The economic importance of rare earth elements volatility forecasts

We compare the suitability of short-memory models (ARMA), long-memory models (ARFIMA), and a GARCH model to describe the volatility of rare earth elements (REEs). We find strong support for the existence of long-memory effects. A simple long-memory ARFIMA (0, d, 0) baseline model shows generally superior accuracy both in- and out-of-sample, and is robust for various subsamples and estimation windows. Volatility forecasts produced by the baseline model also convey material forward-looking information for companies in the REEs industry. Thus, an active trading strategy based on REE volatility forecasts for these companies significantly outperforms a passive buy-and-hold strategy on both an absolute and a risk-adjusted return basis.     

Risk and return: Long-run relations,fractional cointegration,and return predictability

Univariate dependencies in market volatility, both objective and risk neutral, are best described by long-memory fractionally integrated processes. Meanwhile, the ex post difference, or the variance swap payoff reflecting the reward for bearing volatility risk, displays far less persistent dynamics. Using intraday data for the Standard & Poor's 500 and the volatility index (VIX), coupled with frequency domain methods, we separate the series into various components. We find that the coherence between volatility and the volatility-risk reward is the strongest at long-run frequencies. Our results are consistent with generalized long-run risk models and help explain why classical efforts of establishing a naïve return-volatility relation fail. We also estimate a fractionally cointegrated vector autoregression (CFVAR). The model-implied long-run equilibrium relation between the two variance variables results in nontrivial return predictability over interdaily and monthly horizons, supporting the idea that the cointegrating relation between the two variance measures proxies for the economic uncertainty rewarded by the market.     

Posterior analysis of mergers and acquisitions in the international financial market: A re-appraisal

Do mergers and acquisitions (M&A) improve the wealth status of investors, and if so, amidst persistence of volatility shocks? This paper tests these propositions by employing in the first step, a modified event study approach, and estimating a long-memory conditional volatility model, in the second step. The financial and policy implications of M&A are varied and contestable, yet, from an investor’s perspective, the long-term adjusted gain from M&A depends not only on the immediate growth of wealth, but also the fact that such a growth would accompany reduced rate of volatility persistence. Although in the beginning, a high persistence of volatility cannot be ruled out, its presence in the longer-run implies that the wealth gains from M&A are unstable, leading perhaps to a further collapse of both the merged/merger and acquired/acquiring firms. We estimate a long-memory Generalized Conditional Heteroscedasticity (GARCH) model with a Markovian transition for a number of international firms, specifically in Asia, to show in the first place, whether volatility shocks display differential memory in the pre- and post-M&A periods and whether the asymmetric high persistence is in the aftermath of M&A. Our results point at a significant ‘non-zero’ and positive gain for investors following M&A, but this is combined with greater volatility persistence.     

Forecasting daily variability of the S&P 100 stock index using historical,realised and implied volatility measurements

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved volatility measurements but has also inspired research into their potential value as an information source for volatility forecasting. In this paper, we explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day). First, we consider unobserved components (UC-RV) and long memory models for realised volatility which is regarded as an accurate estimator of volatility. The predictive abilities of realised volatility models are compared with those of stochastic volatility (SV) models and generalised autoregressive conditional heteroskedasticity (GARCH) models for daily return series. These historical volatility models are extended to include realised and implied volatility measures as explanatory variables for volatility. The main focus is on forecasting the daily variability of the Standard & Poor's 100 (S&P 100) stock index series for which trading data (tick by tick) of almost 7 years is analysed. The forecast assessment is based on the hypothesis of whether a forecast model is outperformed by alternative models. In particular, we will use superior predictive ability tests to investigate the relative forecast performances of some models. Since volatilities are not observed, realised volatility is taken as a proxy for actual volatility and is used for computing the forecast error. A stationary bootstrap procedure is required for computing the test statistic and its p-value. The empirical results show convincingly that realised volatility models produce far more accurate volatility forecasts compared to models based on daily returns. Long memory models seem to provide the most accurate forecasts.     

