Short-Horizon Asymmetric Mean-Reversion and Overreactions: Evidence from the Nordic Stock Markets

This paper examines the asymmetric behavior of conditional mean and variance. Short-horizon mean-reversion behavior in mean is modeled with an asymmetric nonlinear autoregressive model, and the variance is modeled with an Exponential GARCH in Mean model. The results of the empirical investigation of the Nordic stock markets indicates that negative returns revert faster to positive returns when positive returns generally persist longer. Asymmetry in both mean and variance can be seen on all included markets and are fairly similar. Volatility rises following negative returns more than following positive returns which is an indication of overreactions. Negative returns lead to increased variance and positive returns leads even to decreased variance.

Confronto tra sistemi trend following e mean reversion

Si descrivono strategie di trading trend following e strategie mean reversion applicate a vari strumenti finanziari

Mean reversion in the short horizon returns of UK portfolios

This paper will show that short horizon stock returns for UK portfolios are more predictable than suggested by sample autocorrelation co-efficients. Four capitalisation based portfolios are constructed for the period 1976–1991. It is shown that the first order autocorrelation coefficient of monthly returns can explain no more than 10% of the variation in monthly portfolio returns. Monthly autocorrelation coefficients assume that each weekly return of the previous month contains the same amount of information. However, this will not be the case if short horizon returns contain predictable components which dissipate rapidly. In this case, the return of the most recent week would say a lot more about the future monthly portfolio return than other weeks. This suggests that when predicting future monthly portfolio returns more weight should be given to the most recent weeks of the previous month, because, the most recent weekly returns provide the most information about the subsequent months' performance. We construct a model which exploits the mean reverting characteristics of monthly portfolio returns. Using this model we forecast future monthly portfolio returns. When compared to forecasts that utilise the autocorrelation statistic the model which exploits the mean reverting characteristics of monthlyportfolio returns can forecast future returns better than the autocorrelation statistic, both in and out of sample.

The impact of taxes on optimal portfolio choice : an Australian study

Taxes are an important component of investing that is commonly overlooked in both the literature and in practice. For example, many understand that taxes will reduce an investment’s return, but less understood is the risk-sharing nature of taxes that also reduces the investment’s risk. This thesis examines how taxes affect the optimal asset allocation and asset location decision in an Australian environment. It advances the model of Horan & Al Zaman (2008), improving the method by which the present value of tax liabilities are calculated, by using an after-tax risk-free discount rate, and incorporating any new or reduced tax liabilities generated into its expected risk and return estimates. The asset allocation problem is examined for a range of different scenarios using Australian parameters, including different risk aversion levels, personal marginal tax rates, investment horizons, borrowing premiums, high or low inflation environments, and different starting cost bases. The findings support the Horan & Al Zaman (2008) conclusion that equities should be held in the taxable account. In fact, these findings are strengthened with most of the efficient frontier maximising equity holdings in the taxable account instead of only half. Furthermore, these findings transfer to the Australian case, where it is found that taxed Australian investors should always invest into equities first through the taxable account before investing in super. However, untaxed Australian investors should invest their equity first through superannuation. With borrowings allowed in the taxable account (no borrowing premium), Australian taxed investors should hold 100% of the superannuation account in the risk-free asset, while undertaking leverage in the taxable account to achieve the desired risk-return. Introducing a borrowing premium decreases the likelihood of holding 100% of super in the risk-free asset for taxable investors. The findings also suggest that the higher the marginal tax rate, the higher the borrowing premium in order to overcome this effect. Finally, as the investor’s marginal tax rate increases, the overall allocation to equities should increase due to the increased risk and return sharing caused by taxation, and in order to achieve the same risk/return level as the lower taxation level, the investor must take on more equity exposure. The investment horizon has a minimal impact on the optimal allocation decision in the absence of factors such as mean reversion and human capital.

What drives the commodity price beta of oil industry stocks

We test theoretical drivers of the oil price beta of oil industry stocks. The strongest statistical and economic support comes for market conditions-type variables as the prime drivers: namely, oil price (+), bond rate (+), volatility of oil returns (−) and cost of carry (+). Though statistically significant, exogenous firm characteristics and oil firms' financing decisions have less compelling economic significance. There is weaker support for the prediction that financial risk management reduces the exposure of oil stocks to crude oil price variation. Finally, extended modelling shows that mean reversion in oil prices also helps explain cross-sectional variation in the oil beta.

