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We analyze an unbalanced panel monthly predictions of nonfarm payroll (NFP) changes between January 2008 and December 2020 sourced from Bloomberg. Unsurprisingly, we find that prediction quality varies across economists and we reject the hypothesis of equal predictive ability. In an error decomposition, we find evidence of significantly biased forecasts. Participation rate in the survey is affecting this bias. We find that survey participants under-predict job losses in times of market turmoil while also under-predicting the recovery thereafter, especially during the COVID19 labor shock. For prediction of NFP changes, autoregressive models are outperformed by a deep learning long short-term memory network. However, the consensus forecast yields better forecasts than model-based approaches and are further improved by combining the forecasts of the best performing economists. The COVID19 labor shock is shown to have adverse effects on the prediction performance of economists. However, not all economists are affected equally.

We use high-frequency data to examine the effects of introducing a night trading session at the Shanghai Futures Exchange (SHFE) in 2013. For Copper, the realized volatility of the regular session is endogenously determined, while the night session is driven by the immediately preceding volatility of the London Metal Exchange (LME). In contrast, Chinese Aluminum futures are more resistant to exogenous factors and show pronounced long memory. We find no indications that the SHFE draws volume from LME. The existing break between daytime and night session has significant informational content and must be separated when processing intraday data.

This paper thoroughly integrates speculative bubbles to corporate finance literature by focusing on dividend policy issues. More specifically, we examine the importance of dividend policy when testing for speculative bubbles in the S&P 500 equity index on a data set spanning 1871 to 2014. Given the phenomenon of dividend smoothing, in particular in the U.S., we question the usefulness of observed dividend payments as fundamental factor in testing for bubbles. Circumventing dividend smoothing, we construct hypothetical dividend payouts which are based on reported corporate earnings instead. The empirical evidence presented here indeed suggests that the dividend policy of firms affects testing for speculative bubbles. While the dot.com-bubble—commonly seen as the prime example for a stock price bubble not only in the NASDAQ but also in other, broader equity indices—is detected with the observed dividend series as fundamental factor, this is not necessarily the case with our adjusted dividend time series. Some of our results argue against a speculative price bubble in the broader U.S. stock market in the late 1990s.

We introduce Infinite Hidden Markov (IHM) models to forecasting realized volatilities of crude oil futures markets with exogenous factors. With these IHM models, we lift the restriction of a pre-defined number of regimes and allow for an unknown number of different parameter regimes and breakpoints. We employ two types of infinite hidden Markov models to accommodate structural breaks incurred by policy changes, exogenous shocks, and other factors. We find that IHM-HAR models outperform all other non-switching variants. In regard to forecasting performance, IHM-HAR models with exogenous factors such as realized volatilities of competing futures markets and the S&P500 are superior model choices for short-term forecasts. For longer-term forecasts, the equity channel shows only little positive impact. Evidence of economic gains in portfolio construction based on IHM-HAR forecasts is provided.

This note explores a secondary effect of the GameStop short squeeze event and links the exalted focus of retail investors on meme stocks to financial literacy and autodidacticism. From an overview of stylized facts about the short squeeze of GameStop based on high frequency data, short interest, and key figures of related derivatives, it is shown that these financial concepts are reflected in keyword searches across multiple platforms. This self-education with regard to financial terms, keywords, and products and the understanding of basic market speculation mechanisms such as short sales plays a significant role for the influx of retail investors.

This article examines the interconnectedness of WTI and Brent prices on different resolutions of price movements. Firstly, within a multivariate BEKK framework we identify high but volatile correlations with recurring highs around 0.8 and multiple periods of decoupling. OPEC meetings increase the correlation in the short run. Secondly, linear l1-trends reveal that long-term movements of WTI and Brent are driven by the same dynamics, confirming the ‘one great pool’ hypothesis. OPEC meetings have only little impact on long-term price trends. Thirdly, we find leading effects of WTI over Brent by short-term trends of several days, especially in a negative direction. These trends have an asymmetrical effect on volatility; negative trends cause a stronger increase than positive trends. These findings are of interest to policy makers as well as hedging strategies of crude oil portfolios and provide insight into long-term movements of crude prices.