Forecasting Intraday Volatility and Value-at-Risk with High-Frequency Data

In this paper, we develop modeling tools to forecast Value-at-Risk and volatility with investment horizons of less than one day. We quantify the market risk based on the study at a 30-min time horizon using modified GARCH models. The evaluation of intraday market risk can be useful to market participants (day traders and market makers) involved in frequent trading. As expected, the volatility features a significant intraday seasonality, which motivates us to include the intraday seasonal indexes in the GARCH models. We also incorporate realized variance (RV) and time-varying degrees of freedom in the GARCH models to capture more intraday information on the volatile market. The intrinsic tail risk index is introduced to assist with understanding the inherent risk level in each trading time interval. The proposed models are evaluated based on their forecasting performance of one-period-ahead volatility and Intraday Value-at-Risk (IVaR) with application to the 30 constituent stocks. We find that models with seasonal indexes generally outperform those without; RV can improve the out-of-sample forecasts of IVaR; student GARCH models with time-varying degrees of freedom perform best at 0.5 and 1 % IVaR, while normal GARCH models excel for 2.5 and 5 % IVaR. The results show that RV and seasonal indexes are useful to forecasting intraday volatility and Intraday VaR.     

Modeling and forecasting the additive bias corrected extreme value volatility estimator

In this paper, we provide a framework to model and forecast daily volatility based on the newly proposed additive bias corrected extreme value volatility estimator (the Add RS estimator). The theoretical framework of the additive bias corrected extreme value volatility estimator is based on the closed form solution for the joint probability of the running maximum and the terminal value of the random walk. Using the opening, high, low and closing prices of S&P 500, CAC 40, IBOVESPA and S&P CNX Nifty indices, we find that the logarithm of the Add RS estimator is approximately Gaussian and that a simple linear Gaussian long memory model can be applied to forecast the logarithm of the Add RS estimator. The forecast evaluation analysis indicates that the conditional Add RS estimator provides better forecasts of realized volatility than alternative range-based and return-based models.     

Estimation and pricing under long-memory stochastic volatility

We treat the problem of option pricing under a stochastic volatility model that exhibits long-range dependence. We model the price process as a Geometric Brownian Motion with volatility evolving as a fractional Ornstein–Uhlenbeck process. We assume that the model has long-memory, thus the memory parameter H in the volatility is greater than 0.5. Although the price process evolves in continuous time, the reality is that observations can only be collected in discrete time. Using historical stock price information we adapt an interacting particle stochastic filtering algorithm to estimate the stochastic volatility empirical distribution. In order to deal with the pricing problem we construct a multinomial recombining tree using sampled values of the volatility from the stochastic volatility empirical measure. Moreover, we describe how to estimate the parameters of our model, including the long-memory parameter of the fractional Brownian motion that drives the volatility process using an implied method. Finally, we compute option prices on the S&P 500 index and we compare our estimated prices with the market option prices.     

Financial Leverage,CEO Compensation,and Corporate Hedging: Evidence from Real Estate Investment Trusts

This paper studies the determinants of corporate hedging practices in the REIT industry between 1999 and 2001. We find a positive significant relation between hedging and financial leverage, indicating the financial distress costs motive for using derivatives in the REIT industry. Using estimates of the Black–Scholes sensitivity of CEO’s stock option portfolios to stock return volatility and the sensitivity of CEO’s stock and stock option portfolios to stock price, we find evidence to support managerial risk aversion motive for corporate hedging in the REIT industry. Our results indicate that CEO’s cash compensation and the CEO’s wealth sensitivity to stock return volatility are significant determinants of derivative use in REITs. We also document a significant positive relation between institutional ownership and hedging activity. Further, we find that probability of hedging is related to economies of scale in hedging costs.

Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models

The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naïve models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.     

Volatility forecasts and at-the-money implied volatility: a multi-component ARCH approach and its relation to market models

This article explores the relationships between several forecasts for the volatility built from multi-scale linear ARCH processes, and linear market models for the forward variance. This shows that the structures of the forecast equations are identical, but with different dependencies on the forecast horizon. The process equations for the forward variance are induced by the process equations for an ARCH model, but postulated in a market model. In the ARCH case, they are different from the usual diffusive type. The conceptual differences between both approaches and their implication for volatility forecasts are analysed. The volatility forecast is compared with the realized volatility (the volatility that will occur between date t and t + ΔT), and the implied volatility (corresponding to an at-the-money option with expiry at t + ΔT). For the ARCH forecasts, the parameters are set a priori. An empirical analysis across multiple time horizons ΔT shows that a forecast provided by an I-GARCH(1) process (one time scale) does not capture correctly the dynamics of the realized volatility. An I-GARCH(2) process (two time scales, similar to GARCH(1,1)) is better, while a long-memory LM-ARCH process (multiple time scales) replicates correctly the dynamics of the implied and realized volatilities and delivers consistently good forecasts for the realized volatility.     

