Volatility forecasts, trading volume, and the ARCH versus option-implied volatility trade-off

Market expectations of future return volatility play a crucial role in finance; so too does our understanding of the process by which information is incorporated in security prices through the trading process. The authors seek to learn something about both of these issues by investigating empirically the role of trading volume in predicting the relative informativeness of volatility forecasts produced by ARCH models versus the volatility forecasts derived from option prices and in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. The authors find that if trading volume was low during period $t – 1$ relative to the recent past, then ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period $t – 1$ relative to the recent past, then option-implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, their findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option-implied forward-looking estimate.

Stare down the barrel and center the crosshairs: Targeting the ex ante equity premium

The equity premium of interest in theoretical models is the extra return investors anticipate when purchasing risky stock instead of risk-free debt. Unfortunately, we do not observe this ex ante premium in the data; we only observe the returns that investors actually receive ex post, after they purchase the stock and hold it over some period of time during which random economic shocks affect prices. Over the past century U.S. stocks have returned roughly 6 percent more than risk-free debt, which is higher than warranted by standard economic theory; hence the "equity premium puzzle." In this paper we devise a method to simulate the distribution from which ex post equity premia are drawn, conditional on various assumptions about investors' ex ante equity premium. Comparing statistics that arise from our simulations with key financial characteristics of the U.S. economy, including dividend yields, Sharpe ratios, and interest rates, suggests a much narrower range of plausible equity premia than has been supported to date. Our results imply that the true ex ante equity premium likely lies very close to 4 percent.Bonds ; Investments ; Stock market ; Rate of return

Handling protest responses in contingent valuation surveys

OBJECTIVES: Protest responses, whereby respondents refuse to state the value they place on the health gain, are commonly encountered in contingent valuation (CV) studies, and they tend to be excluded from analyses. Such an approach will be biased if protesters differ from non-protesters on characteristics that predict their responses. The Heckman selection model has been commonly used to adjust for protesters, but its underlying assumptions may be implausible in this context. We present a multiple imputation (MI) approach to appropriately address protest responses in CV studies, and compare it with the Heckman selection model. METHODS: This study exploits data from the multinational EuroVaQ study, which surveyed respondents' willingness-to-pay (WTP) for a Quality Adjusted Life Year (QALY). Here, our simulation study assesses the relative performance of MI and Heckman selection models across different realistic settings grounded in the EuroVaQ study, including scenarios with different proportions of missing data and non-response mechanisms. We then illustrate the methods in the EuroVaQ study for estimating mean WTP for a QALY gain. RESULTS: We find that MI provides lower bias and mean squared error compared with the Heckman approach across all considered scenarios. The simulations suggest that the Heckman approach can lead to considerable underestimation or overestimation of mean WTP due to violations in the normality assumption, even after log-transforming the WTP responses. The case study illustrates that protesters are associated with a lower mean WTP for a QALY gain compared with non-protesters, but that the results differ according to method for handling protesters. CONCLUSIONS: MI is an appropriate method for addressing protest responses in CV studies

Volatility forecasts, trading volume, and the ARCH versus option-implied volatility trade-off

Market expectations of future return volatility play a crucial role in finance; so too does our understanding of the process by which information is incorporated in security prices through the trading process. The authors seek to learn something about both of these issues by investigating empirically the role of trading volume in predicting the relative informativeness of volatility forecasts produced by ARCH models versus the volatility forecasts derived from option prices and in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. The authors find that if trading volume was low during period $t – 1$ relative to the recent past, then ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period $t – 1$ relative to the recent past, then option-implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, their findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option-implied forward-looking estimate

Stare down the barrel and center the crosshairs: Targeting the ex ante equity premium