We examine the long-term impact of oil prices, price uncertainty, and local and global geopolitical risks on Gulf Cooperation Council (GCC) stock markets from May 2007 to August 2018. We apply the ARDL methodology to all member states and control for other sources of risk such as implied volatility of equity and oil markets. This framework captures dependencies and connectedness’ across several time horizons, quantifying the impact of geopolitical risk on GCC countries. While all markets are highly sensitive to changes in energy prices and volatility in the long run, only the stock market of Qatar is negatively affected by global geopolitical risk. All other member states are resilient on a global scale. The negative effects of local geopolitical events—measured as the Saudi Arabian geopolitical risk index—are much more pronounced for GCC members and highly significant. The results show that each GCC country responds differently to shocks in geopolitical uncertainty. Surprisingly, Saudi Arabia as the economic and political heavyweight of the GCC shows no long-term reaction to its local geopolitical risks. Our findings are relevant in terms of risk transmission and hedging for market participants as well as energy market supply and security for policy makers.

A flexible modification of the DCC model that accounts for asymmetry and long memory in variance is proposed. This model is applied on precious metals and indexes of developed countries to revisit the flight-to-quality phenomenon. Market turmoil and shocks are covered by asset-specific variance models. I identify Gold and partly Silver as safe haven while this status seems to be dissipating in the recent years. Interestingly, Platinum shows signs of a surrogate safe haven. The practical difference between the standard DCC and the model proposed herein is significant, which stems from a more realistic variance modeling within the framework.

We transfer the recently introduced fast fractional differencing that utilizes fast Fourier transforms (FFT) to long memory variance models and show that this approach offers immense computation speedups. We demonstrate how calculation times of parameter estimations benefit from this new approach without changing the estimation procedure. A more precise depiction of long memory behavior becomes feasible. The FFT offers a computational advantage to all ARCH(∞)-representations of widely-used long memory models like FIGARCH. Risk management applications like rolling-window Value-at-Risk predictions are substantially sped up. This new approach allows to calculate the conditional volatility of high-frequency in a practicable amount of time.

We examine the Croatian Kuna, the Czech Koruna, the Hungarian Forint, the Polish Złoty, the Romanian Leu, and the Swedish Krona whether their Euro exchange rates volatility exhibits true or spurious long memory. Recent research reveals long memory in foreign exchange rate volatility and we confirm this finding for these currency pairs by examining the long memory behavior of squared residuals by means of the V/S test. However, by using the ICSS approach we also find structural breaks in the unconditional variance. Literature suggests that structural breaks might lead to spurious long memory behavior. In a refined test strategy, we distinguish true from spurious long memory for the six exchange rates. Our findings suggest that Czech Koruna and Hungarian Forint only feature spurious long memory, while the rest of the series have both structural breaks and true long memory. Lastly, we demonstrate how to extend existing models to jointly model both properties yielding superior fit and better Value-at-Risk forecasts. The results of our work help to avoid misspecification and provide a better understanding of the properties of the foreign exchange rate volatility.

We augment the HAR model with additional information channels to forecast realized volatility of WTI futures prices. These channels include stock markets, sentiment indices, commodity and FX markets, and text-based Google indices. We then apply four differing machine learning techniques to identify the most suitable endo- and exogenous factors which improve baseline model forecasts. We show that machine learning generated forecasts provide better forecasting quality and that portfolios which are constructed with these forecasts outperform their competing models. We find LASSO and SSVS to provide outperforming forecasts and portfolio weights. Analyzing the selection process, we show that variable choices vary across forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to the significance of information channels.

We construct a set of HAR models with three types of infinite Hidden Markov regime-switching structures. In particular, jumps, leverage effects, and speculation effects are all taken into account in the realized volatility modeling. We forecast five agricultural commodity futures (Corn, Cotton, Indica rice, Palm oil and Soybeans) based on high-frequency data from Chinese futures markets, and evaluate the forecast performances using both statistical and economic evaluation measures. The statistical evaluation results suggest that HAR models with infinite Hidden Markov regime-switching structures have better precision than the benchmark HAR models based on the MZ- R 2 , MAFE, and MCS results. The economic evaluation results suggest that portfolios constructed with infinite Hidden Markov regime-switching HARs also achieve higher portfolio returns for risk-averse investors than benchmark HAR model for short-term volatility forecasts.

Cryptocurrencies such as Bitcoin are establishing themselves as an investment asset and are often named the New Gold. This study, however, shows that the two assets could barely be more different. Firstly, we analyze and compare conditional variance properties of Bitcoin and Gold as well as other assets and find differences in their structure. Secondly, we implement a BEKK-GARCH model to estimate time-varying conditional correlations. Gold plays an important role in financial markets with flight-to-quality in times of market distress. Our results show that Bitcoin behaves as the exact opposite and it positively correlates with downward markets. Lastly, we analyze the properties of Bitcoin as portfolio component and find no evidence for stable hedging capabilities. We conclude that Bitcoin and Gold feature fundamentally different properties as assets and linkages to equity markets. Our results hold for the broad cryptocurrency index CRIX. As of now, Bitcoin does not reflect any distinctive properties of Gold other than asymmetric response in variance.