Long-term adjusted volatility: Powerful capability in forecasting stock market returns

We develop the long-term adjusted volatility (LV_ADJ) by removing the interference information of short-term volatility from the simple long-term volatility and examine the role of LV_ADJ in the predictability of stock market returns. Using a sample from January 2000 to December 2019 and considering 19 popular predictors, LV_ADJ positively predicts the next-month returns of S&P 500 and the univariate model with LV_ADJ has the best forecasting performance with adjusted in-sample r-squared of 3.825%, out-of-sample r-squared of 3.356%, return gains of 5.976%, CER gains of 4.708 and Sharpe ratio gains of 0.394. The predictive information of LV_ADJ is independent of that obtained from the 19 popular predictors. Furthermore, we find that LV_ADJ also has predictive power for long-term (3–12 months) stock returns, and can forecast returns of industry portfolios and characteristic portfolios.     

Forecasting and trading high frequency volatility on large indices

The present paper analyses the forecastability and tradability of volatility on the large S&P500 index and the liquid SPY ETF, VIX index and VXX ETN. Even though there is already a huge array of literature on forecasting high frequency volatility, most publications only evaluate the forecast in terms of statistical errors. In practice, this kind of analysis is only a minor indication of the actual economic significance of the forecast that has been developed. For this reason, in our approach, we also include a test of our forecast through trading an appropriate volatility derivative. As a method we use parametric and artificial intelligence models. We also combine these models in order to achieve a hybrid forecast. We report that the results of all three model types are of similar quality. However, we observe that artificial intelligence models are able to achieve these results with a shorter input time frame and the errors are uniformly lower comparing with the parametric one. Similarly, the chosen models do not appear to differ much while the analysis of trading efficiency is performed. Finally, we notice that Sharpe ratios tend to improve for longer forecast horizons.     

On the prediction of financial distress in developed and emerging markets: Does the choice of accounting and market information matter? A comparison of UK and Indian Firms

We examine differences in price delay for a sample of real estate investment trust (REIT) and non-REIT matched pairs. Results suggest an economically and statistically higher level of price delay for REIT securities, which implies heightened frictions that increase the time needed for new information to be impounded into the prices of REIT shares. The primary drivers for the observed delay differential include differences in idiosyncratic volatility, market risk, and the number of days traded. Within-REIT determinants of delay confirm findings for the pooled sample of matched pairs. Importantly, we infer find that REIT investors are not compensated for restricted information flow, as excess returns are unrelated to the price delay.     

The Relative Importance of Stock, Bond and Real Estate Factors in Explaining REIT Returns

This paper examines the link between REIT, financial asset and real estate returns, and tests whether it changed subsequent to the “REIT boom” of the early 1990s. The main focus is on answering the question do REIT returns now better reflect the performance of underlying direct (unsecuritized) real estate? We develop and implement a variance decomposition for REIT returns that separates REIT return variability into components directly related to major stock, bond, and real estate-related return indices, as well as idiosyncratic or sector-specific effects. This is applied to aggregate REIT sector (NAREIT) returns as well as returns to size and property-type based REIT portfolios. Our results show that the REIT market went from being driven largely by the same economic factors that drive large cap stocks through the 1970s and 1980s to being more strongly related to both small cap stock and real estate-related factors in the 1990s. There is also a steady increase over time in the proportion of volatility not accounted for by stock, bond or real estate related factors. We also find that small cap REITs are “more like real estate” compared to larger cap REITs, at least over the 1993–1998 period. We argue that this could be a result of the institutionalization of the ownership of larger cap REITs that took place in the 1990s.     

Dual long-memory, structural breaks and the link between turnover and the range-based volatility

This paper investigates the issue of temporal ordering of the range-based volatility and turnover volume in the Korean market for the period 1995–2005. We examine the dynamics of the two variables and their respective uncertainties using a bivariate dual long-memory model. We distinguish volume trading before the Asia financial crisis from trading after the crisis. We find that the apparent long-memory in the variables is quite resistant to the presence of breaks. However, when we take into account structural breaks the order of integration of the conditional variance series decreases considerably. Moreover, the impact of foreign volume on volatility is negative in the pre-crisis period but turns to positive after the crisis. This result is consistent with the view that foreign purchases tend to lower volatility in emerging markets—especially in the first few years after market liberalization when foreigners are buying into local markets—whereas foreign sales increase volatility. Before the crisis there is no causal effect for domestic volume on volatility whereas in the post-crisis period total and domestic volumes affect volatility positively. The former result is in line with the theoretical underpinnings that predict that trading within domestic investor groups does not affect volatility. The latter result is consistent with the theoretical argument that the positive relation between the two variables is driven by the uninformed general public.     