The equity premium of interest in theoretical models is the extra return investors anticipate when purchasing risky stock instead of risk-free debt. Unfortunately, we do not observe this ex ante premium in the data; we only observe the returns that investors actually receive ex post, after they purchase the stock and hold it over some period of time during which random economic shocks affect prices. Over the past century U.S. stocks have returned roughly 6 percent more than risk-free debt, which is higher than warranted by standard economic theory; hence the "equity premium puzzle." In this paper we devise a method to simulate the distribution from which ex post equity premia are drawn, conditional on various assumptions about investors' ex ante equity premium. Comparing statistics that arise from our simulations with key financial characteristics of the U.S. economy, including dividend yields, Sharpe ratios, and interest rates, suggests a much narrower range of plausible equity premia than has been supported to date. Our results imply that the true ex ante equity premium likely lies very close to 4 percent

FEDERAL RESERVE BANK of ATLANTA Volatility Forecasts, Trading Volume, and the ARCH versus Option-Implied Volatility Trade-off

Abstract: Market expectations of future return volatility play a crucial role in finance; so too does our understanding of the process by which information is incorporated in security prices through the trading process. The authors seek to learn something about both of these issues by investigating empirically the role of trading volume in predicting the relative informativeness of volatility forecasts produced by ARCH models versus the volatility forecasts derived from option prices and in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. The authors find that if trading volume was low during period $t -1$ relative to the recent past, then ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period $t -1$ relative to the recent past, then option-implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, their findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option-implied forward-looking estimate. JEL classification: G

Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code

We present a method for systematically evaluating the correctness and robustness of instruction-tuned large language models (LLMs) for code generation via a new benchmark, Turbulence. Turbulence consists of a large set of natural language $\textit{question templates}$, each of which is a programming problem, parameterised so that it can be asked in many different forms. Each question template has an associated $\textit{test oracle}$ that judges whether a code solution returned by an LLM is correct. Thus, from a single question template, it is possible to ask an LLM a $\textit{neighbourhood}$ of very similar programming questions, and assess the correctness of the result returned for each question. This allows gaps in an LLM's code generation abilities to be identified, including $\textit{anomalies}$ where the LLM correctly solves $\textit{almost all}$ questions in a neighbourhood but fails for particular parameter instantiations. We present experiments against five LLMs from OpenAI, Cohere and Meta, each at two temperature configurations. Our findings show that, across the board, Turbulence is able to reveal gaps in LLM reasoning ability. This goes beyond merely highlighting that LLMs sometimes produce wrong code (which is no surprise): by systematically identifying cases where LLMs are able to solve some problems in a neighbourhood but do not manage to generalise to solve the whole neighbourhood, our method is effective at highlighting $\textit{robustness}$ issues. We present data and examples that shed light on the kinds of mistakes that LLMs make when they return incorrect code results.Comment: Modified a typo in the conclusion section regarding the impact of temperature reduction on the diversity of error

Remember the source: Dissociating frontal and parietal contributions to episodic memory

Event-related fMRI studies reveal that episodic memory retrieval modulates lateral and medial parietal cortices, dorsal middle frontal gyrus (MFG), and anterior pFC. These regions respond more for recognized old than correctly rejected new words, suggesting a neural correlate of retrieval success. Despite significant efforts examining retrieval success regions, their role in retrieval remains largely unknown. Here we asked the question, to what degree are the regions performing memory-specific operations? And if so, are they all equally sensitive to successful retrieval, or are other factors such as error detection also implicated? We investigated this question by testing whether activity in retrieval success regions was associated with task-specific contingencies (i.e., perceived targetness) or mnemonic relevance (e.g., retrieval of source context). To do this, we used a source memory task that required discrimination between remembered targets and remembered nontargets. For a given region, the modulation of neural activity by a situational factor such as target status would suggest a more domain-general role; similarly, modulations of activity linked to error detection would suggest a role inmonitoring and control rather than the accumulation of evidence from memory per se. We found that parietal retrieval success regions exhibited greater activity for items receiving correct than incorrect source responses, whereas frontal retrieval success regions were most active on error trials, suggesting that posterior regions signal successful retrieval whereas frontal regions monitor retrieval outcome. In addition, perceived targetness failed to modulate fMRI activity in any retrieval success region, suggesting that these regions are retrieval specific. We discuss the different functions that these regions may support and propose an accumulator model that captures the different pattern of responses seen in frontal and parietal retrieval success regions