Modeling Nonlinearities and Asymmetries in Asset Pricing

Financial time series tend to behave in a manner that is not directly drawn from a normal distribution. Asymmetries and nonlinearities are usually seen and these characteristics need to be taken into account. To make forecasts and predictions of future return and risk is rather complicated. The existing models for predicting risk are of help to a certain degree, but the complexity in financial time series data makes it difficult. The introduction of nonlinearities and asymmetries for the purpose of better models and forecasts regarding both mean and variance is supported by the essays in this dissertation. Linear and nonlinear models are consequently introduced in this dissertation. The advantages of nonlinear models are that they can take into account asymmetries. Asymmetric patterns usually mean that large negative returns appear more often than positive returns of the same magnitude. This goes hand in hand with the fact that negative returns are associated with higher risk than in the case where positive returns of the same magnitude are observed. The reason why these models are of high importance lies in the ability to make the best possible estimations and predictions of future returns and for predicting risk.

Monte Carlo Valuation of natural gas investments

This paper deals with the valuation of energy assets related to natural gas. In particular, we evaluate a baseload Natural Gas Combined Cycle (NGCC) power plant and an ancillary instalation, namely a Liquefied Natural Gas (LNG) facility, in a realistic setting; specifically, these investments enjoy a long useful life but require some non-negligible time to build. Then we focus on the valuation of several investment options again in a realistic setting. These include the option to invest in the power plant when there is uncertainty concerning the initial outlay, or the option's time to maturity, or the cost of CO2 emission permits, or when there is a chance to double the plant size in the future. Our model comprises three sources of risk. We consider uncertain gas prices with regard to both the current level and the long-run equilibrium level; the current electricity price is also uncertain. They all are assumed to show mean reversion. The two-factor model for natural gas price is calibrated using data from NYMEX NG futures contracts. Also, we calibrate the one-factor model for electricity price using data from the Spanish wholesale electricity market, respectively. Then we use the estimated parameter values alongside actual physical parameters from a case study to value natural gas plants. Finally, the calibrated parameters are also used in a Monte Carlo simulation framework to evaluate several American-type options to invest in these energy assets. We accomplish this by following the least squares MC approach.

Valuation of wind energy projects: A real options approach

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Long-term equity anticipation securities and stock market volatility dynamics

Recent empirical findings suggest that the long-run dependence in U.S. stock market volatility is best described by a slowly mean-reverting fractionally integrated process. The present study complements this existing time-series-based evidence by comparing the risk-neutralized option pricing distributions from various ARCH-type formulations. Utilizing a panel data set consisting of newly created exchange traded long-term equity anticipation securities, or leaps, on the Standard and Poor's 500 stock market index with maturity times ranging up to three years, we find that the degree of mean reversion in the volatility process implicit in these prices is best described by a Fractionally Integrated EGARCH (FIEGARCH) model. © 1999 Elsevier Science S.A. All rights reserved.

Does final demand for energy in Portugal exhibit long memory

In this paper, we measure the degree of fractional integration in final energy demand in Portugal using an ARFIMA model with and without adjustments for seasonality. We consider aggregate energy demand as well as final demand for petroleum, electricity, coal, and natural gas. Our findings suggest the presence of long memory in all of the energy demand variables, that the series are stationary, although the mean reversion process will be slower than in the typical short run processes. These results have important implications for the design of energy policies. The effects of temporary policy shocks on final energy demand will tend to disappear slowly. This means that even transitory shocks have long lasting effects. Given the temporary nature of these effects, however, permanent effects require permanent policies. This is unlike what would be suggested by the more standard but much more limited unit root approach, which would incorrectly indicate that even transitory policies would have permanent effects.

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Leverage instability and persistence: Evidence from the United States and Europe

We find that leverage behavior both in level and time-series variation is very similar between the United States and Europe throughout the 1990-2013 period. Leverage regimes are simultaneously unstable and persistent for both regions. We define instability as the extent to which firms largely deviate from their long-term leverage mean, while persistence as the extent to which today’s leverage influences its future levels. We then show that this simultaneous evidence imply a mean-reversion behavior of leverage and discuss some of its implications for future research on this field.