Can Linear Predictability Models Time Bull and Bear Real Estate Markets? Out-of-Sample Evidence from REIT Portfolios

A recent literature has shown that REIT returns contain strong evidence of bull and bear dynamic regimes that may be best captured using nonlinear econometric models of the Markov switching type. In fact, REIT returns would display regime shifts that are more abrupt and persistent than in the case of other asset classes. In this paper we ask whether and how simple linear predictability models of the vector autoregressive (VAR) type may be extended to capture the bull and bear patterns typical of many asset classes, including REITs. We find that nonlinearities are so deep that it is impossibile for a large family of VAR models to either produce similar portfolio weights or to yield realized, ex-post out-of-sample long-horizon portfolio performances that may compete with those typical of bull and bear models. A typical investor with intermediate risk aversion and a 5-year horizon ought to be ready to pay an annual fee of up to 5.7 % to have access to forecasts of REIT returns that take their bull and bear dynamics into account instead of simpler, linear forecast.     

Asymmetric Correlation and Volatility Dynamics among Stock, Bond, and Securitized Real Estate Markets

We apply a multivariate asymmetric generalized dynamic conditional correlation GARCH model to daily index returns of S&P500, US corporate bonds, and their real estate counterparts (REITs and CMBS) from 1999 to 2008. We document, for the first time, evidence for asymmetric volatilities and correlations in CMBS and REITs. Due to their high levels of leverage, REIT returns exhibit stronger asymmetric volatilities. Also, both REIT and stock returns show strong evidence of asymmetries in their conditional correlation, suggesting reduced hedging potential of REITs against the stock market downturn during the sample period. There is also evidence that corporate bonds and CMBS may provide diversification benefits for stocks and REITs. Furthermore, we demonstrate that default spread and stock market volatility play a significant role in driving dynamics of these conditional correlations and that there is a significant structural break in the correlations caused by the recent financial crisis.     

Dedicated REIT Mutual Fund Flows and REIT Performance

This study examines the effects of weekly and monthly capital flows into the dedicated REIT mutual fund sector on aggregate REIT returns and, simultaneously, the effects of industry-level REIT returns on subsequent REIT mutual fund flows. The dynamic relation between REIT capital flows and returns is estimated using vector autoregression (VAR) techniques. Unlike static regression techniques, our dynamic model produces estimates of the short-run relationships, long-run relationships, impulse response functions, and forecast variance decompositions. We find evidence that REIT mutual fund flows are positively and significantly related to prior returns, while prior REIT mutual fund flows do not significantly influence REIT returns. However, contemporaneous flows do appear to have an initial positive effect, which is partially reversed one period later. The positive contemporaneous effect, however, is the result of unexpected REIT mutual fund flows, while the expected portion is insignificant.     

New evidence on the implied-realized volatility relation

We consider the relation between the volatility implied in an option's price and the subsequently realized volatility. Earlier studies on stock index options have found biases and inefficiencies in implied volatility as a forecast of future volatility. More recently, Christensen and Prabhala find that implied volatility in at-the-money one-month OEX call options on the S&P 100 index in fact is an unbiased and efficient forecast of ex-post realized index volatility after the 1987 stock market crash. In this paper, the robustness of the unbiasedness and efficiency result is extended to a more recent period covering April 1993 to February 1997. As a new contribution, implied volatility is constructed as a trade weighted average of implied volatilities from both in-the-money and out-of-the-money options and both puts and calls. We run a horse race between implied call, implied put, and historical return volatility. Several robustness checks, including a new simultaneous equation approach, underscore our conclusion, that implied volatility is an efficient forecast of realized return volatility.     

The role of jumps and leverage in forecasting volatility in international equity markets

We analyze the importance of jumps and the leverage effect on forecasts of realized volatility in a large cross-section of 18 international equity markets, using daily realized measures data from the Oxford-Man Realized Library, and two widely employed empirical models for realized volatility that allow for jumps and leverage. Our out-of-sample forecast evaluation results show that the separation of realized volatility into a continuous and a discontinuous (jump) component is important for the S&P 500, but of rather limited value for the remaining 17 international equity markets that we analyze. Only for 6 equity markets are significant and sizable forecast improvements realized at the one-step-ahead horizon, which, nevertheless, deteriorate quickly and abruptly as the prediction horizon increases. The inclusion of the leverage effect, on the other hand, has a much larger impact on all 18 international equity markets. Forecast gains are not only highly significant, but also sizeable, with gains remaining significant for forecast horizons of up to one month ahead.