Forecasting volatility of the u.s. oil market gold background


We examine the information content of the CBOE Crude Oil Volatility Index (OVX) when forecasting realized volatility in the WTI futures market dollar yen exchange rate. Additionally, we study whether other market variables, such as volume, open interest, daily returns, bid-ask spread and the slope of the futures curve, contains predictive power beyond what is embedded in the implied volatility. In out-of-sample forecasting we find that econometric models based on realized volatility can be improved by including implied volatility and other variables. Our results show that including implied volatility significantly improves daily and weekly volatility forecasts, while including other market variables significantly improves daily, weekly and monthly volatility forecasts fraction to whole number calculator. Forecasting Volatility Of The U.S. Oil Market – Introduction

Accurate volatility forecasts are crucial for portfolio optimization, options and derivatives pricing, value-at-risk modeling, and hedging. Forecasting volatility has traditionally been done using the generalized autoregressive conditional heteroscedasticity (GARCH) approach of Bollerslev (1986) and Engle (1982), also in energy commodity markets (see e.g. Marzo and Zagaglia (2010) and Wei et al. (2010)).

A breakthrough in volatility measuring was provided when Andersen and Bollerslev (1998) introduced realized volatility as the sum of squared intra-daily returns. This made volatility almost an observable variable which can be modeled straightforwardly with standard time-series techniques.

It has long been recognized that there are other sources of information about future volatility than realized volatility name in binary. A natural candidate is the market’s expectation of future volatility, commonly referred to as implied volatility (IV) gold price 2016. Some previous studies (e.g. Lamoureux and Lastrapes (1993); Jorion (1995); Agnolucci (2009)) argue that forecasts obtained from implied volatility are both biased and inefficient.

Evidence that IV improves volatility forecasts has also been presented (e.g. Day and Lewis (1993); Szakmary et al. (2003); Doran and Ronn (2005); Agnolucci (2009)). According to Jorion (1995), a failure to unearth IV’s predictive power can only be interpreted in two ways; inefficient information processing in options markets or misleading test procedures. In highly liquid and transparent markets such as the WTI futures market the former is unlikely canadian dollar to usd. Left is the latter, and in particular the discussion about the bias of the Black-Scholes (BS) formula (see e.g cool pictures of nature. Doran and Ronn (2005)). A way to avoid this possible problem (and several others) is to use a volatility index which is based on the market price of variance. Such an index was introduced for the WTI futures market in 2008 and is one of the main units of analysis in this paper. Volatility has also been linked to several other market variables. For instance, the relationship between volume and volatility is widely documented (e.g binary to hex. Clark (1973) and Gallant et al. (1992)) msn news usa. In addition to possibly improve volatility forecasts, including additional variables in the analysis can increase our understanding of the market.

Even though realized and implied volatility in equity markets has been extensively studied (see e.g. Bollerslev et al. (2013) and references within), much less work has been done in this field for commodity markets. This is particularly suprising for the oil market,considering the market’s economic importance (Sadorsky, 2006). Wang et al. (2008) studied the realized correlation between oil and gas markets and found the use of RV in energy markets to be highly appropriate, especially in areas such as volatility forecasting nzd usd live chart. Martens and Zein (2004) compared forecasts obtained from a long-memory model of RV with options-implied volatility for the WTI futures market. They found that both RV and IV contain useful information in volatility forecasting rs to us dollar. Little work has been done regarding the WTI IV index, due to its recent inception. An exception is Padungsaksawasdi and Daigler (2013) who studied the return-IV relation, and concluded that IV increases with negative returns.

In this paper we examine the role of both volatility implied from the OVX and observable market variables when forecasting volatility for the WTI futures market. We apply the simple HeterogenousAutoregRessive (HAR) model of Corsi (2009) on realized volatility itself. Additionally, two fundamentally different types of variables are used in the model; the forward looking IV index and other exogenous market variables including volume, open interest, daily returns and the slope of the futures curve. The main findings can be summarized as follows. First, we find that including information from the OVX significantly improves the day-ahead and weekahead volatility forecasts. Second, the exogenous market variables improve volatility forecasts for daily, weekly and monthly horizons. Of the additional explanatory market variables, the daily returns is the most important factor to improve volatility forecasts.