This study compares the performance of several methods to calculate the Value-at-Risk of the six main ASEAN stock markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance is analyzed by various backtesting procedures. We find that simpler models fail to produce sufficient Value-at-Risk forecasts, whichappears to stem from several econometric properties of the return distributions. With stochastic volatility models, we obtain better Value-at-Risk forecasts compared to GARCH. The quality varies over forecasting horizons and across markets. This indicates that, despite a regional proximity and homogeneity of the markets, index volatilities are driven by different factors.

We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We find that the Global Real Economic Activity outperforms all other economic and financial drivers under investigation. We also show that the Global Real Economic Activity provides superior volatility predictions for both, bull and bear markets. In addition, the average forecast combination results in low loss functions. This indicates that the information content of exogenous factors is time-varying and the model averaging approach diversifies the impact of single drivers.

We use high-frequency data to examine the effects of introducing an additional night trading session of four hours at the Shanghai Futures Exchange for Copper and Aluminum futures in December 2013. This additional trading session is shown to cause a structural break in the intraday behavior of prices. For Copper, the realized volatility of the regular session is endogenously determined while the night session is strongly driven by the immediately preceding realized volatility of the LME. In contrast, there is only little evidence for a directional spillover from the LME to SHFE for Aluminum futures. We find no indications that the SHFE is pulling volume from LME with the additional trading at night. Last, the now existing break between the day-time session and the night trading session is found to have significant informational content for Copper and Aluminum volatility and needs to be treated separately when extracting jump components from realized volatility.

This paper contributes to the large debate regarding the impact of oil price changes on U.S. GDP growth. Firstly, it replicates empirical findings of prominent studies and finds that the proposed oil price measures have a dissipating effect with recent data up to 2016Q4. Secondly, it re-examines the issue and provides evidence that oil price decreases affect the GDP growth, when taking into consideration mixed data sampling technique. Finally, it puts particular focus on nonlinearity and a possible instability and shows that combining Markov switching and mixed data sampling models allows to identify different regimes permanently changing with the Great Moderation.

This paper investigates the dynamics of the co-movement of GCC stock market returns with global oil market uncertainty, using an ARMA-DCC-EGARCH and time varying Student-t copula models. Empirical results demonstrate that oil uncertainty has significant and time varying impacts on the GCC stock returns. The GCC stock returns are found to be negatively affected by oil market uncertainty for almost the entire period under examination. More interestingly, we find that the impact of oil price uncertainty differs across GCC member states and allows for grouping. The results also show that the stock markets of Oman and Bahrain are relatively less sensitive to the oil uncertainty factor, thus offering investors and portfolio managers different investment options and portfolio diversification opportunities across GCC members.

We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We find that the Global Real Economic Activity outperforms all other economic and financial drivers under investigation. We also show that the Global Real Economic Activity provides superior volatility predictions for both, bull and bear markets. In addition, the average forecast combination results in low loss functions. This indicates that the information content of exogenous factors is time-varying and the model averaging approach diversifies the impact of single drivers.

We expand the literature of volatility and Value-at-Risk forecasting of oil price returns by comparing the recently proposed Mixture Memory GARCH (MMGARCH) model to other discrete volatility models (GARCH, RiskMetrics, EGARCH, APARCH, FIGARCH, HYGARCH, and FIAPARCH). We incorporate an Expectation-Maximization algorithm for parameter estimation of the MMGARCH and find different structures in volatility level as well as shock persistence. MMGARCH is also able to cover asymmetric and long memory effects. Furthermore, a dissimilar memory structure in variance of WTI and Brent crude oil prices is observed which is supported by additional tests. Parameter estimation and comparison of the models reveal significant long memory and asymmetry in oil price returns. In regard of variance forecasting and Value-at-Risk prediction, it is shown that MMGARCH outperforms the aforementioned models due to its dynamic approach in varying the volatility level and memory of the process. We find MMGARCH superior for application in risk management as a result of its flexibility in adjusting to variance shifts and shocks.

