RePEc: Research Papers in Economics
Research Papers in Economics is a collaborative effort of hundreds of volunteers in many countries to enhance the dissemination of research in economics.
NCER Working Paper Series
2015
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#109Download full text
- Keywords:
- Smooth transition conditional correlation; Structural breaks; Return comovement;
(Published)Crude Oil and Agricultural Futures: An Analysis of Correlation Dynamics
Correlations between oil and agricultural commodities have varied over previous decades, impacted by renewable fuels policy and turbulent economic conditions. We estimate smooth transition conditional correlation models for 12 agricultural commodities and WTI crude oil. While a structural change in correlations occurred concurrently with the introduction of biofuel policy, oil and food price levels are also key influences. High correlation between biofuel feedstocks and oil is more likely to occur when food and oil price levels are high. Correlation with oil returns is strong for biofuel feedstocks, unlike with other agricultural futures, suggesting limited contagion from energy to food markets.
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#108Download full text
- JEL-Codes:
- C32; C52
- Keywords:
- autoregressive conditional heteroskedasticity, modelling volatility, testing parameter constancy, time-varying GARCH
Testing constancy of unconditional variance in volatility models by misspecification and specification tests
The topic of this paper is testing the hypothesis of constant unconditional variance in GARCH models against the alternative that the unconditional variance changes deterministically over time. Tests of this hypothesis have previously been performed as misspecification tests after fitting a GARCH model to the original series. It is found by simulation that the positive size distortion present in these tests is a function of the kurtosis of the GARCH process. Adjusting the size by numerical methods is considered. The possibility of testing the constancy of the unconditional variance before fitting a GARCH model to the data is discussed. The power of the ensuing test is vastly superior to that of the misspecification test and the size distortion minimal. The test has reasonable power already in very short time series. It would thus serve as a test of constant variance in conditional mean models. An application to exchange rate returns is included.
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#107Download full text
- JEL-Codes:
- C12;C15;C32;G17
- Keywords:
- Causality, Forward recursion, Hypothesis testing, Inflation, Output, Recursvie rolling test, Rolling Window, Yield curve
Change Detection and the Casual Impact of the Yield Curve
Causal relationships in econometrics are typically based on the concept of predictability and are established in terms of tests for Granger causality. These causal relationships are susceptible to change, especially during times of financial turbulence, making the real-time detection of instability an important practical issue. This paper develops a test for detecting changes in causal relationships based on a recursive rolling window, which is analogous to the procedure used in recent work on financial bubble detection. The limiting distribution of the test takes a simple form under the null hypothesis and is easy to implement in conditions of homoskedasticity, conditional heteroskedasticity and unconditional heteroskedasticity. Simulation experiments compare the efficacy of the proposed test with two other commonly used tests, the forward recursive and the rolling window tests. The results indicate that both the rolling and the recursive rolling approaches offer good finite sample performance in situations where there are one or two changes in the causal relationship over the sample period. The testing strategies are illustrated in an empirical application that explores the causal impact of the slope of the yield curve on output and inflation in the U.S. over the period 1985-2013.
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#106Download full text
- JEL-Codes:
- C22; G00
- Keywords:
- Volatility; Order flow; News; Dynamic conditional score; forecasting
Public news flow in intraday component models for trading activity and volatility
Understanding the determinants of, and forecasting asset return volatility are crucial issues in many financial applications. Many earlier studies have considered the impact of trading activity and news arrivals on volatility. This paper develops a range of intraday component models for volatility and order flow that include the impact of news arrivals. Estimates of the conditional mean of order flow, taking into account news flow are included in models ofvolatility providing a superior in-sample fit. At a 1-minute frequency, it is found that first generating forecasts of order flow which are then included in forecasts of volatility leads to superior day-ahead forecasts of volatility. While including overnight news arrivals directly into models for volatility improves in-sample fit, this approach produces inferior forecasts.
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#105Download full text
- Keywords:
- VAR
A New Method for Working With Sign Restrictions in SVARs
Structural VARs are used to compute impulse responses to shocks. One problem that has arisen involves the information needed to perform this task i.e. how are the shocks to separated into those representing technology, monetary effects etc. Increasingly the signs of impulse responses are used for this task. However it is often desirable to impose some parametric assumption as well e.g. that monetary shocks have no long-run impact on output. Existing methods for combining sign and parametric restrictions are not well developed. In this paper we provide a relatively simple way to allow for these combinations and show how it works in a number of different contexts.
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#104Download full text
- JEL-Codes:
- C14; C53
- Keywords:
- Implied volatility, Hawkes process, Peaks over threshold, Point process, Extreme events
Point process models for extreme returns: Harnessing implied volatility
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 role of IV is considered within a range of models beginning with the traditional GARCH based approach. Furthermore, a number of novel point process models for forecasting extreme risk are proposed in this paper. Univariate models where IV is included as an exogenous variable are considered along with a novel bivariate approach where movements in IV are treated as another point process. It is found that in the context of forecasting Value-at-Risk, the bivariate models produce the most accurate forecasts across a wide range of scenarios.