-linearity, Structural Breaks, or Outliers in Economic Time Series, Chapter 4 in Non-linear Econometrics Modeling in Time Series Analysis, W. A. Barnett, D. F. Hendry, S. Hylleberg, T. Terasvirta, D. Tjostheim and A. Wurtz (Eds.), Cambridge University Press, 61-78.
Krolzig, H., 2001, Business Cycle Measurement in the Presence of Structural Change: International Evidence, International Journal of Forecasting, 17, 349-368.
McCracken, M. W., 2004, Asymptotics for Out of Sample Tests of Granger Causality, Working Paper, University of Missouri, Columbia.
Ng, H. G. and M. McAleer, 2004, Recursive Modelling of Symmetric and Asymmetric Volatility in the Presence of Extreme Observations, International Journal of Forecasting, 20, 115-129.
Pesaran, M. H. and A. Timmermann, 2004, How Costly Is It to Ignore Breaks When Forecasting the Direction of a Time Series? International Journal of Forecasting, 20, 411-425.
Rapach, D. and C. E. Weber, 2004, Financial Variables and the Simulated Out-of-sample Forecast Ability of U.S. Output Growth Since 1985: An Encompassing Approach, Economic Inquiry, 42, 717-738.
Shaffer, S., 2003, Using Prior Bias to Improve Forecast Accuracy, Applied Economic Letters, 10, 459-461.
Stock, J. H. and M. W. Watson, 2003, Forecasting Output and Inflation: The Role of Asset Prices, Journal of Economic Literature, 41, 788-829.
Swanson, N. R. and H. White, 1997, Forecasting Economic Time Series Using Flexible verses Fixed Specification and Linear versus Nonlinear Econometric Models, International Journal of Forecasting, 13, 439-461.
Wang, Z. and D. A. Bessler, 2004, Forecasting Performance of Multivariate Time Series Models with Full and Reduced Rank: An Empirical Examination, International Journal of Forecasting, 20, 683-695.
West, K. D., 1996, Asymptotic Inference about Predictive Ability, Econometrica, 64, 1067-1084.