Till Örebro universitet

In this paper, we evaluate the directional interest-rate forecasts of UK households from the Bank of England’s Inflation Attitudes Survey. Employing a test for directional forecast accuracy and data on the survey balance ranging from 1999Q4 to 2023Q2, we find that the balance is not able to predict in which direction the interest rate will move over the coming year. In addition, regression models based on the balance are not able to generate forecasts for the quantitative change in the interest rate over the coming twelve months that have higher precision than a naïve forecast of no change. In order to provide information as to whether our findings are due to the inherent difficulty when it comes to forecasting interest rates or if households are not very insightful regarding interest rates, we investigate – again using data on the survey balance and testing for directional accuracy – whether households have been able to correctly assess the directional change of the interest rate over the previous twelve months; our results indicate some amount of “literacy” among the households regarding the interest rates that they face. Finally, analyses based on individual-response level data suggest that literacy regarding interest rates – proxied by the respondent having been correct regarding the directional change over the previous twelve months – does not appear helpful when forecasting.

This research is devoted to studying a geometric Brownian motion with drift switching driven by a 2 x 2 Markov chain. A discrete-time multiplicative approximation scheme was developed, and its convergence in Skorokhod topology to the continuous-time geometric Brownian motion with switching has been proved. Furthermore, in a financial market where the discounted asset price follows a geometric Brownian motion with drift switching, market incompleteness was established, and multiple equivalent martingale measures were constructed.

We analyse how financial market analysts' expectations in the Czech National Bank's Financial Market Inflation Expectations survey perform relative to the random-walk forecast when it comes to predicting five financial variables. Using data from 2001 to 2022, our results indicate that the analysts are able to significantly outperform the random-walk forecast in terms of forecast precision for the repo rate and Prague Interbank Offered Rate at the one-month forecasting horizon. For the five- and ten-year interest rate swap rates, the random walk significantly outperforms the analysts at both the one-month and one-year forecasting horizons. For the CZK/EUR exchange rate, the random-walk forecast has a lower root mean squared forecast error than that of the analysts' forecast at the one-month horizon whereas at the one-year horizon the opposite is found; however, none of these differences are statistically significant.

We analyse micro-level data concerning four financial variables in Sveriges Riksbank's Prospera Survey to evaluate the precision of forecasts provided by professionals active in the Swedish fixed -income market. Our results indicate that for the SEK/EUR and SEK/USD exchange rates, and the five-year government bond yield, none of the market participants that frequently participate in the survey manage to significantly outperform the random-walk forecast. For the central bank's policy rate, the market participants typically have a statistically significant higher forecast pre-cision than the random-walk forecast at the three-month horizon; however, at the two-and five-year horizons, the random-walk forecast typically outperforms the market participants.

We suggest a maximum likelihood estimation method for the popular Hull–White interest rate model. Our method uses a time series of yield curves to estimate model parameters under both risk-neutral and real-world measures. The suggested approach thus offers a solution to two possible drawbacks of calibration to prices of vanilla interest rate derivatives, the current standard for identification of time-inhomogeneous interest rate models. First, our method allows for derivatives pricing on illiquid markets where prices of vanilla products, which the model is calibrated to, are not available. Second, as we identify the real-world measure, we facilitate the use of the Hull–White model for forecasting and hence risk and portfolio management. The main idea of our approach is to maximise the likelihood of yields in periods subsequent to the time at which the model’s time-dependent parameter is fitted to a market forward rate curve. The empirical part of the paper implements the suggested estimation approach on EUR interest rate data. We investigate in-sample and out-of-sample performance of the estimated model, and compare estimation with calibration to swaption prices.

We study the mean-variance hedging of an American-type contingent claim that is exercised at a random time in a Markovian setting. This problem is motivated by applications in the areas of employee stock option valuation, credit risk, or equity-linked life insurance policies with an underlying risky asset value guarantee. Our analysis is based on dynamic programming and uses PDE techniques. In particular, we prove that the complete solution to the problem can be expressed in terms of the solution to a system of one quasi-linear parabolic PDE and two linear parabolic PDEs. Using a suitable iterative scheme involving linear parabolic PDEs and Schauder's interior estimates for parabolic PDEs, we show that each of these PDEs has a classical C-1,C- 2 solution. Using these results, we express the claim's mean-variance hedging value that we derive as its expected discounted payoff with respect to an equivalent martingale measure that does not coincide with the minimal martingale measure, which, in the context that we consider, identifies with the minimum entropy martingale measure as well as the variance-optimal martingale measure. Furthermore, we present a numerical study that illustrates aspects of our theoretical results.

I denna artikel analyseras huruvida två serier som beskriver hushållens bostadsprisförväntningar – SEB:s Boprisindikator och Erik Olssons Bostadsindex – kan prognostisera riktningen som de svenska bostadspriserna tar det kommande året. Ett formellt test för riktningsprognoser utförs. Våra resultat indikerar att ingen av förväntnings-serierna är framgångsrik när det gäller att prediktera den faktiska förändringen i bostadspriserna.

Survey data of households’ expectations of macroeconomic variables might provide useful information to those who analyse or forecast the economy. In this article, we evaluate whether households can predict in which direction inflation and the unemployment rate will move over the coming year. The analysis is conducted using monthly Swedish data from the National Institute of Economic Research’s Economic Tendency Survey over the period from January 1996 until August 2019. Our results indicate that households can forecast in what direction the unemployment rate is headed, but they fail to predict the direction of future inflation.

In this paper, we evaluate the forecasting precision of survey expectations of the four financial variables in the Prospera survey commissioned by Sveriges Riksbank – one of Sweden’s most important economic surveys. Our analysis shows that the market participants in the survey are able to significantly outperform the random walk for only one horizon and variable, namely the three-month horizon for the repo rate. At the longest horizon for the repo rate, and at all horizons for the five-year government bond yield, the random walk significantly outperforms the market participants. For the exchange-rate data studied – SEK/USD and SEK/EUR – no significant differences in forecasting precision in favour of the survey expectations can be established. We conclude that while the Prospera survey might be informative regarding the market participants’ expectations, it does not seem to carry much information about the actual future developments of the exchange rates and interest rates covered by the survey.

We analyze the optimal exercise of an American call executive stock option (ESO) written on a stock whose drift parameter falls to a lower value at a change point, an exponentially distributed random time independent of the Brownian motion driving the stock. Two agents, who do not trade the stock, have differing information on the change point and seek to optimally exercise the option by maximizing its discounted payoff under the physical measure. The first agent has full information and observes the change point. The second agent has partial information and filters the change point from price observations. This scenario is designed to mimic the positions of two employees of varying seniority, a fully informed executive and a partially informed less senior employee, each of whom receives an ESO. The partial information scenario yields a model under the observation filtration (F) over cap in which the stock drift becomes a diffusion driven by the innovations process, an (F) over cap Brownian motion also driving the stock under (F) over cap, and the partial information optimal stopping value function has two spatial dimensions. We rigorously characterize the free boundary PDEs for both agents, establish shape and regularity properties of the associated optimal exercise boundaries, and prove the smooth pasting property in both information scenarios, exploiting some stochastic flow ideas to do so in the partial information case. We develop finite difference algorithms to numerically solve both agents' exercise and valuation problems and illustrate that the additional information of the fully informed agent can result in exercise patterns which exploit the information on the change point, lending credence to empirical studies which suggest that privileged information of bad news is a factor leading to early exercise of ESOs prior to poor stock price performance.