The conditional variance of returns of Gold, Silver, Palladium, and Platinum is modeled incorporating GARCH, APARCH, FIGARCH, and FIAPARCH for different underlying distributions. These returns are found to be non-normal. The results suggest that Gold and Silver feature different econometric properties in their dynamics than Palladium and Platinum. While Gold and Silver are dominated by asymmetric effects, Palladium and Platinum feature a preponderant long memory. This behavior is verified by taking into account recent shocks like the Volkswagen Diesel scandal or the 'Brexit' poll. The forecasting comparison of these models for one, five, and 20 day-ahead forecasts is compared with two different proxies for the daily realized variance -- squared daily returns and a proxy based on intra-day data. It is found that incorporating a more realistic measure for the realized volatility, the loss functions are significantly reduced and the decision on the best performing model changes. For Gold and Silver, APARCH and partly FIAPARCH are found to be the best performing.

In econometrics, long memory models for variance modeling like FIGARCH or FIAPARCH are characterized by a Fractional Differencing term. In order to estimate and apply these models, the infinite MacLaurin expansion of the differencing term has to be truncated at a certain level. We transfer the recently introduced fast fractional differencing that utilizes fast Fourier transforms (FFT) to long memory conditional variance models and show that this FFT approach offers immense speed-ups. This allows to further increase the truncation lag while ensuring a feasible computation time. We demonstrate how calculation times of parameter estimations of these models benefit from this new approach, relative to sample length and truncation lag. In this simulation study, allowing for higher truncation lags implies better and more precise results in parameter estimations. In order to emphasize the importance for practitioners and research in risk management, we carry out different time consuming rolling-window analyses for WTI and Brent crude oil returns and show that total computation times can be reduced by a factor 20 to 30 for FIGARCH. The speed-ups for FIAPARCH are found to be significantly higher. The FFT approach offers a computational advantage to all ARCH(infinity)-representations of widely-used long memory models like FIGARCH, FIAPARCH, HYGARCH, and FIEGARCH, especially for large data sets which are common in high frequency analyses.

This paper discusses how contingent convertible bonds (CCB) influence the risktaking behaviour of managers. A methodology to measure the impact is presented. The results show that the decision of issuing CCB to finance company assets sets incentives to managers to increase risk, if it is not adjusted to the compensation system. However, if the remuneration of managers is adjusted simultaneously with the issuance, e.g. with internal debt, the drawbacks of the sole compensation with stock options can be equalised. Furthermore, it was found that CCB does have an impact on the risk-taking behaviour, while CCB does not change the incentive to increase the company value at all, Se analiza cómo los bonos convertibles contingentes (CCB) influyen las conductas deriesgo de los directivos. Se presenta una metodología para medir el impacto. Los resultados muestran que la decisión de emitir los CCB para financiar la empresa incentiva a los directivos a conductas más arriesgadas, si no se ajusta el sistema de compensación. Sin embargo, si la remuneracíon de los directivos se ajusta simultáneamente con la emisión, p.ej. con deuda interna, los inconvenientes de una única compensación con opciones se compensarían. Además, hemos encontrado que los CCB no cambian los incentivos para aumentar el valor de la empresa en general.

T he mineral opal, chemically a form of hydrated silica, is found on practically all continents, but mostly as an “opalescent”, milky–white, soft rock. However, in some parts of Australia small pieces of beautifully coloured gemstones, “precious opal”, are to be found embedded in a matrix of ordinary opal. What makes these quintessentially Australian gemstones sparkle with flecks of pure spectral colour (Fig.1)? Oddly enough, the answer to this question was a mystery to mineralogists for a long time until noted CSIRO electron microscopist John Sanders discovered the surprising answer as recently as the 1970s [1],[2]. Because of the spectral colours exhibited, the phenomenon of diffraction from periodic features was suspected to be the cause, but nobody could guess at the nature of such periodic structures until they were revealed by electron microscopy. It was surmised that the optical properties of precious opal, as distinct from the milky-white appearance of common opal that shows no ‘fire’, depends on the existence of orderly, regular arrays of optical discontinuities, spaced at repeat distances of the order of 150 to 350 nanometers, i.e., distances that correspond to half the wavelength of visible light. Chemically, opals are made of pure, transparent, hydrated silica, i.e., hydrated Silicon Dioxide. But what the electron microscope revealed was that the silica is in the form of tiny spheres, of the appropriate range of sizes, stacked in close-packed regular arrays, as may be seen in Fig. 2, just like atoms or molecules in crystalline substances. How are these little spherical objects formed is answered by noting that the solubility of silica in water increases markedly with temperature so that, upon cooling, silica is usually deposited as quartz crystals that are said to grow in what is called a hydrothermal process. Alternatively, in the presence of centres of nucleation, the silica can precipitate from saturated solutions in the form of amorphous clusters. These continue to grow as concentric spheres, which then fall through the solution and end up in interstitial cavities. In most cases, a poly-disperse range of sizes results, which when dried out results in a milky-white solid of ordinary, or so-called ‘potch’ opal. However, in rare cases, where the little spheres have a greater distance through which to fall, a gravitational separation can take place, where the larger spheres fall more quickly than the smaller ones and arrange themselves in layers upon layers of uniformly sized regions of hexagonal close-packed groups, like oranges in a crate. Hence the quasi-crystalline arrangement of precious opal, usually in small pieces consisting of separate small regions, is analogous to crystal grains. The opal ‘grains’ can vary in size, from a few millimeters, known as pin-fire opals, up to quite large ones in what is known as boulder opals. Because the individual silica spheres are completely transparent, pieces of opal show practically no colour when viewed in transmission. However, when viewed in reflection, strong diffraction colours are seen. These diffraction phenomena are not like those from a two-dimensional grating, such as is seen from the surface of a CD or DVD, and erroneously shown in illustrations in some popular articles. On the contrary, 3-dimensional diffraction

A bout 15 years ago, I learned from a gemologist friend that the value of pearls is appraised by evaluating the following factors: Size; shape; colour and lustre (a term used for the quality of shinyness). The first three are easily quantified, but what about lustre? I decided to look into the problem as an intriguing piece of applied optics. Pearlescence? Iridescence? What optical phenomenon is responsible for the unique appearance of nacre (i.e. motherof-pearl)? The appearance of abalone shell, especially New Zealand Paua shell, gives a clue: Interference colours. So it must be something to do with the structure, maybe it is due to something like thin film interference? This is confirmed by scanning electron microscopysee Fig.1 – and explained by marine biologists as follows: Nacre consists of thin, tabular crystals of aragonite, which is Calcium Carbonate that the mollusk concentrates from seawater. But that is not all: aragonite normally forms needle-shaped crystals. However in seashells, it is constrained into tabular, platelet-shaped crystals by means of a protein secreted by the animal. This protein has dangling bonds with a spacing that is comparable with that of calcium carbonate molecules arranged in flat layers. So the biology of the situation constrains a change in crystal ‘architecture’ – from acicular, i.e. needle-shaped, to tabular. This, incidentally, gives the resulting composite structure quite remarkable mechanical strength, apart from the interesting optical properties. That seems to explain it, I thought: In abalone shells, the thickness of the “bricks” must be very uniform so that thin-film interference gives rise to iridescent colours. But what about ordinary nacre (mother-of-pearl) and indeed pearls themselves? My first thoughts were that maybe the layers are too thick or maybe of non-uniform thickness in ordinary nacre. I wasted a lot of time trying to calculate the reflection properties of thin films of slightly variable (i.e. poly-disperse) thickness. This turned out to be an interesting exercise, rather more difficult than I thought. I even enlisted the expert help of Professor John Lekner from Victoria University, New Zealand, who spent a brief Sabbatical with us in Melbourne and is a noted specialist in reflection phenomena, random matrices and so forth. It turned out to be a wild goose chase and eventually the real answer dawned on me and turned out to be much simpler than I had thought. Before coming to the real explanation, I must remind you why sugar, salt, snow, etc. are white. Rather surprisingly, many students and even more accomplished physicists are unaware of the answer. Starting with an analogy, the random walk of a drunk near a cliff-top, even if initially stepping away from the cliff face, inevitably ends up at the bottom of the cliff! In a completely analogous way, a photon (or a ray, if you prefer) entering a substance consisting of small transparent crystals gets randomly refracted (scattered) and ends up coming back to the surface, whence it emerges in some random direction, never to return to the bulk. This is a form of diffuse reflection, which – if the crystallites are totally non-absorbing – corresponds to 100% reflection. Even better than the best metallic mirrors! So, a white surface is a better reflector than a metallic surface. Even with a certain amount of absorption caused by impurities, this gives rise to strong diffuse reflection, hence the utility of white paint. The mean depth to which the photon (or ray) penetrates depends on the scattering power of the crystallites. This, in cartoon by melbourne artist Leunig

Two of the most substantial pillars underpinning modern Physics, namely Electrodynamics and Quantum Mechanics, have co-existed in a mutually compatible state for many decades. Nevertheless, from time to time new ideas are put forward to challenge the happy unity and to put the theory to test in new situations.